Journal Publications

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Nitul Dutta, Sudeep Tanwar, Shobhit K. Patel & Gheorghita Ghinea (2021) SVM-based Analysis for Predicting Success Rate of Interest Packets in Information Centric Networks, Applied Artificial Intelligence, DOI: 10.1080/08839514.2021.2020488

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@article{doi:10.1080/08839514.2021.2020488,
author = {Nitul Dutta and Sudeep Tanwar and Shobhit K. Patel and Gheorghita Ghinea},
title = {SVM-based Analysis for Predicting Success Rate of Interest Packets in Information Centric Networks},
journal = {Applied Artificial Intelligence},
volume = {0},
number = {0},
pages = {1-22},
year  = {2021},
publisher = {Taylor & Francis},
doi = {10.1080/08839514.2021.2020488},

URL = { 
        https://doi.org/10.1080/08839514.2021.2020488
    
},
eprint = { 
        https://doi.org/10.1080/08839514.2021.2020488
    
}

}

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Gupta, S.K., Kumar, S., Tyagi, S. et al. SSEER: Segmented sectors in energy efficient routing for wireless sensor network. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-021-11829-5

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Gupta, S.K., Kumar, S., Tyagi, S. et al. SSEER: Segmented sectors in energy efficient routing for wireless sensor network. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-021-11829-5

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Kumar, J.S., Gupta, A., Tanwar, S. et al. Security enhancement in cellular networks employing D2D friendly jammer for V2V communication. Cluster Comput (2022). https://doi.org/10.1007/s10586-022-03551-0

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Kumar, J.S., Gupta, A., Tanwar, S. et al. Security enhancement in cellular networks employing D2D friendly jammer for V2V communication. Cluster Comput (2022). https://doi.org/10.1007/s10586-022-03551-0

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U. Singh et al., "Coalition Games for Performance Evaluation in 5G and Beyond Networks: A Survey," in IEEE Access, doi: 10.1109/ACCESS.2022.3146158.

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@ARTICLE{9691369,
  author={Singh, Upendra and Ramaswamy, Aditya and Dua, Amit and Kumar, Neeraj and Tanwar, Sudeep and Sharma, Gulshan and Davidson, Innocent E. and Sharma, Ravi},
  journal={IEEE Access}, 
  title={Coalition Games for Performance Evaluation in 5G and Beyond Networks: A Survey}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3146158}}

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Shah, K., Chadotra, S., Tanwar, S. et al. Blockchain for IoV in 6G environment: review solutions and challenges. Cluster Comput (2022). https://doi.org/10.1007/s10586-021-03492-0

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Shah, K., Chadotra, S., Tanwar, S. et al. Blockchain for IoV in 6G environment: review solutions and challenges. Cluster Comput (2022). https://doi.org/10.1007/s10586-021-03492-0

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Hathaliya, JJ, Tanwar, S, Sharma, P. Adversarial learning techniques for security and privacy preservation: A comprehensive review. Security and Privacy. 2022;e209. doi:10.1002/spy2.209

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@article{https://doi.org/10.1002/spy2.209,
author = {Hathaliya, Jigna J. and Tanwar, Sudeep and Sharma, Priyanka},
title = {Adversarial learning techniques for security and privacy preservation: A comprehensive review},
journal = {SECURITY AND PRIVACY},
volume = {n/a},
number = {n/a},
pages = {e209},
keywords = {adversarial attacks, adversarial defenses, adversarial learning, block chain, deep learning, federated learning, machine learning, privacy, reinforcement learning, security},
doi = {https://doi.org/10.1002/spy2.209},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/spy2.209},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/spy2.209},
abstract = {Abstract In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real-time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML- and DL-based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high-graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.}
}

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R. Gupta, P. Bhattacharya, S. Tanwar, N. Kumar and S. Zeadally, "GaRuDa: A Blockchain-Based Delivery Scheme Using Drones for Healthcare 5.0 Applications," in IEEE Internet of Things Magazine, vol. 4, no. 4, pp. 60-66, December 2021, doi: 10.1109/IOTM.001.2100045.

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@ARTICLE{9712387,
  author={Gupta, Rajesh and Bhattacharya, Pronaya and Tanwar, Sudeep and Kumar, Neeraj and Zeadally, Sherali},
  journal={IEEE Internet of Things Magazine}, 
  title={GaRuDa: A Blockchain-Based Delivery Scheme Using Drones for Healthcare 5.0 Applications}, 
  year={2021},
  volume={4},
  number={4},
  pages={60-66},
  doi={10.1109/IOTM.001.2100045}}

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Kumar Jadav, N, Gupta, R, Tanwar, S. A survey on energy-efficient resource allocation schemes in device-to-device communication. Int J Commun Syst. 2022;e5112. doi:10.1002/dac.5112

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@article{https://doi.org/10.1002/dac.5112,
author = {Kumar Jadav, Nilesh and Gupta, Rajesh and Tanwar, Sudeep},
title = {A survey on energy-efficient resource allocation schemes in device-to-device communication},
journal = {International Journal of Communication Systems},
volume = {n/a},
number = {n/a},
pages = {e5112},
keywords = {channel allocation, D2D communication, energy efficiency, power allocation, resource allocation, spectrum allocation},
doi = {https://doi.org/10.1002/dac.5112},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.5112},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.5112},
abstract = {Summary Device-to-device (D2D) communication is an emanating and robust technology for next-generation wireless networks. It aims to minimize load from the centralized base station (BS) and lets the device communicate directly in the presence or absence of it. Further, to increase the spectral efficiency, it operates in an underlay mode, where D2D devices/users reuse the spectrum of cellular users (CUs), resulting in unusual intercell/intracell interferences. Managing resources efficiently can drastically reduce the effect of interference but on the cost of high energy consumption. The research community is diligently studying the optimal methods for energy-efficient resource allocation in D2D systems. As per the literature, there exists no comprehensive survey that studied the existing energy-efficient resource allocation schemes in the D2D environment. Motivated from this, we present a survey on recently adopted solutions for energy-efficient power, channel, and spectrum allocation in D2D communication, which includes artificial intelligence (AI), optimization, dynamic programming, heuristic approach, game theory, and graph models. In addition, we also highlighted the open research issues and technical challenges while allocating resources to D2D users (DUs).}
}

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R. K. Patel, A. Kumari, S. Tanwar, W. -C. Hong and R. Sharma, "AI-empowered Recommender System for Renewable Energy Harvesting in Smart Grid System," in IEEE Access, doi: 10.1109/ACCESS.2022.3152528.

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@ARTICLE{9716157,
  author={Patel, Rushil Kaushikkumar and Kumari, Aparna and Tanwar, Sudeep and Hong, Wei-Chiang and Sharma, Ravi},
  journal={IEEE Access}, 
  title={AI-empowered Recommender System for Renewable Energy Harvesting in Smart Grid System}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3152528}}

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Shah, I.; Doshi, C.; Patel, M.; Tanwar, S.; Hong, W.-C.; Sharma, R. A Comprehensive Review of the Technological Solutions to Analyse the Effects of Pandemic Outbreak on Human Lives. Medicina 2022, 58, 311. https://doi.org/10.3390/medicina58020311

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@Article{medicina58020311,
AUTHOR = {Shah, Ishwa and Doshi, Chelsy and Patel, Mohil and Tanwar, Sudeep and Hong, Wei-Chiang and Sharma, Ravi},
TITLE = {A Comprehensive Review of the Technological Solutions to Analyse the Effects of Pandemic Outbreak on Human Lives},
JOURNAL = {Medicina},
VOLUME = {58},
YEAR = {2022},
NUMBER = {2},
ARTICLE-NUMBER = {311},
URL = {https://www.mdpi.com/1648-9144/58/2/311},
ISSN = {1648-9144},
ABSTRACT = {A coronavirus outbreak caused by a novel virus known as SARS-CoV-2 originated towards the latter half of 2019. COVID-19’s abrupt emergence and unchecked global expansion highlight the inability of the current healthcare services to respond to public health emergencies promptly. This paper reviews the different aspects of human life comprehensively affected by COVID-19. It then discusses various tools and technologies from the leading domains and their integration into people’s lives to overcome issues resulting from pandemics. This paper further focuses on providing a detailed review of existing and probable Artificial Intelligence (AI), Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), and Blockchain-based solutions. The COVID-19 pandemic brings several challenges from the viewpoint of the nation’s healthcare, security, privacy, and economy. AI offers different predictive services and intelligent strategies for detecting coronavirus signs, promoting drug development, remote healthcare, classifying fake news detection, and security attacks. The incorporation of AI in the COVID-19 outbreak brings robust and reliable solutions to enhance the healthcare systems, increases user’s life expectancy, and boosts the nation’s economy. Furthermore, AR/VR helps in distance learning, factory automation, and setting up an environment of work from home. Blockchain helps in protecting consumer’s privacy, and securing the medical supply chain operations. IoT is helpful in remote patient monitoring, distant sanitising via drones, managing social distancing (using IoT cameras), and many more in combating the pandemic. This study covers an up-to-date analysis on the use of blockchain technology, AI, AR/VR, and IoT for combating COVID-19 pandemic considering various applications. These technologies provide new emerging initiatives and use cases to deal with the COVID-19 pandemic. Finally, we discuss challenges and potential research paths that will promote further research into future pandemic outbreaks.},
DOI = {10.3390/medicina58020311}
}

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Akhil Gupta, Alamuru Venkata Harsha Vardhan, Sudeep Tanwar, Neeraj Kumar, Anupam Singh, Performance Analysis at Different Millimetre Wave Frequencies for Indoor Shopping Complex and Outdoor UAV Applications towards 5G, Microprocessors and Microsystems, 2022, 104506, ISSN 0141-9331, https://doi.org/10.1016/j.micpro.2022.104506.

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@article{GUPTA2022104506,
title = {Performance Analysis at Different Millimetre Wave Frequencies for Indoor Shopping Complex and Outdoor UAV Applications towards 5G},
journal = {Microprocessors and Microsystems},
pages = {104506},
year = {2022},
issn = {0141-9331},
doi = {https://doi.org/10.1016/j.micpro.2022.104506},
url = {https://www.sciencedirect.com/science/article/pii/S0141933122000667},
author = {Akhil Gupta and Alamuru Venkata Harsha Vardhan and Sudeep Tanwar and Neeraj Kumar and Anupam Singh},
keywords = {mmWave, NYUSIM, Human Blockage, Spatial Consistency, Outdoor to Indoor Loss, Building Penetration},
abstract = {This paper provides descriptions and implementations of NYUSIM, a novel framework for simulation of networks and applications for the three essential characteristics of channel design, spatial consistency, human blockage, and outdoor-to-indoor penetration. This paper also introduces NYUSIM 2.0, an enhanced channel simulator that can simulate spatially consistent channel parameters according to the NYUSIM 2.0 drop-dependent simulator. The 5G Network Analysis special interest community introduced a parabolic model for outdoor-indoor penetration failure to model the excess route failure due to penetration through buildings and enforced in NYUSIM 2.0. It is a powerful measuring-based channel emulator for the fifth generation and beyond the configuration and measurement of the mm-Wave communication network.}
}

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D. Saraswat et al., "Blockchain-based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions," in IEEE Access, doi: 10.1109/ACCESS.2022.3161132.

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@ARTICLE{9739009,
  author={Saraswat, Deepti and Verma, Ashwin and Bhattacharya, Pronaya and Tanwar, Sudeep and Sharma, Gulshan and Bokoro, Pitshou N and Sharma, Ravi},
  journal={IEEE Access}, 
  title={Blockchain-based Federated Learning in UAVs Beyond 5G Networks: A Solution Taxonomy and Future Directions}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3161132}}

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D. Rohera et al., "A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects," in IEEE Access, doi: 10.1109/ACCESS.2022.3159651.

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@ARTICLE{9734026,
  author={Rohera, Dhiren and Shethna, Harshal and Patel, Keyur and Thakker, Urvish and Tanwar, Sudeep and Gupta, Rajesh and Hong, Wei-Chiang and Sharma, Ravi},
  journal={IEEE Access}, 
  title={A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.31596

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R. Parekh et al., "DL-GuesS: Deep Learning and Sentiment Analysis-based Cryptocurrency Price Prediction," in IEEE Access, doi: 10.1109/ACCESS.2022.3163305.

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@ARTICLE{9745117,
  author={Parekh, Raj and Patel, Nisarg P. and Thakkar, Nihar and Gupta, Rajesh and Tanwar, Sudeep and Sharma, Gulshan and DAVIDSON, Innocent E. and Sharma, Ravi},
  journal={IEEE Access}, 
  title={DL-GuesS: Deep Learning and Sentiment Analysis-based Cryptocurrency Price Prediction}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3163305}}

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N. P. Patel et al., "Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions," in IEEE Access, doi: 10.1109/ACCESS.2022.3163023.

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@ARTICLE{9743931,
  author={Patel, Nisarg P. and Parekh, Raj and Thakkar, Nihar and Gupta, Rajesh and Tanwar, Sudeep and Sharma, Gulshan and Davidson, Innocent E. and Sharma, Ravi},
  journal={IEEE Access}, 
  title={Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3163023}}

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Jigna J. Hathaliya, Hetav Modi, Rajesh Gupta, Sudeep Tanwar, Priyanka Sharma, Ravi Sharma, Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity, Computers and Electrical Engineering, Volume 101, 2022, 107946.

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@article{HATHALIYA2022107946,
title = {Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity},
journal = {Computers and Electrical Engineering},
volume = {101},
pages = {107946},
year = {2022},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2022.107946},
url = {https://www.sciencedirect.com/science/article/pii/S0045790622002245},
author = {Jigna J. Hathaliya and Hetav Modi and Rajesh Gupta and Sudeep Tanwar and Priyanka Sharma and Ravi Sharma},
keywords = {Parkinson’s disease, Essential tremor, Parkinson tremor, Tremor severity, Convolutional neural network},
abstract = {Parkinson’s disease (PSD) and essential tremor (ET) are oscillatory and rhythmic movements in the human body with similar characteristics and becomes challenging to identify it accurately. Thus, the chances of misdiagnosis are high. Researchers employed machine learning (ML) algorithms to accurately classify ET and PSD patients. This requires manual feature extraction that, without knowing their importance in prediction purposes, can be mitigated with automated feature engineering using deep learning (DL). So, in this paper, we propose a convolutional neural network (CNN)-based classification model with seven hidden layers and different filter sizes for the accurate classification of PSD and healthy control (HC) subjects. A flatten layer converts three-dimensional data to one-dimensional Tensor flow. Finally, the dense layer outputs the classification of PSD and HC patients based on tremor intensity to identify the PSD patient’s risk at an early stage. It outperforms the traditional models with 92.4% accuracy of tremor classification.}
}

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Riya Kakkar, Rajesh Gupta, Smita Agrawal, Sudeep Tanwar, Ravi Sharma, Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G, Journal of Information Security and Applications, Volume 67, 2022, 103179, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2022.103179.

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@article{KAKKAR2022103179,
title = {Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G},
journal = {Journal of Information Security and Applications},
volume = {67},
pages = {103179},
year = {2022},
issn = {2214-2126},
doi = {https://doi.org/10.1016/j.jisa.2022.103179},
url = {https://www.sciencedirect.com/science/article/pii/S2214212622000618},
author = {Riya Kakkar and Rajesh Gupta and Smita Agrawal and Sudeep Tanwar and Ravi Sharma},
keywords = {Blockchain, Random forest, Latency, Scalability, Edge intelligence, 5G},
abstract = {This paper proposes a blockchain-based secure and reliable data sharing scheme for autonomous vehicles (AVs). It aims to secure data sharing among AVs. We integrate the fifth-generation (5G) communication network with the proposed blockchain-based scheme to enable ultra-low latency, high reliability, and availability. We have incorporated the edge intelligence nodes (EINs) with blockchain for efficient data processing. We have used a random forest classifier model (RFCM) on the road accidents dataset to predict the accident severity, i.e., fatal, serious, and slight. It is based on road type, light condition, and vehicle movement to be shared among other AVs. We have considered various scenarios and performed the predictive analysis that needs to be secured by using blockchain technology. Results of the study demonstrate the accuracy of RFCM as 93%, the precision of 98%, 97%, and 87% for fatal, serious, and slight categories of accident severity, respectively. The results show that the proposed scheme with RFCM yields better accuracy of 93% than other existing classification algorithms such as Decision tree, Naive Bayes, and AdaBoost. The blockchain-based proposed system has been compared with the traditional approach based on parameters such as network latency, scalability, computation time, delay comparison for node validation, data storage latency, and data storage cost. Obtained results show that the proposed scheme enables the data sharing reliable, secure, and efficient for AVs and outperforms the blockchain and non-blockchain systems in terms of security and efficiency.}
}

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Kalariya V, Parmar P, Jay P, Tanwar S, Raboaca MS, Alqahtani F, Tolba A, Neagu B-C. Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market. Mathematics. 2022; 10(9):1456. https://doi.org/10.3390/math10091456

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@Article{math10091456,
AUTHOR = {Kalariya, Vasu and Parmar, Pushpendra and Jay, Patel and Tanwar, Sudeep and Raboaca, Maria Simona and Alqahtani, Fayez and Tolba, Amr and Neagu, Bogdan-Constantin},
TITLE = {Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {9},
ARTICLE-NUMBER = {1456},
URL = {https://www.mdpi.com/2227-7390/10/9/1456},
ISSN = {2227-7390},
ABSTRACT = {Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.},
DOI = {10.3390/math10091456}
}

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Pronaya Bhattacharya, Arpit Shukla, Sudeep Tanwar, Neeraj Kumar, Ravi Sharma, 6Blocks: 6G-enabled trust management scheme for decentralised autonomous vehicles, Computer Communications, 2022,

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@article{BHATTACHARYA2022,
title = {6Blocks: 6G-enabled trust management scheme for decentralised autonomous vehicles},
journal = {Computer Communications},
year = {2022},
issn = {0140-3664},
doi = {https://doi.org/10.1016/j.comcom.2022.04.024},
url = {https://www.sciencedirect.com/science/article/pii/S0140366422001360},
author = {Pronaya Bhattacharya and Arpit Shukla and Sudeep Tanwar and Neeraj Kumar and Ravi Sharma},
keywords = {6G, Connected Autonomous Smart Vehicles, Vehicle-to-anything, Blockchain, Software-defined networking, Smart contracts},
abstract = {In this paper, we propose a 6G-envisioned blockchain (BC)-based scheme for cellular vehicle-to-everything (C-V2X) ecosystems (termed as 6Blocks), which optimizes the edge-resource provisioning of 5G C-V2X nodes via network function virtualization (NFV)-enabled infrastructure. 6Blocks operates in three phases. In the first phase, a secure data aggregation through 6G sensors is performed at the data plane. Then, in the second phase, the aggregated data is sent to the 6G-enabled NFV control plane to leverage resource allocation to connected autonomous smart vehicles (CASVs) through edge nodes via service container operations. In the third phase, management plane operations are designed that includes registration and auction smart contracts (SC) for connected autonomous vehicle owner (CAVO) and CASVs over the BC with public/private pairs fetched from interplanetary file systems (IPFS). The proposed scheme is compared against existing NFV schemes like distant, edge, and SDN-based edge control. At 20 CASVs, 6Blocks achieves an edge-service latency of 0.05ms against 0.76ms, 0.27, and 0.15ms for cloud, edge, and SDN-edge schemes, respectively, with high connection density of 107 nodes, at maximum utilized IPFS bandwidth of 0.2 Mbps. Then, we validate the trust of our model through the automated validation of the internet security protocol and applications (AVISPA) tool and modeled the vehicle and sensor nodes.The obtained results show the efficacy of the proposed scheme against existing state-of-the-art approaches with respect to parameters such as network, security, transaction costs, and auction.}
}

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Aparna Kumari, Mihir Trivedi, Sudeep Tanwar, Gulshan Sharma, Ravi Sharma, "SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning", International Transactions on Electrical Energy Systems, vol. 2022, Article ID 9761157, 11 pages, 2022. https://doi.org/10.1155/2022/9761157

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Aparna Kumari, Mihir Trivedi, Sudeep Tanwar, Gulshan Sharma, Ravi Sharma, "SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning", International Transactions on Electrical Energy Systems, vol. 2022, Article ID 9761157, 11 pages, 2022. https://doi.org/10.1155/2022/9761157

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Sudeep Tanwar, Umesh Bodkhe, Mohammad Dahman Alshehri, Rajesh Gupta, Ravi Sharma, Blockchain-assisted industrial automation beyond 5G networks, Computers & Industrial Engineering, Volume 169, 2022, 108209, ISSN 0360-8352, https://doi.org/10.1016/j.cie.2022.108209.

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@article{TANWAR2022108209,
title = {Blockchain-assisted industrial automation beyond 5G networks},
journal = {Computers & Industrial Engineering},
volume = {169},
pages = {108209},
year = {2022},
issn = {0360-8352},
doi = {https://doi.org/10.1016/j.cie.2022.108209},
url = {https://www.sciencedirect.com/science/article/pii/S0360835222002790},
author = {Sudeep Tanwar and Umesh Bodkhe and Mohammad Dahman Alshehri and Rajesh Gupta and Ravi Sharma},
keywords = {6G, Additive manufacturing, Blockchain technology, Industrial automation, Security},
abstract = {Automation of industrial tasks becomes the necessity of organizations due to high risks and low operational efficiency associated with traditional procedures. The industrial Internet-of-thing (IIoT) has proved it a reality with ubiquitous computing, which interconnects tens of billions of objects, such as devices and machines, for real-time data transfer. However, the rapid increase in the number of connected devices and machines leads to various security issues, such as data modification, sniffing, and many more. Achieving secure and reliable communication between devices becomes challenging as the entire communication takes place over an open channel, i.e., the Internet. Most of the security solutions presented in the literature are centralized and susceptible to security, latency, reliability, and single-point-of-failure issues. Blockchain is a plausible solution to mitigate the aforementioned issues with reduced capital and operating expenditures, whereas the sixth-generation (6G) network makes communication faster and more reliable. Motivated by these facts, this paper presents an exhaustive survey on blockchain-based industrial automation in the 6G environment. Also, a blockchain and 6G-enabled industrial automation architecture for secure and efficient communication among IIoT devices is proposed. This paper also presents a case study on blockchain-based additive manufacturing for designing three-dimensional products to validate the proposed architecture. The performance of the proposed case study is evaluated based on network parameters such as delay, packet loss, scalability, network bandwidth, computation time, and data storage cost. Finally, various research challenges and future directions are suggested in this emerging area.}
}

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R. Kakkar et al., "PADaaV: Blockchain-based Parking Price Prediction Scheme for Sustainable Traffic Management," in IEEE Access, doi: 10.1109/ACCESS.2022.3173162.

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@ARTICLE{9770066,
  author={Kakkar, Riya and Alzubi, Jafar and Dua, Amit and Agrawal, Smita and Tanwar, Sudeep and Agrawal, Rajat and Sharma, Gulshan and Bokoro, Pitshou N and Sharma, Ravi},
  journal={IEEE Access}, 
  title={PADaaV: Blockchain-based Parking Price Prediction Scheme for Sustainable Traffic Management}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3173162}}

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Sudeep Tanwar,Halim Khujamatov,Bairam Turumbetov,Ernazar Reypnazarov and Zamira Allamuratova,"Designing and Calculating Bandwidth of the LTE Network for Rural Areas," International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 2, pp. 437-445, 2022. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.12.2.14950.

BibText

@article{IJASEIT14950,
   author = {Sudeep Tanwar and Halim Khujamatov and Bairam Turumbetov and Ernazar Reypnazarov and Zamira Allamuratova},
   title = {Designing and Calculating Bandwidth of the LTE Network for Rural Areas},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {12},
   number = {2},
   year = {2022},
   pages = {437--445},
   keywords = {Base station; bandwidth; equipment; LTE; mobile communication; network.},
   abstract = {The standard of broadband mobile communication LTE can be used to build a mobile communication network in remote populations and provide the population with modern mobile communication services, such as video calls, data transmission, high-speed mobile Internet, Internet TV, etc. In this work, a LTE network has been designed, and the network capacity has been calculated for a certain area. Muynak of the Republic of Karakalpakstan, located in the northwest of the Republic of Uzbekistan, was taken as the territory. The paper presents the characteristics of the selected equipment required for the construction of the mobile network, calculations of network bandwidth, number of potential and active network subscribers, the number of base stations, the average bandwidth of the projected network, the average traffic per subscriber in the busy hour, the radio frequency plan, the geographical location of the base stations, and the LTE network design scheme has been developed. In addition, the characteristics of the number of potential subscribers are described in tabular form, as well as graphs of the number of base stations, the average bandwidth of the designed network, the total traffic of the designed network depending on the number of potential subscribers. The LTE network frequency plan has been developed for the city of Muynak. The designed network will increase the number of subscribers to 12,000 without reducing bandwidth.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14950},
   doi = {10.18517/ijaseit.12.2.14950}
}

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Prajapati, K.; Ramakrishnan, R.; Bhavsar, M.; Mahajan, A.; Narmawala, Z.; Bhavsar, A.; Raboaca, M.S.; Tanwar, S. Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images. Mathematics 2022, 10, 1697. https://doi.org/10.3390/math10101697

BibText

@Article{math10101697,
AUTHOR = {Prajapati, Kinjal and Ramakrishnan, Ratheesh and Bhavsar, Madhuri and Mahajan, Alka and Narmawala, Zunnun and Bhavsar, Archana and Raboaca, Maria Simona and Tanwar, Sudeep},
TITLE = {Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {10},
ARTICLE-NUMBER = {1697},
URL = {https://www.mdpi.com/2227-7390/10/10/1697},
ISSN = {2227-7390},
ABSTRACT = {Oil spills on the ocean surface are a serious threat to the marine ecosystem. Automation of oil spill detection through full/dual polarimetric Synthetic Aperture Radar (SAR) images is considered a good aid for oil spill disaster management. This paper uses the power of log transformation to discern the scattering behavior more effectively from the coherency matrix (T3). The proposed coherency matrix is tested on patches of the clean sea surface and four different classes of oil spills, viz. heavy sedimented oil, thick oil, oil-water emulsion, fresh oil; by analyzing the entropy (H), anisotropy (A), and mean scattering angle alpha (α), following the H/A/α decomposition. Experimental results show that not only does the proposed T3 matrix differentiate between Bragg scattering of the clean sea surface from a random scattering of thick oil spills but is also able to distinguish between different emulsions of oil spills with water and sediments. Moreover, unlike classical T3, the proposed method distinguishes concrete-like structures and heavy sedimented oil even though both exhibit similar scattering behavior. The proposed algorithm is developed and validated on the data acquired by the UAVSAR full polarimetric L band SAR sensor over the Gulf of Mexico (GOM) region during the Deepwater Horizon (DWH) oil spill accident in June 2010.},
DOI = {10.3390/math10101697}
}

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Gupta,A., et al.: RoboticProcessAutomationuse casesin academiaand earlyimplementationexperiences. IET Soft.1–10(2022).https://doi.org/10.1049/sfw2.1206110-GUPTAET AL.

BibText

@article{https://doi.org/10.1049/sfw2.12061,
author = {Gupta, Ankur and Prabhat, Purnendu and Sawhney, Sahil and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj and Shabaz, Mohammad},
title = {Robotic Process Automation use cases in academia and early implementation experiences},
journal = {IET Software},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {datasets, gaze detection, neural networks, text tagging},
doi = {https://doi.org/10.1049/sfw2.12061},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/sfw2.12061},
eprint = {https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/sfw2.12061},
abstract = {Abstract Robotic Process Automation (RPA) has emerged as a new sub-domain in software-based automation. RPA-based software bots promise to alleviate the tedium in labour-intensive tasks involving manual and repetitive operations. Therefore, RPA finds wide adoption across industries and domains. In this paper, RPA use cases for academia and document early implementation experiences are examined. The authors believe that the ease of implementation of RPA bots and their potential benefits shall facilitate the deployment of RPA across institutions resulting in efficient data collation and analysis while helping drive operational efficiency. However, while RPA bot development encompasses low complexity, maintenance and long-term seamless operation require significant software engineering effort, which must be factored in by institutions looking to adopt RPA for automation.}
}

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Mankodiya, Harsh, Dhairya Jadav, Rajesh Gupta, Sudeep Tanwar, Wei-Chiang Hong, and Ravi Sharma. 2022. "OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles" Applied Sciences 12, no. 11: 5310. https://doi.org/10.3390/app12115310

BibText

@Article{app12115310,
AUTHOR = {Mankodiya, Harsh and Jadav, Dhairya and Gupta, Rajesh and Tanwar, Sudeep and Hong, Wei-Chiang and Sharma, Ravi},
TITLE = {OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles},
JOURNAL = {Applied Sciences},
VOLUME = {12},
YEAR = {2022},
NUMBER = {11},
ARTICLE-NUMBER = {5310},
URL = {https://www.mdpi.com/2076-3417/12/11/5310},
ISSN = {2076-3417},
ABSTRACT = {In recent years, artificial intelligence (AI) has become one of the most prominent fields in autonomous vehicles (AVs). With the help of AI, the stress levels of drivers have been reduced, as most of the work is executed by the AV itself. With the increasing complexity of models, explainable artificial intelligence (XAI) techniques work as handy tools that allow naive people and developers to understand the intricate workings of deep learning models. These techniques can be paralleled to AI to increase their interpretability. One essential task of AVs is to be able to follow the road. This paper attempts to justify how AVs can detect and segment the road on which they are moving using deep learning (DL) models. We trained and compared three semantic segmentation architectures for the task of pixel-wise road detection. Max IoU scores of 0.9459 and 0.9621 were obtained on the train and test set. Such DL algorithms are called “black box models” as they are hard to interpret due to their highly complex structures. Integrating XAI enables us to interpret and comprehend the predictions of these abstract models. We applied various XAI methods and generated explanations for the proposed segmentation model for road detection in AVs.},
DOI = {10.3390/app12115310}
}

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Tanwar, Sudeep, Aparna Kumari, Darshan Vekaria, Maria S. Raboaca, Fayez Alqahtani, Amr Tolba, Bogdan-Constantin Neagu, and Ravi Sharma. 2022. "GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection" Sensors 22, no. 11: 4048. https://doi.org/10.3390/s22114048

BibText

@Article{s22114048,
AUTHOR = {Tanwar, Sudeep and Kumari, Aparna and Vekaria, Darshan and Raboaca, Maria Simona and Alqahtani, Fayez and Tolba, Amr and Neagu, Bogdan-Constantin and Sharma, Ravi},
TITLE = {GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {11},
ARTICLE-NUMBER = {4048},
URL = {https://www.mdpi.com/1424-8220/22/11/4048},
ISSN = {1424-8220},
ABSTRACT = {Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as GrAb, which uses a data-driven analytics approach. GrAb uses a DL-based long short-term memory (LSTM) model to predict the energy consumption using smart meter data. Then, a threshold calculator is used to calculate the energy consumption. Both the predicted energy consumption and the threshold value are passed to the support vector machine (SVM)-based classifier to categorize the energy losses into technical, non-technical (energy theft), and normal consumption. The proposed data-driven theft detection scheme identifies various forms of energy theft (e.g., smart meter data manipulation or clandestine connections). Experimental results show that the proposed scheme (GrAb) identifies energy theft more accurately compared to the state-of-the-art approaches.},
DOI = {10.3390/s22114048}
}

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Gupta, R, Tanwar, S. SARAS: Secure resource allocation scheme for NOMA-based device-to-device communication using coalition game and cognitive radio. Int J Commun Syst. 2022;e5239. doi:10.1002/dac.5239

BibText

@article{https://doi.org/10.1002/dac.5239,
author = {Gupta, Rajesh and Tanwar, Sudeep},
title = {SARAS: Secure resource allocation scheme for NOMA-based device-to-device communication using coalition game and cognitive radio},
journal = {International Journal of Communication Systems},
volume = {n/a},
number = {n/a},
pages = {e5239},
keywords = {5G, cognitive radio, device-to-device communication, NOMA, resource allocation, secrecy capacity, security, sum rate},
doi = {https://doi.org/10.1002/dac.5239},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.5239},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.5239},
abstract = {Summary Device-to-device (D2D) is a fifth-generation (5G) network's key technology which allows devices in proximity to communicate one-to-one without a base station (BS). Moreover, it ameliorates channel gain, communication latency, energy efficiency, and spectral efficiency. However, mitigating interference (D2D and cellular mobile users [CMUs]) and eavesdropper effect (at D2D links) is a prime concern in D2D communication. These issues can be resolved with an efficient allocation of radio resources. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are viable solutions to resolve the aforementioned issues. Motivated by the aforementioned discussion, in this paper, we propose a joint CR and NOMA-based scheme, that is, SARAS for secure resource allocation in D2D communication. It aims to maximize the overall sum rate and secrecy capacity of D2D users with assured quality of service (QoS) and quality of experience (QoE). CR is used for pairing between strong and weak D2D users (i.e., D2D pair) and NOMA's successive interference cancelation (SIC) technique helps to reduce the interference effect between cellular and D2D users. We used a coalition game for efficient resource utilization, which transfers D2D users from one coalition to another based on the preference order and channel conditions. This landed D2D users in the best coalition having favorable channel conditions. We have executed the proposed SARAS scheme over the MATLAB simulation tool, considering a varying number of D2D and CMUs. Simulation results show that the proposed scheme outperforms the traditional OFDMA, random, and nearest approaches concerning parameters such as sum rate and secrecy capacity.}
}

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Pronaya Bhattacharya, Farnazbanu Patel, Sudeep Tanwar, Neeraj Kumar, Ravi Sharma, MB-MaaS: Mobile Blockchain-based Mining-as-a-Service for IIoT environments, Journal of Parallel and Distributed Computing, 2022, , ISSN 0743-7315, https://doi.org/10.1016/j.jpdc.2022.05.008. (https://www.sciencedirect.com/science/article/pii/S0743731522001228) Abstract: In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches. Keywords: Mobile Blockchain; Mobile Edge computing; 5G; Mining-as-a-Service; Data Offloading

BibText

@article{BHATTACHARYA2022,
title = {MB-MaaS: Mobile Blockchain-based Mining-as-a-Service for IIoT environments},
journal = {Journal of Parallel and Distributed Computing},
year = {2022},
issn = {0743-7315},
doi = {https://doi.org/10.1016/j.jpdc.2022.05.008},
url = {https://www.sciencedirect.com/science/article/pii/S0743731522001228},
author = {Pronaya Bhattacharya and Farnazbanu Patel and Sudeep Tanwar and Neeraj Kumar and Ravi Sharma},
keywords = {Mobile Blockchain, Mobile Edge computing, 5G, Mining-as-a-Service, Data Offloading},
abstract = {In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches.}
}

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Rathod T, Jadav NK, Alshehri MD, Tanwar S, Sharma R, Felseghi R-A, Raboaca MS. Blockchain for Future Wireless Networks: A Decade Survey. Sensors. 2022; 22(11):4182. https://doi.org/10.3390/s22114182

BibText

@Article{s22114182,
AUTHOR = {Rathod, Tejal and Jadav, Nilesh Kumar and Alshehri, Mohammad Dahman and Tanwar, Sudeep and Sharma, Ravi and Felseghi, Raluca-Andreea and Raboaca, Maria Simona},
TITLE = {Blockchain for Future Wireless Networks: A Decade Survey},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {11},
ARTICLE-NUMBER = {4182},
URL = {https://www.mdpi.com/1424-8220/22/11/4182},
ISSN = {1424-8220},
ABSTRACT = {The emerging need for high data rate, low latency, and high network capacity encourages wireless networks (WNs) to build intelligent and dynamic services, such as intelligent transportation systems, smart homes, smart cities, industrial automation, etc. However, the WN is impeded by several security threats, such as data manipulation, denial-of-service, injection, man-in-the-middle, session hijacking attacks, etc., that deteriorate the security performance of the aforementioned WN-based intelligent services. Toward this goal, various security solutions, such as cryptography, artificial intelligence (AI), access control, authentication, etc., are proposed by the scientific community around the world; however, they do not have full potential in tackling the aforementioned security issues. Therefore, it necessitates a technology, i.e., a blockchain, that offers decentralization, immutability, transparency, and security to protect the WN from security threats. Motivated by these facts, this paper presents a WNs survey in the context of security and privacy issues with blockchain-based solutions. First, we analyzed the existing research works and highlighted security requirements, security issues in a different generation of WN (4G, 5G, and 6G), and a comparative analysis of existing security solutions. Then, we showcased the influence of blockchain technology and prepared an exhaustive taxonomy for blockchain-enabled security solutions in WN. Further, we also proposed a blockchain and a 6G-based WN architecture to highlight the importance of blockchain technology in WN. Moreover, the proposed architecture is evaluated against different performance metrics, such as scalability, packet loss ratio, and latency. Finally, we discuss various open issues and research challenges for blockchain-based WNs solutions.},
DOI = {10.3390/s22114182}
}

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Tanwar, S, Kakkar, R, Gupta, R, et al. Blockchain-based electric vehicle charging reservation scheme for optimum pricing. Int J Energy Res. 2022; 1- 14. doi:10.1002/er.8199

BibText

@article{https://doi.org/10.1002/er.8199,
author = {Tanwar, Sudeep and Kakkar, Riya and Gupta, Rajesh and Raboaca, Maria Simona and Sharma, Ravi and Alqahtani, Fayez and Tolba, Amr},
title = {Blockchain-based electric vehicle charging reservation scheme for optimum pricing},
journal = {International Journal of Energy Research},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {blockchain, double-auction mechanism, electric vehicle charging, ethereum, optimum pricing, smart contract},
doi = {https://doi.org/10.1002/er.8199},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/er.8199},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.8199},
abstract = {Summary The adaptation of intelligent transportation system has evolved the quality of life of people with the huge demand for electric vehicles. Moreover, it becomes essential to schedule an electric vehicle for charging optimally. Therefore, this paper proposes a blockchain-based electric vehicle charging reservation scheme for optimum pricing. It primarily aims to secure data transactions between electric vehicles and charging stations. It uses the communication channel as 5G to ensure ultra-low latency and extremely high reliability. Furthermore, the proposed scheme uses a double-auction mechanism to optimize the payoff for both electric vehicles and charging stations. The performance of the proposed scheme is evaluated by distinguishing it from the conventional networks such as 4G and LTE-A. The performance parameters are considered profit and loss for electric vehicles, scalability, cost overhead for data storage, communication overhead for data transactions, and data storage cost. Results show that the proposed scheme is secure and achieves the optimized payoff for electric vehicles and charging stations compared with traditional approaches.}
}

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Mankodiya, Harsh, Dhairya Jadav, Rajesh Gupta, Sudeep Tanwar, Abdullah Alharbi, Amr Tolba, Bogdan-Constantin Neagu, and Maria S. Raboaca. 2022. "XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques" Mathematics 10, no. 12: 1990. https://doi.org/10.3390/math10121990

BibText

@Article{math10121990,
AUTHOR = {Mankodiya, Harsh and Jadav, Dhairya and Gupta, Rajesh and Tanwar, Sudeep and Alharbi, Abdullah and Tolba, Amr and Neagu, Bogdan-Constantin and Raboaca, Maria Simona},
TITLE = {XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {12},
ARTICLE-NUMBER = {1990},
URL = {https://www.mdpi.com/2227-7390/10/12/1990},
ISSN = {2227-7390},
ABSTRACT = {A fall detection system is vital for the safety of older people, as it contacts emergency services when it detects a person has fallen. There have been various approaches to detect falls, such as using a single tri-axial accelerometer to detect falls or fixing sensors on the walls of a room to detect falls in a particular area. These approaches have two major drawbacks: either (i) they use a single sensor, which is insufficient to detect falls, or (ii) they are attached to a wall that does not detect a person falling outside its region. Hence, to provide a robust method for detecting falls, the proposed approach uses three different sensors for fall detection, which are placed at five different locations on the subject’s body to gather the data used for training purposes. The UMAFall dataset is used to attain sensor readings to train the models for fall detection. Five models are trained corresponding to the five sensors models, and a majority voting classifier is used to determine the output. Accuracy of 93.5%, 93.5%, 97.2%, 94.6%, and 93.1% is achieved on each of the five sensors models, and 92.54% is the overall accuracy achieved by the majority voting classifier. The XAI technique called LIME is incorporated into the system in order to explain the model’s outputs and improve the model’s interpretability.},
DOI = {10.3390/math10121990}
}

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Deepti Saraswat, Farnazbanu Patel, Pronaya Bhattacharya, Ashwin Verma, Sudeep Tanwar, Ravi Sharma, UpHaaR: Blockchain-based charity donation scheme to handle financial irregularities, Journal of Information Security and Applications, Volume 68, 2022, 103245, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2022.103245. (https://www.sciencedirect.com/science/article/pii/S2214212622001144) Abstract: Non-profit organization raise charity donation through different sources, and raised funds often require a clean traceability. Recently, fake charity fundraisers schemes have raised a social concern, and thus a trusted and auditable approach is necessary to manage charity organizations reputations. Blockchain-based charity (BBC) schemes are proposed with decentralization that ensure trust, transparency, and chronology among donators and beneficiaries. However, the schemes suffer from the high-end complexity of certificate generation. Motivated from this, in this paper, we propose a BBC scheme, UpHaaR, where donators, defined as charity persons (CP), and beneficiaries, defined as charity beneficiaries (CB), register in blockchain, managed for diverse fundraisers by charity organizations (CO). The scheme mitigates fake fundraisers, as the charity scheme data and fund allocations to CP are managed as ledger entries in the blockchain. To maintain the security and privacy of massive CP and CB users, the scheme proposes partial key generations through certificate-less cryptography (CLC), which mitigates expensive digital signing and eliminates certificate generation through key distribution centers. For signature aspects, the scheme proposes a lightweight certificate-less signcryption scheme (CSS) that eliminates collusion against insider attacks and reduces computational costs. The scheme is evaluated for security analysis using automated validation of internet security protocols and applications (AVISPA) tool, and informal attack analysis, and cost evaluations. The proposed results indicate the scheme’s viability against similar conventional approaches. Keywords: Blockchain; Charity donation schemes; Certificate less cryptography; Signcryption; Smart contracts

BibText

@article{SARASWAT2022103245,
title = {UpHaaR: Blockchain-based charity donation scheme to handle financial irregularities},
journal = {Journal of Information Security and Applications},
volume = {68},
pages = {103245},
year = {2022},
issn = {2214-2126},
doi = {https://doi.org/10.1016/j.jisa.2022.103245},
url = {https://www.sciencedirect.com/science/article/pii/S2214212622001144},
author = {Deepti Saraswat and Farnazbanu Patel and Pronaya Bhattacharya and Ashwin Verma and Sudeep Tanwar and Ravi Sharma},
keywords = {Blockchain, Charity donation schemes, Certificate less cryptography, Signcryption, Smart contracts},
abstract = {Non-profit organization raise charity donation through different sources, and raised funds often require a clean traceability. Recently, fake charity fundraisers schemes have raised a social concern, and thus a trusted and auditable approach is necessary to manage charity organizations reputations. Blockchain-based charity (BBC) schemes are proposed with decentralization that ensure trust, transparency, and chronology among donators and beneficiaries. However, the schemes suffer from the high-end complexity of certificate generation. Motivated from this, in this paper, we propose a BBC scheme, UpHaaR, where donators, defined as charity persons (CP), and beneficiaries, defined as charity beneficiaries (CB), register in blockchain, managed for diverse fundraisers by charity organizations (CO). The scheme mitigates fake fundraisers, as the charity scheme data and fund allocations to CP are managed as ledger entries in the blockchain. To maintain the security and privacy of massive CP and CB users, the scheme proposes partial key generations through certificate-less cryptography (CLC), which mitigates expensive digital signing and eliminates certificate generation through key distribution centers. For signature aspects, the scheme proposes a lightweight certificate-less signcryption scheme (CSS) that eliminates collusion against insider attacks and reduces computational costs. The scheme is evaluated for security analysis using automated validation of internet security protocols and applications (AVISPA) tool, and informal attack analysis, and cost evaluations. The proposed results indicate the scheme’s viability against similar conventional approaches.}
}

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Ashwin Verma, Pronaya Bhattacharya, Umesh Bodkhe, Deepti Saraswat, Sudeep Tanwar, Kapal Dev, FedRec: Trusted rank-based recommender scheme for service provisioning in federated cloud environment, Digital Communications and Networks, 2022, , ISSN 2352-8648, https://doi.org/10.1016/j.dcan.2022.06.003. (https://www.sciencedirect.com/science/article/pii/S2352864822001250) Abstract: The emergence of on-demand service provisioning by Federated Cloud Providers (FCPs) to Cloud Users (CU) has fuelled significant innovations in cloud provisioning models. Owing to the massive traffic, massive CU resource requests are sent to FCPs, and appropriate service recommendations are sent by FCPs Currently, the fourth-generation (4G)-Long Term Evolution (LTE) network faces bottlenecks that affect end-user throughput and latency. Moreover, the data is exchanged among heterogeneous stakeholders, and thus trust is a prime concern. To address these limitations, the paper proposes a BlockChain (BC)-leveraged rank-based recommender scheme, FedRec, to expedite secure and trusted cloud Service Provisioning (CSP) to the CU through the FCP, at the backdrop of base 5G communication service. The scheme operates in three phases. In the first phase, a BC-integrated request-response broker model is formulated between the CU, Cloud Brokers (BR), and the FCP, where a CU service request is forwarded through the BR to different FCPs. For service requests, Anything-as-a-Service (XaaS) is supported by 5G-Enhanced Mobile Broadband (eMBB) service. In the next phase, a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender (RBR) model based on the CU requests. In the final phase, based on the matching recommendations between the CU and the FCP, Smart Contracts (SC) are executed, and resource provisioning data is stored on the Interplanetary File Systems (IPFS) that expedite the block validations. The proposed scheme FedRec is compared in terms of SC evaluation and formal verification. In simulation, FedRec achieves a reduction of 27.55% in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks. For the IPFS, we have achieved a bandwidth improvement by 17.91%. In the RBR models, the maximum obtained hit ratio is 0.9314 at 200 million CU requests, showing an improvement of 1.2% in average servicing latency over non-RBR models, and a maximization trade-off of QoE index of 2.768, at the flow request 1.088, at granted service price of USD 1.559 million to FCP for provided services. The obtained results indicate the viability of the proposed scheme against traditional approaches. Keywords: Blockchain; 5G-enhanced mobile broadband; Federated clouds; Rank-based recommender model; Smart contracts

BibText

@article{VERMA2022,
title = {FedRec: Trusted rank-based recommender scheme for service provisioning in federated cloud environment},
journal = {Digital Communications and Networks},
year = {2022},
issn = {2352-8648},
doi = {https://doi.org/10.1016/j.dcan.2022.06.003},
url = {https://www.sciencedirect.com/science/article/pii/S2352864822001250},
author = {Ashwin Verma and Pronaya Bhattacharya and Umesh Bodkhe and Deepti Saraswat and Sudeep Tanwar and Kapal Dev},
keywords = {Blockchain, 5G-enhanced mobile broadband, Federated clouds, Rank-based recommender model, Smart contracts},
abstract = {The emergence of on-demand service provisioning by Federated Cloud Providers (FCPs) to Cloud Users (CU) has fuelled significant innovations in cloud provisioning models. Owing to the massive traffic, massive CU resource requests are sent to FCPs, and appropriate service recommendations are sent by FCPs Currently, the fourth-generation (4G)-Long Term Evolution (LTE) network faces bottlenecks that affect end-user throughput and latency. Moreover, the data is exchanged among heterogeneous stakeholders, and thus trust is a prime concern. To address these limitations, the paper proposes a BlockChain (BC)-leveraged rank-based recommender scheme, FedRec, to expedite secure and trusted cloud Service Provisioning (CSP) to the CU through the FCP, at the backdrop of base 5G communication service. The scheme operates in three phases. In the first phase, a BC-integrated request-response broker model is formulated between the CU, Cloud Brokers (BR), and the FCP, where a CU service request is forwarded through the BR to different FCPs. For service requests, Anything-as-a-Service (XaaS) is supported by 5G-Enhanced Mobile Broadband (eMBB) service. In the next phase, a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender (RBR) model based on the CU requests. In the final phase, based on the matching recommendations between the CU and the FCP, Smart Contracts (SC) are executed, and resource provisioning data is stored on the Interplanetary File Systems (IPFS) that expedite the block validations. The proposed scheme FedRec is compared in terms of SC evaluation and formal verification. In simulation, FedRec achieves a reduction of 27.55% in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks. For the IPFS, we have achieved a bandwidth improvement by 17.91%. In the RBR models, the maximum obtained hit ratio is 0.9314 at 200 million CU requests, showing an improvement of 1.2% in average servicing latency over non-RBR models, and a maximization trade-off of QoE index of 2.768, at the flow request 1.088, at granted service price of USD 1.559 million to FCP for provided services. The obtained results indicate the viability of the proposed scheme against traditional approaches.}
}

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Kumari A, Chintukumar Sukharamwala U, Tanwar S, Raboaca MS, Alqahtani F, Tolba A, Sharma R, Aschilean I, Mihaltan TC. Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System. Sensors. 2022; 22(13):4826. https://doi.org/10.3390/s22134826

BibText

@Article{s22134826,
AUTHOR = {Kumari, Aparna and Chintukumar Sukharamwala, Urvi and Tanwar, Sudeep and Raboaca, Maria Simona and Alqahtani, Fayez and Tolba, Amr and Sharma, Ravi and Aschilean, Ioan and Mihaltan, Traian Candin},
TITLE = {Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System},
JOURNAL = {Sensors},
VOLUME = {22},
YEAR = {2022},
NUMBER = {13},
ARTICLE-NUMBER = {4826},
URL = {https://www.mdpi.com/1424-8220/22/13/4826},
ISSN = {1424-8220},
ABSTRACT = {In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.},
DOI = {10.3390/s22134826}
}

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P. Bhattacharya et al., "A Deep-Q Learning Scheme for Secure Spectrum Allocation and Resource Management in 6G Environment," in IEEE Transactions on Network and Service Management, 2022, doi: 10.1109/TNSM.2022.3186725.

BibText

@ARTICLE{9810019,
  author={Bhattacharya, Pronaya and Patel, Farnazbanu and Alabdulatif, Abdulatif and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj and Sharma, Ravi},
  journal={IEEE Transactions on Network and Service Management}, 
  title={A Deep-Q Learning Scheme for Secure Spectrum Allocation and Resource Management in 6G Environment}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TNSM.2022.3186725}}

Plain Text

A. Verma et al., "Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3186892.

BibText

@ARTICLE{9809962,
  author={Verma, Ashwin and Bhattacharya, Pronaya and Madhani, Nirav and Trivedi, Chandan and Bhushan, Bharat and Tanwar, Sudeep and Sharma, Gulshan and Bokoro, Pitshou N and Sharma, Ravi},
  journal={IEEE Access}, 
  title={Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3186892}}

Plain Text

Sachi Chaudhary, Riya Kakkar, Nilesh Kumar Jadav, Anuja Nair, Rajesh Gupta, Sudeep Tanwar, Smita Agrawal, Mohammad Dahman Alshehri, Ravi Sharma, Gulshan Sharma, Innocent E. Davidson, "A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions", Journal of Sensors, vol. 2022, Article ID 1863838, 30 pages, 2022. https://doi.org/10.1155/2022/1863838

BibText

Sachi Chaudhary, Riya Kakkar, Nilesh Kumar Jadav, Anuja Nair, Rajesh Gupta, Sudeep Tanwar, Smita Agrawal, Mohammad Dahman Alshehri, Ravi Sharma, Gulshan Sharma, Innocent E. Davidson, "A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions", Journal of Sensors, vol. 2022, Article ID 1863838, 30 pages, 2022. https://doi.org/10.1155/2022/1863838

Plain Text

Sachi Chaudhary, Riya Kakkar, Nilesh Kumar Jadav, Anuja Nair, Rajesh Gupta, Sudeep Tanwar, Smita Agrawal, Mohammad Dahman Alshehri, Ravi Sharma, Gulshan Sharma, Innocent E. Davidson, "A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions", Journal of Sensors, vol. 2022, Article ID 1863838, 30 pages, 2022. https://doi.org/10.1155/2022/1863838

BibText

Sachi Chaudhary, Riya Kakkar, Nilesh Kumar Jadav, Anuja Nair, Rajesh Gupta, Sudeep Tanwar, Smita Agrawal, Mohammad Dahman Alshehri, Ravi Sharma, Gulshan Sharma, Innocent E. Davidson, "A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions", Journal of Sensors, vol. 2022, Article ID 1863838, 30 pages, 2022. https://doi.org/10.1155/2022/1863838

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A. Verma, P. Bhattacharya, D. Saraswat, S. Tanwar, N. Kumar and R. Sharma, "SanJeeVni: Secure UAV-envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network," in IEEE Sensors Journal, 2022, doi: 10.1109/JSEN.2022.3188929.

BibText

@ARTICLE{9827943,
  author={Verma, Ashwin and Bhattacharya, Pronaya and Saraswat, Deepti and Tanwar, Sudeep and Kumar, Neeraj and Sharma, Ravi},
  journal={IEEE Sensors Journal}, 
  title={SanJeeVni: Secure UAV-envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JSEN.2022.3188929}}

Plain Text

Jha, Keshav, Akhil Gupta, Abdulatif Alabdulatif, Sudeep Tanwar, Calin O. Safirescu, and Traian C. Mihaltan. 2022. "CSVAG: Optimizing Vertical Handoff Using Hybrid Cuckoo Search and Genetic Algorithm-Based Approaches" Sustainability 14, no. 14: 8547. https://doi.org/10.3390/su14148547

BibText

@Article{su14148547,
AUTHOR = {Jha, Keshav and Gupta, Akhil and Alabdulatif, Abdulatif and Tanwar, Sudeep and Safirescu, Calin Ovidiu and Mihaltan, Traian Candin},
TITLE = {CSVAG: Optimizing Vertical Handoff Using Hybrid Cuckoo Search and Genetic Algorithm-Based Approaches},
JOURNAL = {Sustainability},
VOLUME = {14},
YEAR = {2022},
NUMBER = {14},
ARTICLE-NUMBER = {8547},
URL = {https://www.mdpi.com/2071-1050/14/14/8547},
ISSN = {2071-1050},
ABSTRACT = {One of the primary challenges that wireless technology in the present generation is facing is always best connected (ABC) service. This is possible only when the wireless overlay networks follow a cooperative and coordinated process. Vertical handoff is one such process. Concerning this process, the main challenge is to develop algorithms that take care of optimal connection management with proper resource utilization for uninterrupted mobility. In this paper, we develop a new hybrid cuckoo search (CS) and genetic algorithm (GA) that maximizes the performance of heterogeneous wireless systems in terms of minimizing latency, handover failure probability, and enhancing the throughput. We focus on an optimized simulation framework to demonstrate the advantage of our hybrid model. It can be discerned from the simulation analysis that the proposed hybrid technique increases throughput by 17% and 8% compared to the cuckoo search and genetic algorithms applied individually. The performance of the proposed scheme is promising for applications wherein the handoff mechanisms have to be optimized to control frequent handoffs to further reduce the power consumption of user equipment.},
DOI = {10.3390/su14148547}
}

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V. K. Prasad et al., "ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3190497.

BibText

@ARTICLE{9828031,
  author={Prasad, Vivek Kumar and Bhattacharya, Pronaya and Bhavsar, Madhuri and Verma, Ashwin and Tanwar, Sudeep and Sharma, Gulshan and Bokoro, Pitshou N and Sharma, Ravi},
  journal={IEEE Access}, 
  title={ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3190497}}

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Anand, P., Singh, Y., Singh, H. et al. SALT: transfer learning-based threat model for attack detection in smart home. Sci Rep 12, 12247 (2022). https://doi.org/10.1038/s41598-022-16261-9

BibText

Anand, P., Singh, Y., Singh, H. et al. SALT: transfer learning-based threat model for attack detection in smart home. Sci Rep 12, 12247 (2022). https://doi.org/10.1038/s41598-022-16261-9

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R. Gupta, N. K. Jadav, H. Mankodiya, M. D. Alshehri, S. Tanwar and R. Sharma, "Blockchain and Onion Routing-based Secure Message Exchange System for Edge-enabled IIoT," in IEEE Transactions on Industrial Informatics, 2022, doi: 10.1109/TII.2022.3191444.

BibText

@ARTICLE{9832760,
  author={Gupta, Rajesh and Jadav, Nilesh Kumar and Mankodiya, Harsh and Alshehri, Mohammad Dahman and Tanwar, Sudeep and Sharma, Ravi},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={Blockchain and Onion Routing-based Secure Message Exchange System for Edge-enabled IIoT}, 
  year={2022},
  volume={},
  number={},
  pages={1-12},
  doi={10.1109/TII.2022.3191444}}

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Hathaliya J, Parekh R, Patel N, Gupta R, Tanwar S, Alqahtani F, Elghatwary M, Ivanov O, Raboaca MS, Neagu B-C. Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data. Mathematics. 2022; 10(15):2566. https://doi.org/10.3390/math10152566

BibText

@Article{math10152566,
AUTHOR = {Hathaliya, Jigna and Parekh, Raj and Patel, Nisarg and Gupta, Rajesh and Tanwar, Sudeep and Alqahtani, Fayez and Elghatwary, Magdy and Ivanov, Ovidiu and Raboaca, Maria Simona and Neagu, Bogdan-Constantin},
TITLE = {Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {15},
ARTICLE-NUMBER = {2566},
URL = {https://www.mdpi.com/2227-7390/10/15/2566},
ISSN = {2227-7390},
ABSTRACT = {In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize the deficiency of dopamine-generated patterns inside the brain. These patterns are used to establish a patient’s disease progression, which helps distinguish the patients into different categories. Furthermore, we used a convolutional neural network (CNN) model to classify the patients based on the dopamine level inside the brain. The dataset used throughout this paper is the Parkinson’s progressive markers initiative (PPMI) dataset. The collected dataset was pre-processed and data amplification was performed to balance the imbalanced dataset. A CNN-based neural network was defined to classify input SPECT images into four categories. The motivation behind the proposed model is to reduce the number of resources consumed while maintaining the performance of the classification model. This will help the healthcare ecosystem run the classification model on mobile devices. The proposed model contains 14 layers with input layers, convolutional layers, max-pool layers, flatten layers, and dense layers with different dimensions. The dense layer classifies the patients into four different categories, including PSD, healthy control, scans without evidence of dopaminergic deficit (SWEDD), and GenReg PSD from the entire SPECT imaging dataset, which is used to establish the disease progression of different patients using SPECT images. The proposed model is trained with a large dataset with 58,692 images for training and 11,738 images for validation, and 7826 for testing. The proposed model outperforms the classification models from the surveyed papers. The proposed model’s accuracy is 0.889, recall is 0.9012, the precision is 0.9104, and the F1-score is 0.9057.},
DOI = {10.3390/math10152566}
}

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Hathaliya J, Modi H, Gupta R, Tanwar S, Alqahtani F, Elghatwary M, Neagu B-C, Raboaca MS. Stacked Model-Based Classification of Parkinson’s Disease Patients Using Imaging Biomarker Data. Biosensors. 2022; 12(8):579. https://doi.org/10.3390/bios12080579

BibText

@Article{bios12080579,
AUTHOR = {Hathaliya, Jigna and Modi, Hetav and Gupta, Rajesh and Tanwar, Sudeep and Alqahtani, Fayez and Elghatwary, Magdy and Neagu, Bogdan-Constantin and Raboaca, Maria Simona},
TITLE = {Stacked Model-Based Classification of Parkinson’s Disease Patients Using Imaging Biomarker Data},
JOURNAL = {Biosensors},
VOLUME = {12},
YEAR = {2022},
NUMBER = {8},
ARTICLE-NUMBER = {579},
URL = {https://www.mdpi.com/2079-6374/12/8/579},
ISSN = {2079-6374},
ABSTRACT = {Parkinson’s disease (PSD) is a neurological disorder of the brain where nigrostriatal integrity functions lead to motor and non-motor-based symptoms. Doctors can assess the patient based on the patient’s history and symptoms; however, the symptoms are similar in various neurodegenerative diseases, such as progressive supranuclear palsy (PSP), multiple system atrophy—parkinsonian type (MSA), essential tremor, and Parkinson’s tremor. Thus, sometimes it is difficult to identify a patient’s disease based on his or her symptoms. To address the issue, we have used neuroimaging biomarkers to analyze dopamine deficiency in the brains of subjects. We generated the different patterns of dopamine levels inside the brain, which identified the severity of the disease and helped us to measure the disease progression of the patients. For the classification of the subjects, we used machine learning (ML) algorithms for a multivariate classification of the subjects using neuroimaging biomarkers data. In this paper, we propose a stacked machine learning (ML)-based classification model to identify the HC and PSD subjects. In this stacked model, meta learners can learn and combine the predictions from various ML algorithms, such as K-nearest neighbor (KNN), random forest algorithm (RFA), and Gaussian naive Bayes (GANB) to achieve a high performance model. The proposed model showed 92.5% accuracy, outperforming traditional schemes.},
DOI = {10.3390/bios12080579}
}

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Kakkar R, Gupta R, Agrawal S, Bhattacharya P, Tanwar S, Raboaca MS, Alqahtani F, Tolba A. Blockchain and Double Auction-Based Trustful EVs Energy Trading Scheme for Optimum Pricing. Mathematics. 2022; 10(15):2748. https://doi.org/10.3390/math10152748

BibText

@Article{math10152748,
AUTHOR = {Kakkar, Riya and Gupta, Rajesh and Agrawal, Smita and Bhattacharya, Pronaya and Tanwar, Sudeep and Raboaca, Maria Simona and Alqahtani, Fayez and Tolba, Amr},
TITLE = {Blockchain and Double Auction-Based Trustful EVs Energy Trading Scheme for Optimum Pricing},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {15},
ARTICLE-NUMBER = {2748},
URL = {https://www.mdpi.com/2227-7390/10/15/2748},
ISSN = {2227-7390},
ABSTRACT = {Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical study problem. The problem is further intensified due to the scarcity of charging stations (CSs) in near EV proximity. Moreover, as energy transactions occur over open channels, it presents critical security, privacy, and trust issues among decentralized channels. To address the open limitations of trusted energy management and optimize the pricing control among EV entities (i.e., prosumers and consumers), the paper proposes a scheme that integrates blockchain and a truthful double auction strategy for trustful EV trading. To address the transaction scalability, we integrate an Interplanetary File System (IPFS) with a double auction mechanism handled through the Remix Smart Contract environment. The double auction leverages an optimal payoff condition between peer EVs. To address the communication latency, we present the scheme at the backdrop of Fifth Generation (5G) networks that minimizes the optimal payoff response time. The scheme is simulated against parameters such as convergence, profit for consumers, computation time, and blockchain analysis regarding node commit latency, collusion attacks, and EV energy consumption. The results indicate the scheme’s viability against traditional (non-blockchain) approaches with high reliability, scalability, and improved cost-efficiency.},
DOI = {10.3390/math10152748}
}

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D. Saraswat et al., "Explainable AI for Healthcare 5.0: Opportunities and Challenges," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3197671.

BibText

@ARTICLE{9852458,
  author={Saraswat, Deepti and Bhattacharya, Pronaya and Verma, Ashwin and Prasad, Vivek Kumar and Tanwar, Sudeep and Sharma, Gulshan and Bokoro, Pitshou N and Sharma, Ravi},
  journal={IEEE Access}, 
  title={Explainable AI for Healthcare 5.0: Opportunities and Challenges}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3197671}}

Plain Text

Kumari A, Patel RK, Sukharamwala UC, Tanwar S, Raboaca MS, Saad A, Tolba A. AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System. Mathematics. 2022; 10(16):2852. https://doi.org/10.3390/math10162852

BibText

@Article{math10162852,
AUTHOR = {Kumari, Aparna and Patel, Rushil Kaushikkumar and Sukharamwala, Urvi Chintukumar and Tanwar, Sudeep and Raboaca, Maria Simona and Saad, Aldosary and Tolba, Amr},
TITLE = {AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {16},
ARTICLE-NUMBER = {2852},
URL = {https://www.mdpi.com/2227-7390/10/16/2852},
ISSN = {2227-7390},
ABSTRACT = {The existing grid infrastructure has already begun transforming into the next-generation cyber-physical smart grid (SG) system. This transformation has improved the grid’s reliability and efficiency but has exposed severe vulnerabilities due to growing cyberattacks and threats. For example, malicious actors may be able to tamper with system readings, parameters, and energy prices and penetrate to get direct access to the data. Several works exist to handle the aforementioned issues, but they have not been fully explored. Consequently, this paper proposes an AI-ADP scheme for the SG system, which is an artificial intelligence (AI)-based attack-detection and prevention (ADP) mechanism by using a cryptography-driven recommender system to ensure data security and integrity. The proposed AI-ADP scheme is divided into two phases: (i) attack detection and (ii) attack prevention. We employed the extreme gradient-boosting (XGBoost) mechanism for attack detection and classification. It is a new ensemble learning methodology that offers many advantages over similar methods, including built-in features, etc. Then, SHA-512 is used to secure the communication that employs faster performance, allowing the transmission of more data with the same security level. The performance of the proposed AI-ADP scheme is evaluated based on various parameters, such as attack-detection accuracy, cycles used per byte, and total cycles used. The proposed AI-ADP scheme outperformed the existing approaches and obtained 99.12% accuracy, which is relatively high compared to the pre-existing methods.},
DOI = {10.3390/math10162852}
}

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Mankodiya H, Palkhiwala P, Gupta R, Jadav NK, Tanwar S, Neagu B-C, Grigoras G, Alqahtani F, Shehata AM. A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors. Mathematics. 2022; 10(16):2927. https://doi.org/10.3390/math10162927

BibText

@Article{math10162927,
AUTHOR = {Mankodiya, Harsh and Palkhiwala, Priyal and Gupta, Rajesh and Jadav, Nilesh Kumar and Tanwar, Sudeep and Neagu, Bogdan-Constantin and Grigoras, Gheorghe and Alqahtani, Fayez and Shehata, Ahmed M.},
TITLE = {A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {16},
ARTICLE-NUMBER = {2927},
URL = {https://www.mdpi.com/2227-7390/10/16/2927},
ISSN = {2227-7390},
ABSTRACT = {Artificial intelligence has been utilized extensively in the healthcare sector for the last few decades to simplify medical procedures, such as diagnosis, prognosis, drug discovery, and many more. With the spread of the COVID-19 pandemic, more methods for detecting and treating COVID-19 infections have been developed. Several projects involving considerable artificial intelligence use have been researched and put into practice. Crowdsensing is an example of an application in which artificial intelligence is employed to detect the presence of a virus in an individual based on their physiological parameters. A solution is proposed to detect the potential COVID-19 carrier in crowded premises of a closed campus area, for example, hospitals, corridors, company premises, and so on. Sensor-based wearable devices are utilized to obtain measurements of various physiological indicators (or parameters) of an individual. A machine-learning-based model is proposed for COVID-19 prediction with these parameters as input. The wearable device dataset was used to train four different machine learning algorithms. The support vector machine, which performed the best, received an F1-score of 96.64% and an accuracy score of 96.57%. Moreover, the wearable device is used to retrieve the coordinates of a potential COVID-19 carrier, and the YOLOv5 object detection method is used to do real-time visual tracking on a closed-circuit television video feed.},
DOI = {10.3390/math10162927}
}

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Patel D, Sanghvi H, Jadav NK, Gupta R, Tanwar S, Florea BC, Taralunga DD, Altameem A, Altameem T, Sharma R. BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety. Mathematics. 2022; 10(17):3195. https://doi.org/10.3390/math10173195

BibText

@Article{math10173195,
AUTHOR = {Patel, Dev and Sanghvi, Harshil and Jadav, Nilesh Kumar and Gupta, Rajesh and Tanwar, Sudeep and Florea, Bogdan Cristian and Taralunga, Dragos Daniel and Altameem, Ahmed and Altameem, Torki and Sharma, Ravi},
TITLE = {BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety},
JOURNAL = {Mathematics},
VOLUME = {10},
YEAR = {2022},
NUMBER = {17},
ARTICLE-NUMBER = {3195},
URL = {https://www.mdpi.com/2227-7390/10/17/3195},
ISSN = {2227-7390},
ABSTRACT = {Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. Breakthroughs in deep learning (DL) make it feasible to address such challenges and construct a complete intelligent framework that automatically detects such malicious behaviors. Motivated by this, we propose a convolutional neural network (CNN)-based Xception model, i.e., BlockCrime, to detect crimes and improve public safety. Furthermore, we integrate blockchain technology to securely store the detected crime scene locations and alert the nearest law enforcement authorities. Due to the scarcity of the dataset, transfer learning has been preferred, in which a CNN-based Xception model is used. The redesigned Xception architecture is evaluated against various assessment measures, including accuracy, F1 score, precision, and recall, where it outperforms existing CNN architectures in terms of train accuracy, i.e., 96.57%.},
DOI = {10.3390/math10173195}
}

Plain Text

Jadav, NK, Nair, AR, Gupta, R, Tanwar, S, Lakys, Y, Sharma, R. AI-driven network softwarization scheme for efficient message exchange in IoT environment beyond 5G. Int J Commun Syst. 2022;e5336. doi:10.1002/dac.5336

BibText

@article{https://doi.org/10.1002/dac.5336,
author = {Jadav, Nilesh Kumar and Nair, Anuja R. and Gupta, Rajesh and Tanwar, Sudeep and Lakys, Yahya and Sharma, Ravi},
title = {AI-driven network softwarization scheme for efficient message exchange in IoT environment beyond 5G},
journal = {International Journal of Communication Systems},
volume = {n/a},
number = {n/a},
pages = {e5336},
keywords = {6G network, artificial intelligence, internet of things, LSTM, network softwarization, software-defined networking},
doi = {https://doi.org/10.1002/dac.5336},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.5336},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.5336},
abstract = {Summary Internet of things (IoT) has massively adopted the market due to the current era demanding fully intelligent and autonomous services. However, efficient network management and message exchange are challenging in a dynamic and energy-constrained IoT environment. Hence, for efficient message passing in IoT applications, softwarization of the IoT network is essential, wherein the logical control plane is decoupled from the data plane consisting of hardware devices such as routers and switches. Using softwarization, a centralized software-defined networking (SDN) controller is responsible for routing data packets from source to destination in a dynamic environment. However, to reduce the computational overhead of filtering malicious and nonmalicious packets, artificial intelligence (AI) classifiers prove beneficial. Moreover, such challenges have trade-offs with network requirements such as ultralow latency, high reliability, and higher data rates. Motivated by this, we propose an AI-enabled network softwarization scheme for efficient message exchange under the 6G network. Lastly, the performance of the proposed scheme is evaluated with different performance metrics, such as accuracy, precision, f1-score, packet drop ratio, and latency. The empirical result revealed that the proposed scheme outperforms in terms of accuracy and controller efficiency, that is, 81.64\% and 82.2\%, respectively.}
}

Plain Text

Khanpara, P, Lavingia, K, Trivedi, R, Tanwar, S, Verma, A, Sharma, R. A context-aware internet of things-driven security scheme for smart homes. Security and Privacy. 2022;e269. doi:10.1002/spy2.269

BibText

@article{https://doi.org/10.1002/spy2.269,
author = {Khanpara, Pimal and Lavingia, Kruti and Trivedi, Rajvi and Tanwar, Sudeep and Verma, Amit and Sharma, Ravi},
title = {A context-aware internet of things-driven security scheme for smart homes},
journal = {SECURITY AND PRIVACY},
volume = {n/a},
number = {n/a},
pages = {e269},
keywords = {context-aware security, IoT, role and object-based context-aware security, smart city, smart home system},
doi = {https://doi.org/10.1002/spy2.269},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/spy2.269},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/spy2.269},
abstract = {Abstract In recent years, the Internet of Things (IoT) has become very popular as it has numerous applications in the industrial and research domain. Moreover, the features of IoT systems play a crucial role in the development of smart cities. It enables smart cities and their subsystems to monitor, control, and manage heterogeneous devices remotely by extracting and communicating real-time data. However, automated IoT systems are vulnerable to many security threats like tempered protocols, device hijacking, and unauthorized access. Motivated by the aforementioned discussion, this paper addresses the security requirements of an essential subsystem of smart city architecture, that is, IoT-based smart homes. Based on the features and functionalities of smart homes, the risk of security violations in the system behavior needs to be analyzed This paper explores various security threats in a smart home environment and proposes a context-aware security-based scheme to prevent and detect possible threats. Results show that the proposed scheme is superior compared to the traditional schemes considering parameters such as the performance, cost, and maintenance requirements.}
}

Plain Text

K. Agrawal, M. Aggarwal, S. Tanwar, G. Sharma, P. N. Bokoro and R. Sharma, "An Extensive Blockchain based Applications Survey: Tools, Frameworks, Opportunities, Challenges and Solutions," in IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3219160.

BibText

@ARTICLE{9936616,
  author={Agrawal, Kanika and Aggarwal, Mayank and Tanwar, Sudeep and Sharma, Gulshan and Bokoro, Pitshou N and Sharma, Ravi},
  journal={IEEE Access}, 
  title={An Extensive Blockchain based Applications Survey: Tools, Frameworks, Opportunities, Challenges and Solutions}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2022.3219160}}

Plain Text

Kakkar R, Gupta R, Agrawal S, Tanwar S, Sharma R, Alkhayyat A, Neagu B-C, Raboaca MS. A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure. Applied Sciences. 2022; 12(23):12096. https://doi.org/10.3390/app122312096

BibText

@Article{app122312096,
AUTHOR = {Kakkar, Riya and Gupta, Rajesh and Agrawal, Smita and Tanwar, Sudeep and Sharma, Ravi and Alkhayyat, Ahmed and Neagu, Bogdan-Constantin and Raboaca, Maria Simona},
TITLE = {A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure},
JOURNAL = {Applied Sciences},
VOLUME = {12},
YEAR = {2022},
NUMBER = {23},
ARTICLE-NUMBER = {12096},
URL = {https://www.mdpi.com/2076-3417/12/23/12096},
ISSN = {2076-3417},
ABSTRACT = {The deployment of charging infrastructure is one of the main challenges that need to be tackled due to the increasing demand for electric vehicles (EVs). Moreover, EVs associated with different charging standards can face compatibility issues while charging via public or private infrastructure. Many solutions were surveyed by researchers on EVs, but they were not focused on addressing the issue of charging infrastructure standardization. Motivated by this, we present a comprehensive survey on standardizing EV charging infrastructure. We also present a taxonomy on various aspects such as charging levels, charging modes, charging standards, charging technologies (based on the different charging types such as conductive charging and wireless charging), and types of vehicle (i.e., 2-wheeler (2W), 3-wheeler (3W), and 4-wheeler (4W)). Furthermore, we target the benefits associated with community EV charging operated by the community charging service operator. Furthermore, we propose an architecture for standardized EV community charging infrastructure to provide adaptability for EVs with different charging standards. Finally, the research challenges and opportunities of the proposed survey have been discussed for efficient EV charging.},
DOI = {10.3390/app122312096}
}

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Gupta R, Bhattacharya P, Tanwar S, Sharma R, Alqahtani F, Tolba A, Èšurcanu F-E, Raboaca MS. Fight against Future Pandemics: UAV-Based Data-Centric Social Distancing, Sanitizing, and Monitoring Scheme. Drones. 2022; 6(12):381. https://doi.org/10.3390/drones6120381

BibText

@Article{drones6120381,
AUTHOR = {Gupta, Rajesh and Bhattacharya, Pronaya and Tanwar, Sudeep and Sharma, Ravi and Alqahtani, Fayez and Tolba, Amr and Èšurcanu, Florin-Emilian and Raboaca, Maria Simona},
TITLE = {Fight against Future Pandemics: UAV-Based Data-Centric Social Distancing, Sanitizing, and Monitoring Scheme},
JOURNAL = {Drones},
VOLUME = {6},
YEAR = {2022},
NUMBER = {12},
ARTICLE-NUMBER = {381},
URL = {https://www.mdpi.com/2504-446X/6/12/381},
ISSN = {2504-446X},
ABSTRACT = {The novel coronavirus disease-2019 (COVID-19) has transformed into a global health concern, which resulted in human containment and isolation to flatten the curve of mortality rates of infected patients. To leverage the massive containment strategy, fifth-generation (5G)-envisioned unmanned aerial vehicles (UAVs) are used to minimize human intervention with the key benefits of ultra-low latency, high bandwidth, and reliability. This allows phased treatment of infected patients via threefold functionalities (3FFs) such as social distancing, proper sanitization, and inspection and monitoring. However, UAVs have to send massive recorded data back to ground stations (GS), which requires a real-time device connection density of 107/km2, which forms huge bottlenecks on 5G ecosystems. A sixth-generation (6G) ecosystem can provide terahertz (THz) frequency bands with massive short beamforming cells, intelligent deep connectivity, and physical- and link-level protocol virtualization. The UAVs form a swarm network to assure 3FFs which requires high-end computations and are data-intensive; thus, these computational tasks can be offloaded to nearby edge servers, which employ local federated learning to train the global models. It synchronizes the UAV task formations and optimizes the network functions. Task optimization of UAV swarms in 6G-assisted channels allows better management and ubiquitous and energy-efficient seamless communication over ground, space, and underwater channels. Thus, a data-centric 3FF approach is essential to fight against future pandemics, with a 6G backdrop channel. The proposed scheme is compared with traditional fourth-generation (4G) and 5G-networks-based schemes to indicate its efficiency in traffic density, processing latency, spectral efficiency, UAV mobility, radio loss, and device connection density.},
DOI = {10.3390/drones6120381}
}

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Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Secrecy-ensured NOMA-based cooperative D2D-aided fog computing under imperfect CSI, Journal of Information Security and Applications, Volume 59, 2021, 102812, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2021.102812

BibText

@article{GUPTA2021102812,
title = {Secrecy-ensured NOMA-based cooperative D2D-aided fog computing under imperfect CSI},
journal = {Journal of Information Security and Applications},
volume = {59},
pages = {102812},
year = {2021},
issn = {2214-2126},
doi = {https://doi.org/10.1016/j.jisa.2021.102812},
url = {https://www.sciencedirect.com/science/article/pii/S2214212621000521},
author = {Rajesh Gupta and Sudeep Tanwar and Neeraj Kumar},
keywords = {Device-to-device communication, Fog computing, Sum rate, Secrecy capacity, Non-orthogonal multiple access, Channel state information}
}

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Dhyey Mehta, Sudeep Tanwar, Umesh Bodkhe, Arpit Shukla, Neeraj Kumar, Blockchain-based royalty contract transactions scheme for Industry 4.0 supply-chain management, Information Processing & Management, Volume 58, Issue 4, 2021, 102586, ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2021.102586.

BibText

@article{MEHTA2021102586,
title = {Blockchain-based royalty contract transactions scheme for Industry 4.0 supply-chain management},
journal = {Information Processing & Management},
volume = {58},
number = {4},
pages = {102586},
year = {2021},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2021.102586},
url = {https://www.sciencedirect.com/science/article/pii/S0306457321000868},
author = {Dhyey Mehta and Sudeep Tanwar and Umesh Bodkhe and Arpit Shukla and Neeraj Kumar}
}

Plain Text

Aparna Kumari, Rajesh Gupta, Sudeep Tanwar, Amalgamation of blockchain and IoT for smart cities underlying 6G communication: A comprehensive review, Computer Communications, Volume 172, 2021, Pages 102-118, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2021.03.005.

BibText

@article{KUMARI2021102,
title = {Amalgamation of blockchain and IoT for smart cities underlying 6G communication: A comprehensive review},
journal = {Computer Communications},
volume = {172},
pages = {102-118},
year = {2021},
issn = {0140-3664},
doi = {https://doi.org/10.1016/j.comcom.2021.03.005},
url = {https://www.sciencedirect.com/science/article/pii/S0140366421001006},
author = {Aparna Kumari and Rajesh Gupta and Sudeep Tanwar}
}

Plain Text

Tanwar, S, Popat, A, Bhattacharya, P, Gupta, R, Kumar, N. A taxonomy of energy optimization techniques for smart cities: Architecture and future directions. Expert Systems. 2021;e12703. https://doi.org/10.1111/exsy.12703

BibText

@article{article,
author = {Tanwar, Sudeep and Popat, Aarti and Bhattacharya, Pronaya and Gupta, Rajesh and Kumar, Neeraj},
year = {2021},
month = {03},
pages = {},
title = {A Taxonomy of Energy Optimization Techniques for Smart Cities: Architecture and Future Directions},
journal = {Expert Systems}
}

Plain Text

Rajesh Gupta, Dakshita Reebadiya, Sudeep Tanwar, 6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission, Computer Standards & Interfaces, Volume 77, 2021, 103521, ISSN 0920-5489, https://doi.org/10.1016/j.csi.2021.103521

BibText

@article{GUPTA2021103521,
title = {6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission},
journal = {Computer Standards & Interfaces},
volume = {77},
pages = {103521},
year = {2021},
issn = {0920-5489},
doi = {https://doi.org/10.1016/j.csi.2021.103521},
url = {https://www.sciencedirect.com/science/article/pii/S0920548921000167},
author = {Rajesh Gupta and Dakshita Reebadiya and Sudeep Tanwar},
keywords = {Edge intelligence, 6G, Ultra-reliable low-latency, COVID-19, Internet of drones, Holographic communication},
abstract = {The increasing demand for automation and instant solution leads the technological world towards massive applications such as the Internet of drones, Autonomous Vehicles (AVs), border surveillance, telesurgery, and Augmented Reality (AR), which requires a vast upgrade in technology with improved processing and computation capabilities. The Centralized Cloud Server (CCS) provides the facility to compute critical tasks at the central data server, but it consumes more time due to the distance between CSS and edge device. In this article, we discuss the advantages of edge computing over cloud computing to overcome latency and reliability issues in critical applications. Moreover, the idea of processing and analyzing massive applications at the edge comes up with the requirement of building intelligence at the edge to compute complex tasks within a negligible time. Edge intelligence offers intelligence at the edge to process large datasets for critical computations and to overcome storage issues. Also, the performance tolerant connectivity and low-speed rate issues with 4G and 5G can be solved using a 6G wireless network. The 6G connected edge intelligence application offers ultra-low-latency, security, and reliability mechanisms that could be helpful in COVID-19 pandemic situations. We have also discussed the demonstration of aforementioned massive application in the form of a case study on combating COVID-19 situations using 6G-based edge intelligence. The case study depicts the benefits of using 6G (latency: 10?100?s) over 4G (latency: <10ms) and 5G (latency: <5ms) communication networks. The proposed 6G-enabled scheme is compared against the traditional 4G and 5G networks to designate its efficiency in terms of communication latency and network mobility. Eventually, we then analyzed various open issues and research challenges in this emerging research area for future gains and insights.}
}

Plain Text

Reebadiya, Dakshita & Rathod, Tejal & Gupta, Rajesh & Tanwar, Sudeep & Kumar, Neeraj. (2021). Blockchain-based Secure and Intelligent Sensing for Autonomous Vehicles Activity Tracking Beyond 5G Networks. Peer-to-Peer Networking and Applications. 10.1007/s12083-021-01073-x.

BibText

@article{article,
author = {Reebadiya, Dakshita and Rathod, Tejal and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj},
year = {2021},
month = {02},
pages = {},
title = {Blockchain-based Secure and Intelligent Sensing for Autonomous Vehicles Activity Tracking Beyond 5G Networks},
journal = {Peer-to-Peer Networking and Applications},
doi = {10.1007/s12083-021-01073-x}
}

Plain Text

Nama, Mahima & Nath, Ankita & Bechra, Nancy & Bhatia, Jitendra & Tanwar, Sudeep & Chaturvedi, Manish & Sadoun, Balqies. (2021). Machine Learning-based Traffic Scheduling Techniques for Intelligent Transportation System: Opportunities and Challenges. International Journal of Communication Systems. 10.1002/dac.4814.

BibText

@article{https://doi.org/10.1002/dac.4814,
author = {Nama, Mahima and Nath, Ankita and Bechra, Nancy and Bhatia, Jitendra and Tanwar, Sudeep and Chaturvedi, Manish and Sadoun, Balqies},
title = {Machine learning-based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges},
journal = {International Journal of Communication Systems},
volume = {n/a},
number = {n/a},
pages = {e4814},
keywords = {intelligent transportation system (ITS), machine learning, reallocation, scheduling, traffic police},
doi = {https://doi.org/10.1002/dac.4814},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.4814},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.4814}
}

Plain Text

Shah, Jay & Agrawal, Sarthak & Shukla, Arpit & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj. (2021). Blockchain-based Scheme for the Mobile Number Portability. Journal of Information Security and Applications. 58. 10.1016/j.jisa.2021.102764.

BibText

@article{article,
author = {Shah, Jay and Agrawal, Sarthak and Shukla, Arpit and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2021},
month = {01},
pages = {},
title = {Blockchain-based Scheme for the Mobile Number Portability},
volume = {58},
journal = {Journal of Information Security and Applications},
doi = {10.1016/j.jisa.2021.102764}
}

Plain Text

Kumari, Aparna & Tanwar, Sudeep. (2021). A Secure Data Analytics Scheme for Multimedia Communication in a Decentralized Smart Grid. Multimedia Tools and Applications. 10.1007/s11042-021-10512-z.

BibText

@article{article,
author = {Kumari, Aparna and Tanwar, Sudeep},
year = {2021},
month = {02},
pages = {},
title = {A Secure Data Analytics Scheme for Multimedia Communication in a Decentralized Smart Grid},
journal = {Multimedia Tools and Applications},
doi = {10.1007/s11042-021-10512-z}
}

Plain Text

D. Prerna, R. Tekchandani, N. Kumar and S. Tanwar, "An Energy-Efficient Cache Localization Technique for D2D Communication in IoT Environment," in IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4816-4829, 15 March15, 2021, doi: 10.1109/JIOT.2020.3029168.

BibText

@ARTICLE{9214841,  author={Prerna, Divya and Tekchandani, Rajkumar and Kumar, Neeraj and Tanwar, Sudeep},  journal={IEEE Internet of Things Journal},   title={An Energy-Efficient Cache Localization Technique for D2D Communication in IoT Environment},   year={2021},  volume={8},  number={6},  pages={4816-4829},  doi={10.1109/JIOT.2020.3029168}}

Plain Text

Khatri, Sahil & Vachhani, Hrishikesh & Shah, Shalin & Bhatia, Jitendra & Chaturvedi, Manish & Tanwar, Sudeep & Kumar, Neeraj. (2021). Machine Learning Models and Techniques for VANET Based Traffic Management: Implementation Issues and Challenges. Peer-to-Peer Networking and Applications. 10.1007/s12083-020-00993-4.

BibText

@article{article,
author = {Khatri, Sahil and Vachhani, Hrishikesh and Shah, Shalin and Bhatia, Jitendra and Chaturvedi, Manish and Tanwar, Sudeep and Kumar, Neeraj},
year = {2021},
month = {05},
pages = {},
title = {Machine Learning Models and Techniques for VANET Based Traffic Management: Implementation Issues and Challenges},
journal = {Peer-to-Peer Networking and Applications},
doi = {10.1007/s12083-020-00993-4}
}

Plain Text

S. Aggarwal, N. Kumar and S. Tanwar, "Blockchain-Envisioned UAV Communication Using 6G Networks: Open Issues, Use Cases, and Future Directions," in IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5416-5441, 1 April1, 2021, doi: 10.1109/JIOT.2020.3020819.

BibText

@ARTICLE{9184022,  author={Aggarwal, Shubhani and Kumar, Neeraj and Tanwar, Sudeep},  journal={IEEE Internet of Things Journal},   title={Blockchain-Envisioned UAV Communication Using 6G Networks: Open Issues, Use Cases, and Future Directions},   year={2021},  volume={8},  number={7},  pages={5416-5441},  doi={10.1109/JIOT.2020.3020819}}

Plain Text

A. Kumari, S. Tanwar, S. Tyagi and N. Kumar, "Blockchain-Based Massive Data Dissemination Handling in IIoT Environment," in IEEE Network, vol. 35, no. 1, pp. 318-325, January/February 2021, doi: 10.1109/MNET.011.2000355.

BibText

@ARTICLE{9165552,  author={Kumari, Aparna and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},  journal={IEEE Network},   title={Blockchain-Based Massive Data Dissemination Handling in IIoT Environment},   year={2021},  volume={35},  number={1},  pages={318-325},  doi={10.1109/MNET.011.2000355}}

Plain Text

Verma, Jaiprakash & Tanwar, Sudeep & Garg, Sanjay & Rathod, Abhay. (2021). Crowdsourced Social Media Reaction Analysis for Recommendation. International Journal of Knowledge and Systems Science. 12. 1-19. 10.4018/IJKSS.2021010101.

BibText

@article{article,
author = {Verma, Jaiprakash and Tanwar, Sudeep and Garg, Sanjay and Rathod, Abhay},
year = {2021},
month = {01},
pages = {1-19},
title = {Crowdsourced Social Media Reaction Analysis for Recommendation},
volume = {12},
journal = {International Journal of Knowledge and Systems Science},
doi = {10.4018/IJKSS.2021010101}
}

Plain Text

Gupta, Rajesh & Nair, Anuja & Tanwar, Sudeep & Kumar, Neeraj. (2020). Blockchain-assisted Secure UAV Communication in 6G Environment: Architecture, Opportunities, and Challenges. IET Communications. 10.1049/cmu2.12113.

BibText

@article{https://doi.org/10.1049/cmu2.12113,
author = {Gupta, Rajesh and Nair, Anuja and Tanwar, Sudeep and Kumar, Neeraj},
title = {Blockchain-assisted secure UAV communication in 6G environment: Architecture, opportunities, and challenges},
journal = {IET Communications},
volume = {},
number = {},
pages = {},
doi = {https://doi.org/10.1049/cmu2.12113},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cmu2.12113},
eprint = {https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/cmu2.12113}
}

Plain Text

Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar,Blockchain and 5G integrated softwarized UAV network management: Architecture, solutions, and challenges, Physical Communication, 2021, 101355, ISSN 1874-4907, https://doi.org/10.1016/j.phycom.2021.101355.

BibText

@article{GUPTA2021101355,
title = {Blockchain and 5G integrated softwarized UAV network management: Architecture, solutions, and challenges},
journal = {Physical Communication},
pages = {101355},
year = {2021},
issn = {1874-4907},
doi = {https://doi.org/10.1016/j.phycom.2021.101355},
url = {https://www.sciencedirect.com/science/article/pii/S1874490721000926},
author = {Rajesh Gupta and Sudeep Tanwar and Neeraj Kumar},
}

Plain Text

I. Budhiraja et al., "A Systematic Review on NOMA Variants for 5G and Beyond," in IEEE Access, doi: 10.1109/ACCESS.2021.3081601.

BibText

@ARTICLE{9433543,
  author={Budhiraja, Ishan and Kumar, Neeraj and Tyagi, Sudhanshu and Tanwar, Sudeep and Han, Zhu and Suh, Doug Young and Piran, MD. Jalil},
  journal={IEEE Access}, 
  title={A Systematic Review on NOMA Variants for 5G and Beyond}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2021.3081601}}

Plain Text

Vyas, T., Yadav, R., Solanki, C., Darji, R., Desai, S., & Tanwar, S. (2021). Deep learning-based scheme to diagnose Parkinson's disease. Expert Systems, e12739. https://doi.org/10.1111/exsy.12739

BibText

@article{https://doi.org/10.1111/exsy.12739,
author = {Vyas, Tarjni and Yadav, Raj and Solanki, Chitra and Darji, Rutvi and Desai, Shivani and Tanwar, Sudeep},
title = {Deep learning-based scheme to diagnose Parkinson's disease},
journal = {Expert Systems},
volume = {n/a},
number = {n/a},
pages = {e12739},
keywords = {3D CNN, binary classification, deep learning, feature extraction, histogram matching, MRI, N4 bias field correction, Parkinson's, PPMI},
doi = {https://doi.org/10.1111/exsy.12739}}

Plain Text

I. Budhiraja, N. Kumar, S. Tyagi and S. Tanwar, "Energy Consumption Minimization Scheme for NOMA-Based Mobile Edge Computation Networks Underlaying UAV" in IEEE Systems Journal, doi: 10.1109/JSYST.2021.3076782.

BibText

@ARTICLE{9442899,
  author={Budhiraja, Ishan and Kumar, Neeraj and Tyagi, Sudhanshu and Tanwar, Sudeep},
  journal={IEEE Systems Journal}, 
  title={Energy Consumption Minimization Scheme for NOMA-Based Mobile Edge Computation Networks Underlaying UAV}, 
  year={2021},
  volume={},
  number={},
  pages={1-10},
  doi={10.1109/JSYST.2021.3076782}}

Plain Text

Dixit, P., Bhattacharya, P., Tanwar, S., & Gupta, R. (2021). Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey. Expert Systems, 1– 30. https://doi.org/10.1111/exsy.12754

BibText

@article{https://doi.org/10.1111/exsy.12754,
author = {Dixit, Palak and Bhattacharya, Pronaya and Tanwar, Sudeep and Gupta, Rajesh},
title = {Anomaly detection in autonomous electric vehicles using AI techniques: A comprehensive survey},
journal = {Expert Systems},
volume = {n/a},
number = {n/a},
pages = {},
keywords = {anomalous classes, anomaly detection, artificial intelligence, autonomous electric vehicles, deep learning, machine learning, reinforcement learning},
doi = {https://doi.org/10.1111/exsy.12754},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.12754},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12754},}

Plain Text

A. Kumari and S. Tanwar, "A Reinforcement Learning-based Secure Demand Response Scheme for Smart Grid System," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3090305.

BibText

@ARTICLE{9459149,
  author={Kumari, Aparna and Tanwar, Sudeep},
  journal={IEEE Internet of Things Journal}, 
  title={A Reinforcement Learning-based Secure Demand Response Scheme for Smart Grid System}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JIOT.2021.3090305}}

Plain Text

Aman Agarwal, Mishra, A., Basavarajaiah, M. et al. Dilated Volumetric Network: an Enhanced Fully Convolutional Network for Volumetric Prostate Segmentation from Magnetic Resonance Imaging. Pattern Recognit. Image Anal. 31, 228–239 (2021). https://doi.org/10.1134/S1054661821020024

BibText

Aman Agarwal, Mishra, A., Basavarajaiah, M. et al. Dilated Volumetric Network: an Enhanced Fully Convolutional Network for Volumetric Prostate Segmentation from Magnetic Resonance Imaging. Pattern Recognit. Image Anal. 31, 228–239 (2021). https://doi.org/10.1134/S1054661821020024

Plain Text

K. Dev, S. A. Khowajay, P. K. Sharma, B. S. Chowdhry, S. Tanwar and G. Fortino, "DDI: A Novel Architecture for Joint Active user Detection and IoT Device Identification in Grant-Free NOMA Systems for 6G and Beyond Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3095255.

BibText

@ARTICLE{9475985,
  author={Dev, Kapal and Khowajay, Sunder Ali and Sharma, Prabhat Kumar and Chowdhry, Bhawani Shankar and Tanwar, Sudeep and Fortino, Giancarlo},
  journal={IEEE Internet of Things Journal}, 
  title={DDI: A Novel Architecture for Joint Active user Detection and IoT Device Identification in Grant-Free NOMA Systems for 6G and Beyond Networks}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JIOT.2021.3095255}}

Plain Text

Bhattacharya, P., Tanwar, S., Bodkhe, U. et al. EVBlocks: A Blockchain-Based Secure Energy Trading Scheme for Electric Vehicles underlying 5G-V2X Ecosystems. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08732-5

BibText

Bhattacharya, P., Tanwar, S., Bodkhe, U. et al. EVBlocks: A Blockchain-Based Secure Energy Trading Scheme for Electric Vehicles underlying 5G-V2X Ecosystems. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08732-5

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Shah, H., Shah, S., Tanwar, S. et al. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. Multimedia Systems (2021). https://doi.org/10.1007/s00530-021-00818-1

BibText

Shah, H., Shah, S., Tanwar, S. et al. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. Multimedia Systems (2021). https://doi.org/10.1007/s00530-021-00818-1

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U. Singh, A. Dua, S. Tanwar, N. Kumar and M. Alazab, "A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks," in IEEE Access, doi: 10.1109/ACCESS.2021.3100541.

BibText

@ARTICLE{9497084,
  author={Singh, Upendra and Dua, Amit and Tanwar, Sudeep and Kumar, Neeraj and Alazab, Mamoun},
  journal={IEEE Access}, 
  title={A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2021.3100541}}

Plain Text

I. Budhiraja, N. Kumar, S. Tyagi, S. Tanwar and Z. Han, "An Energy Efficient Scheme for WPCN-NOMA based Device-to-Device Communication," in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2021.3099527.

BibText

@ARTICLE{9496104,
  author={Budhiraja, Ishan and Kumar, Neeraj and Tyagi, Sudhanhsu and Tanwar, Sudeep and Han, Zhu},
  journal={IEEE Transactions on Vehicular Technology}, 
  title={An Energy Efficient Scheme for WPCN-NOMA based Device-to-Device Communication}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TVT.2021.3099527}}

Plain Text

Shah, H., Shah, M., Tanwar, S. et al. Blockchain for COVID-19: a comprehensive review. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01610-8

BibText

Shah, H., Shah, M., Tanwar, S. et al. Blockchain for COVID-19: a comprehensive review. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01610-8

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A. Verma, P. Bhattacharya, M. Zuhair, S. Tanwar and N. Kumar, "VaCoChain: Blockchain-based 5G-assisted UAV Vaccine distribution scheme for future pandemics," in IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2021.3103404.

BibText

@ARTICLE{9513603,
  author={Verma, Ashwin and Bhattacharya, Pronaya and Zuhair, Mohammad and Tanwar, Sudeep and Kumar, Neeraj},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={VaCoChain: Blockchain-based 5G-assisted UAV Vaccine distribution scheme for future pandemics}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JBHI.2021.3103404}}

Plain Text

Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, B-IoMV: Blockchain-based onion routing protocol for D2D communication in an IoMV environment beyond 5G, Vehicular Communications, 2021, 100401, ISSN 2214-2096, https://doi.org/10.1016/j.vehcom.2021.100401. (https://www.sciencedirect.com/science/article/pii/S221420962100070X)

BibText

@article{GUPTA2021100401,
title = {B-IoMV: Blockchain-based onion routing protocol for D2D communication in an IoMV environment beyond 5G},
journal = {Vehicular Communications},
pages = {100401},
year = {2021},
issn = {2214-2096},
doi = {https://doi.org/10.1016/j.vehcom.2021.100401},
url = {https://www.sciencedirect.com/science/article/pii/S221420962100070X},
author = {Rajesh Gupta and Sudeep Tanwar and Neeraj Kumar},
keywords = {Internet of military vehicles, Beyond 5G, Blockchain, Onion routing, Smart contract, Security, Privacy},
abstract = {With the popularity of blockchain technology in terms of security, privacy, traceability, and trust, it is being applied in various major applications concerning connected autonomous vehicles. One of the most sensitive applications is military operations, where we do not compromise with either security, privacy, trust, and communication latency of connected military vehicles, i.e., Internet of military vehicles (IoMVs). Achieving anonymity along with security and privacy of sender, receiver, and a data path for IoMVS is still an open question. Efforts we have made to address the aforementioned issue by proposing a blockchain-based onion routing protocol for IoMVs, i.e., B-IoMV, to achieve secure, trusted, and anonymous D2D communication. We studied the working of onion routing and presented a blockchain and token-based solution to strengthen its security and anonymity of IoMVs. A blockchain-based solution is quite costly and we use InterPlanetary File System (IPFS) to make the proposed B-IoMV system cost-effective. Finally, results show that the proposed B-IoMV system achieved better performance in communication latency, data storage cost, and network bandwidth utilization.}
}

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R. Gupta, M. M. Patel, S. Tanwar, N. Kumar and S. Zeadally, "Blockchain-based Data Dissemination Scheme for 5G-enabled Softwarized UAV Networks," in IEEE Transactions on Green Communications and Networking, doi: 10.1109/TGCN.2021.3111529.

BibText

@ARTICLE{9531961,
  author={Gupta, Rajesh and Patel, Mohil Maheshkumar and Tanwar, Sudeep and Kumar, Neeraj and Zeadally, Sherali},
  journal={IEEE Transactions on Green Communications and Networking}, 
  title={Blockchain-based Data Dissemination Scheme for 5G-enabled Softwarized UAV Networks}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TGCN.2021.3111529}}

Plain Text

Kumari, A., Tanwar, S. Multiagent-based secure energy management for multimedia grid communication using Q-learning. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-021-11491-x

BibText

Cite this article
Kumari, A., Tanwar, S. Multiagent-based secure energy management for multimedia grid communication using Q-learning. Multimed Tools Appl (2021). https://doi.org/10.1007/s11042-021-11491-x

Plain Text

S. Tanwar, R. Gupta, M. M. Patel, A. Shukla, G. Sharma and I. E. Davidson, "Blockchain and AI-empowered Social Distancing Scheme to Combat COVID-19 Situations," in IEEE Access, doi: 10.1109/ACCESS.2021.3114098.

BibText

@ARTICLE{9541193,
  author={Tanwar, Sudeep and Gupta, Rajesh and Patel, Mohil Maheshkumar and Shukla, Arpit and Sharma, Gulshan and Davidson, I. E.},
  journal={IEEE Access}, 
  title={Blockchain and AI-empowered Social Distancing Scheme to Combat COVID-19 Situations}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2021.3114098}}

Plain Text

Ashwin Verma, Pronaya Bhattacharya, Deepti Saraswat, Sudeep Tanwar, NyaYa: Blockchain-based electronic law record management scheme for judicial investigations, Journal of Information Security and Applications, Volume 63, 2021, 103025, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2021.103025.

BibText

@article{VERMA2021103025,
title = {NyaYa: Blockchain-based electronic law record management scheme for judicial investigations},
journal = {Journal of Information Security and Applications},
volume = {63},
pages = {103025},
year = {2021},
issn = {2214-2126},
doi = {https://doi.org/10.1016/j.jisa.2021.103025},
url = {https://www.sciencedirect.com/science/article/pii/S2214212621001873},
author = {Ashwin Verma and Pronaya Bhattacharya and Deepti Saraswat and Sudeep Tanwar},}

Plain Text

R. Gupta, D. Reebadiya, S. Tanwar, N. Kumar and M. Guizani, "When Blockchain Meets Edge Intelligence: Trusted and Security Solutions for Consumers," in IEEE Network, doi: 10.1109/MNET.001.2000735.

BibText

@ARTICLE{9579400,
  author={Gupta, Rajesh and Reebadiya, Dakshita and Tanwar, Sudeep and Kumar, Neeraj and Guizani, Mohsen},
  journal={IEEE Network}, 
  title={When Blockchain Meets Edge Intelligence: Trusted and Security Solutions for Consumers}, 
  year={2021},
  volume={},
  number={},
  pages={1-7},
  doi={10.1109/MNET.001.2000735}}

Plain Text

S. Tanwar, N. P. Patel, S. N. Patel, J. R. Patel, G. Sharma and I. E. Davidson, "Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations," in IEEE Access, vol. 9, pp. 138633-138646, 2021, doi: 10.1109/ACCESS.2021.3117848.

BibText

@ARTICLE{9558869,
  author={Tanwar, Sudeep and Patel, Nisarg P. and Patel, Smit N. and Patel, Jil R. and Sharma, Gulshan and Davidson, Innocent E.},
  journal={IEEE Access}, 
  title={Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations}, 
  year={2021},
  volume={9},
  number={},
  pages={138633-138646},
  doi={10.1109/ACCESS.2021.3117848}}

Plain Text

S. Aggarwal, N. Kumar, S. Tanwar and M. Alazab, "A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions," in IEEE Access, vol. 9, pp. 116231-116253, 2021, doi: 10.1109/ACCESS.2021.3104354.

BibText

@ARTICLE{9512064,
  author={Aggarwal, Shubhani and Kumar, Neeraj and Tanwar, Sudeep and Alazab, Mamoun},
  journal={IEEE Access}, 
  title={A Survey on Energy Trading in the Smart Grid: Taxonomy, Research Challenges and Solutions}, 
  year={2021},
  volume={9},
  number={},
  pages={116231-116253},
  doi={10.1109/ACCESS.2021.3104354}}

Plain Text

Rajesh Gupta, Mohil Maheshkumar Patel, Arpit Shukla, Sudeep Tanwar, Deep learning-based malicious smart contract detection scheme for internet of things environment, Computers & Electrical Engineering, 2021, 107583, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2021.107583. (https://www.sciencedirect.com/science/article/pii/S004579062100519X) Abstract: Smart contracts are essential in maintaining the trust between the members of the blockchain. Its verification is of utmost importance, as it is unmodifiable once deployed. Moreover, a malicious user can deploy vulnerable smart contracts to breach the blockchain data. To restrict this, we propose a deep learning-based scheme to detect the vulnerabilities and rate them as safe/vulnerable based on probability value <0.5/≥0.5 respectively. An open Google BigQuery dataset with 7000 samples was used to train the classifier. We train artificial neural networks (ANN), long-short term memory (LSTM), and gated recurrent unit models (GRU) and compare their accuracy, precision, recall, and receiver operating characteristic (ROC) curve values. Results show the LSTM model outperforms ANN and GRU. Then, we simulate the LSTM to classify the smart contracts before their deployment in the blockchain. Also, the efficacy of the blockchain is justified with the proposed system’s data storage cost and scalability. Keywords: Deep learning; Smart contract; Blockchain; Internet of things; LSTM; GRU; ANN; Malicious

BibText

@article{GUPTA2021107583,
title = {Deep learning-based malicious smart contract detection scheme for internet of things environment},
journal = {Computers & Electrical Engineering},
pages = {107583},
year = {2021},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2021.107583},
url = {https://www.sciencedirect.com/science/article/pii/S004579062100519X},
author = {Rajesh Gupta and Mohil Maheshkumar Patel and Arpit Shukla and Sudeep Tanwar},
keywords = {Deep learning, Smart contract, Blockchain, Internet of things, LSTM, GRU, ANN, Malicious},
abstract = {Smart contracts are essential in maintaining the trust between the members of the blockchain. Its verification is of utmost importance, as it is unmodifiable once deployed. Moreover, a malicious user can deploy vulnerable smart contracts to breach the blockchain data. To restrict this, we propose a deep learning-based scheme to detect the vulnerabilities and rate them as safe/vulnerable based on probability value <0.5/≥0.5 respectively. An open Google BigQuery dataset with 7000 samples was used to train the classifier. We train artificial neural networks (ANN), long-short term memory (LSTM), and gated recurrent unit models (GRU) and compare their accuracy, precision, recall, and receiver operating characteristic (ROC) curve values. Results show the LSTM model outperforms ANN and GRU. Then, we simulate the LSTM to classify the smart contracts before their deployment in the blockchain. Also, the efficacy of the blockchain is justified with the proposed system’s data storage cost and scalability.}

Plain Text

Patel, K., Mistry, C., Mehta, D. et al. A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif Intell Rev (2021). https://doi.org/10.1007/s10462-021-10084-2

BibText

Patel, K., Mistry, C., Mehta, D. et al. A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif Intell Rev (2021). https://doi.org/10.1007/s10462-021-10084-2

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P. Bhattacharya, S. B. Patel, R. Gupta, S. Tanwar and J. J. P. C. Rodrigues, "SaTYa: Trusted Bi-LSTM-Based Fake News Classification Scheme for Smart Community," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2021.3131945.

BibText

@ARTICLE{9646438,
  author={Bhattacharya, Pronaya and Patel, Shivani Bharatbhai and Gupta, Rajesh and Tanwar, Sudeep and Rodrigues, Joel J. P. C.},
  journal={IEEE Transactions on Computational Social Systems}, 
  title={SaTYa: Trusted Bi-LSTM-Based Fake News Classification Scheme for Smart Community}, 
  year={2021},
  volume={},
  number={},
  pages={1-10},
  doi={10.1109/TCSS.2021.3131945}}

Plain Text

R. Kakkar, R. Gupta, S. Tanwar and J. J. P. C. Rodrigues, "Coalition Game and Blockchain-Based Optimal Data Pricing Scheme for Ride Sharing Beyond 5G," in IEEE Systems Journal, doi: 10.1109/JSYST.2021.3126620.

BibText

@ARTICLE{9631875,
  author={Kakkar, Riya and Gupta, Rajesh and Tanwar, Sudeep and Rodrigues, Joel J. P. C.},
  journal={IEEE Systems Journal}, 
  title={Coalition Game and Blockchain-Based Optimal Data Pricing Scheme for Ride Sharing Beyond 5G}, 
  year={2021},
  volume={},
  number={},
  pages={1-10},
  doi={10.1109/JSYST.2021.3126620}}

Plain Text

M. Sharma, J. Singh, A. Gupta, S. Tanwar, G. Sharma and I. E. Davidson, "Intercloud Resource Discovery Using Blockchain," in IEEE Access, vol. 9, pp. 161224-161247, 2021, doi: 10.1109/ACCESS.2021.3131515.

BibText

@ARTICLE{9628129,
  author={Sharma, Mekhla and Singh, Jaiteg and Gupta, Ankur and Tanwar, Sudeep and Sharma, Gulshan and Davidson, Innocent E.},
  journal={IEEE Access}, 
  title={Intercloud Resource Discovery Using Blockchain}, 
  year={2021},
  volume={9},
  number={},
  pages={161224-161247},
  doi={10.1109/ACCESS.2021.3131515}}

Plain Text

P. Bhattacharya et al., "Coalition of 6G and Blockchain in AR/VR Space: Challenges and Future Directions," in IEEE Access, doi: 10.1109/ACCESS.2021.3136860.

BibText

@ARTICLE{9656726,
  author={Bhattacharya, Pronaya and Saraswat, Deepti and Dave, Amit and Acharya, Mohak and Tanwar, Sudeep and Sharma, Gulshan and Davison, I. E.},
  journal={IEEE Access}, 
  title={Coalition of 6G and Blockchain in AR/VR Space: Challenges and Future Directions}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/ACCESS.2021.3136860}}

Plain Text

Kapoor, A.; Gupta, A.; Gupta, R.; Tanwar, S.; Sharma, G.; Davidson, I.E. Ransomware Detection, Avoidance, and Mitigation Scheme: A Review and Future Directions. Sustainability 2022, 14, 8. https://doi.org/10.3390/su14010008

BibText

Kapoor, Adhirath, Ankur Gupta, Rajesh Gupta, Sudeep Tanwar, Gulshan Sharma, and Innocent E. Davidson. 2022. "Ransomware Detection, Avoidance, and Mitigation Scheme: A Review and Future Directions" Sustainability 14, no. 1: 8. https://doi.org/10.3390/su14010008

Plain Text

Talati, Shreya & Vekaria, Darshan & Kumari, Aparna & Tanwar, Sudeep. (2020). An AI-driven Object Segmentation and Speed Control Scheme for Autonomous Moving Platforms. Computer Networks. 186. 10.1016/j.comnet.2020.107783.

BibText

@article{article,
author = {Talati, Shreya and Vekaria, Darshan and Kumari, Aparna and Tanwar, Sudeep},
year = {2020},
month = {12},
pages = {},
title = {An AI-driven Object Segmentation and Speed Control Scheme for Autonomous Moving Platforms},
volume = {186},
journal = {Computer Networks},
doi = {10.1016/j.comnet.2020.107783}
}

Plain Text

D. Vekaria, A. Kumari, S. Tanwar and N. Kumar, "?boost: An AI-based Data Analytics Scheme for COVID-19 Prediction and Economy Boosting," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2020.3047539.

BibText

@ARTICLE{9309032,
  author={Vekaria, Darshan and Kumari, Aparna and Tanwar, Sudeep and Kumar, Neeraj},
  journal={IEEE Internet of Things Journal}, 
  title={?boost: An AI-based Data Analytics Scheme for COVID-19 Prediction and Economy Boosting}, 
  year={2020},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JIOT.2020.3047539}}

Plain Text

Gupta, Rajesh & Kumari, Aparna & Tanwar, Sudeep. (2020). Fusion of Blockchain and AI for Secure Drone Networking underlying 5G Communications. Transactions on Emerging Telecommunications Technologies. 32. 10.1002/ett.4176.

BibText

@article{article,
author = {Gupta, Rajesh and Kumari, Aparna and Tanwar, Sudeep},
year = {2020},
month = {11},
pages = {},
title = {Fusion of Blockchain and AI for Secure Drone Networking underlying 5G Communications},
volume = {32},
journal = {Transactions on Emerging Telecommunications Technologies},
doi = {10.1002/ett.4176}
}

Plain Text

R. Gupta, A. Shukla and S. Tanwar, "BATS: A Blockchain and AI-empowered Drone-assisted Telesurgery System towards 6G," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2020.3043262.

BibText

@ARTICLE{9286685,  author={Gupta, Rajesh and Shukla, Arpit and Tanwar, Sudeep},  journal={IEEE Transactions on Network Science and Engineering},   title={BATS: A Blockchain and AI-empowered Drone-assisted Telesurgery System towards 6G},   year={2020},  volume={},  number={},  pages={1-1},  doi={10.1109/TNSE.2020.3043262}}

Plain Text

U. Thakker, R. Patel, S. Tanwar, N. Kumar and H. Song, "Blockchain for Diamond Industry: Opportunities and Challenges," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2020.3047550.

BibText

@ARTICLE{9308965,  author={Thakker, Urvish and Patel, Ruhi and Tanwar, Sudeep and Kumar, Neeraj and Song, Houbing},  journal={IEEE Internet of Things Journal},   title={Blockchain for Diamond Industry: Opportunities and Challenges},   year={2020},  volume={},  number={},  pages={1-1},  doi={10.1109/JIOT.2020.3047550}}

Plain Text

Aparna Kumari, Sudeep Tanwar, Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions, Sustainable Computing: Informatics and Systems, Volume 28, 2020, 100427, ISSN 2210-5379, https://doi.org/10.1016/j.suscom.2020.100427.

BibText

@article{KUMARI2020100427,
title = {Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions},
journal = {Sustainable Computing: Informatics and Systems},
volume = {28},
pages = {100427},
year = {2020},
issn = {2210-5379},
doi = {https://doi.org/10.1016/j.suscom.2020.100427},
url = {https://www.sciencedirect.com/science/article/pii/S2210537920301542},
author = {Aparna Kumari and Sudeep Tanwar},
keywords = {Big data, Secure data analytics, Sustainable smart city, Load forecasting, Smart meter, Data security and privacy, Machine learning},
abstract = {A smart city requires an intelligent infrastructure to improve the quality of life with sustainable environment for its citizens. There is an exponential demand for efficient, secure, reliable, and uninterrupted electricity supply, so there is a need for an intelligent grid, which uses Information and Communications Technology (ICT) to optimize the generation, circulation, and ingestion of electricity. Thus, Smart Grid (SG) acts as an intelligent grid, which plays an important role in the overall growth of any smart city. Further, the Big Data (BD) generated from SG, provides noteworthy information that could significantly benefit different applications of SG, such as demand response and load profiling. However, an insecure technique for decision-making may lead to the breach of SG data where hackers gained full access to consumer data. On the contrary, a secure technique for decision-making can provide satisfaction to all the stakeholders, including consumers and utility providers. Motivated from these facts, this paper presents a comprehensive literature survey and analysis of state-of-the-art proposals for Secure Data Analytics (SDA) in the SG system. However, to achieve SDA for the SG systems is one of the critical tasks. The existing research and development endeavors not fully exploited the SDA in the SG system. In this paper, we discuss the distinctive nature of SDA and its complexity over the SG data. A detailed taxonomy abstracted into a novel process model, which highlights various research challenges such as secure data collection and preprocessing, secure load data processing and storage, load prediction, load management and analysis, data security and privacy issues, and data communication. Finally, a case study is presented to demonstrate the process model.}
}

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A. Shukla, P. Bhattacharya, S. Tanwar, N. Kumar and M. Guizani, "DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems," in IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12510-12520, Nov. 2020, doi: 10.1109/TVT.2020.3022168.

BibText

@ARTICLE{9187395,  author={Shukla, Arpit and Bhattacharya, Pronaya and Tanwar, Sudeep and Kumar, Neeraj and Guizani, Mohsen},  journal={IEEE Transactions on Vehicular Technology},   title={DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems},   year={2020},  volume={69},  number={11},  pages={12510-12520},  doi={10.1109/TVT.2020.3022168}}

Plain Text

Patel, Ujash & Tanwar, Sudeep & Nair, Anuja. (2020). Performance Analysis of Video On-demand and Live Video Streaming using Cloud based Services. Scalable Computing. 10.12694/scpe.v21i3.1764.

BibText

@article{article,
author = {Patel, Ujash and Tanwar, Sudeep and Nair, Anuja},
year = {2020},
month = {07},
pages = {},
title = {Performance Analysis of Video On-demand and Live Video Streaming using Cloud based Services},
journal = {Scalable Computing},
doi = {10.12694/scpe.v21i3.1764}
}

Plain Text

Karan Sheth, Keyur Patel, Het Shah, Sudeep Tanwar, Rajesh Gupta, Neeraj Kumar, A taxonomy of AI techniques for 6G communication networks, Computer Communications, Volume 161, 2020, Pages 279-303, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2020.07.035.

BibText

@article{SHETH2020279,
title = {A taxonomy of AI techniques for 6G communication networks},
journal = {Computer Communications},
volume = {161},
pages = {279-303},
year = {2020},
issn = {0140-3664},
doi = {https://doi.org/10.1016/j.comcom.2020.07.035},
url = {https://www.sciencedirect.com/science/article/pii/S0140366420318478},
author = {Karan Sheth and Keyur Patel and Het Shah and Sudeep Tanwar and Rajesh Gupta and Neeraj Kumar},
keywords = {Artificial Intelligence, 6G, Communication networks, Mobile edge computing, Intelligent transportation system},
abstract = {With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by 2030, many institutes worldwide have started to explore various issues and challenges in 6G communication networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many applications currently not viable by today’s 4G and 5G communication standards. The current 5G technology has security and privacy issues which makes its usage in limited applications. In such an environment, we believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications. In this article, we explore how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption of AI techniques in intelligent transport system.}
}

Plain Text

Aparna Kumari, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, A taxonomy of blockchain-enabled softwarization for secure UAV network, Computer Communications, Volume 161, 2020, Pages 304-323, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2020.07.042

BibText

@article{KUMARI2020304,
title = {A taxonomy of blockchain-enabled softwarization for secure UAV network},
journal = {Computer Communications},
volume = {161},
pages = {304-323},
year = {2020},
issn = {0140-3664},
doi = {https://doi.org/10.1016/j.comcom.2020.07.042},
url = {https://www.sciencedirect.com/science/article/pii/S0140366420318545},
author = {Aparna Kumari and Rajesh Gupta and Sudeep Tanwar and Neeraj Kumar},
keywords = {Unmanned aerial vehicle, Softwarization, 5G, Software defined networking, Network function virtualization, Blockchain, Smart contract, Security},
abstract = {The recent advancements in unmanned aerial vehicles (UAVs) upsurges its usages in commercial and civilian applications such as surveillance, rescue, and crowdsensing. UAVs are vulnerable to being destroyed, lost, or stolen in case of security breaches of its network. The network management of UAVs is a crucial task due to its high mobility, which necessitates UAV network softwarization. Then, it becomes indispensable that allows the separation of control functions (i.e., control plane data) from hardware for smooth execution of complex operations. Further, UAV uses the Internet (an open channel) for communication in its complex system that raises a network security concern. The well-known softwarization techniques, i.e., software-defined networking (SDN) and network function virtualization (NFV) can be used to accomplish the secure network services on less expense. Conversely, these softwarization techniques may suffer from various threats like access control, user authentication, controller hijacking, and many more attack. The existing solutions are using a centralized system, which is having a single point of failure issue and vulnerable to security threats. Motivated from these facts, we present a comprehensive and systematic survey on the blockchain-based softwarization for a secure UAV network. Then, we propose a blockchain-enabled UAV softwarization architecture for secure communication and network management. It provides dynamic, flexible, and on-the-fly decision capabilities for communication services over the UAV network. Eventually, we analyzed the open research issues and challenges for future research directions in this emerging area.}
}

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Bodkhe, Umesh & Tanwar, Sudeep. (2020). Taxonomy of Secure Data Dissemination Techniques for IoT Environment. IET Software. 10.1049/iet-sen.2020.0006.

BibText

@article{article,
author = {Bodkhe, Umesh and Tanwar, Sudeep},
year = {2020},
month = {07},
pages = {},
title = {Taxonomy of Secure Data Dissemination Techniques for IoT Environment},
journal = {IET Software},
doi = {10.1049/iet-sen.2020.0006}
}

Plain Text

Gupta, Rajesh & Kumari, Aparna & Tanwar, Sudeep & Kumar, Neeraj. (2020). Blockchain-envisioned Softwarized Multi-Swarming UAVs to Tackle COVID-19 Situations. IEEE Network. PP. 10.1109/MNET.011.2000439.

BibText

@article{article,
author = {Gupta, Rajesh and Kumari, Aparna and Tanwar, Sudeep and Kumar, Neeraj},
year = {2020},
month = {06},
pages = {},
title = {Blockchain-envisioned Softwarized Multi-Swarming UAVs to Tackle COVID-19 Situations},
volume = {PP},
journal = {IEEE Network},
doi = {10.1109/MNET.011.2000439}
}

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Tanwar, Sudeep & Kaneriya, Shriya & Kumar, Neeraj & Zeadally, Sherali. (2020). ElectroBlocks: A Blockchain-based Energy Trading Scheme for Smart Grid Systems. International Journal of Communication Systems. 10.1002/dac.4547.

BibText

@article{article,
author = {Tanwar, Sudeep and Kaneriya, Shriya and Kumar, Neeraj and Zeadally, Sherali},
year = {2020},
month = {06},
pages = {},
title = {ElectroBlocks: A Blockchain-based Energy Trading Scheme for Smart Grid Systems},
journal = {International Journal of Communication Systems},
doi = {10.1002/dac.4547}
}

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S. B. Patel, P. Bhattacharya, S. Tanwar and N. Kumar, "KiRTi: A Blockchain-based Credit Recommender System for Financial Institutions," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2020.3005678.

BibText

@ARTICLE{9128017,  author={Patel, Shivani Bharatbhai and Bhattacharya, Pronaya and Tanwar, Sudeep and Kumar, Neeraj},  journal={IEEE Transactions on Network Science and Engineering},   title={KiRTi: A Blockchain-based Credit Recommender System for Financial Institutions},   year={2020},  volume={},  number={},  pages={1-1},  doi={10.1109/TNSE.2020.3005678}}

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Mohil Maheshkumar Patel, Sudeep Tanwar, Rajesh Gupta, Neeraj Kumar, A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions, Journal of Information Security and Applications, Volume 55, 2020, 102583, ISSN 2214-2126, https://doi.org/10.1016/j.jisa.2020.102583

BibText

@article{PATEL2020102583,
title = {A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions},
journal = {Journal of Information Security and Applications},
volume = {55},
pages = {102583},
year = {2020},
issn = {2214-2126},
doi = {https://doi.org/10.1016/j.jisa.2020.102583},
url = {https://www.sciencedirect.com/science/article/pii/S2214212620307535},
author = {Mohil Maheshkumar Patel and Sudeep Tanwar and Rajesh Gupta and Neeraj Kumar},
keywords = {Cryptocurrency, Price prediction, Litecoin, Monero, Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Time Series},
abstract = {A cryptocurrency is a network-based digital exchange medium, where the records are secured using strong cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). It uses blockchain technology to make the transactions secure, transparent, traceable, and immutable. Due to these properties, the cryptocurrencies have gained popularity in almost all the sectors especially in financial sectors. Though, cryptocurrencies are getting recognition form the approval bodies, but still, the uncertainty and dynamism in their prices risk the investments substantially. Cryptocurrency price prediction has become a trending research topic globally. Many machine learning and deep learning algorithms such as Gated Recurrent Unit (GRU), Neural Networks (NN), and Long short-term memory (LSTM) have been used by the researchers to predict and analyze the factors affecting the cryptocurrency prices. In this paper, a LSTM and GRU-based hybrid cryptocurrency prediction scheme is proposed, which focuses on only two cryptocurrencies, namely Litecoin and Monero. The results depict that the proposed scheme accurately predicts the prices with high accuracy, revealing that the scheme can be applicable in various cryptocurrencies price predictions.}
}

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I. Budhiraja, N. Kumar, S. Tyagi, S. Tanwar and M. S. Obaidat, "URJA: Usage Jammer as a Resource Allocation for Secure Transmission in a CR-NOMA-Based 5G Femtocell System," in IEEE Systems Journal, doi: 10.1109/JSYST.2020.2999474.

BibText

@ARTICLE{9119092,  author={Budhiraja, Ishan and Kumar, Neeraj and Tyagi, Sudhanshu and Tanwar, Sudeep and Obaidat, Mohammad S.},  journal={IEEE Systems Journal},   title={URJA: Usage Jammer as a Resource Allocation for Secure Transmission in a CR-NOMA-Based 5G Femtocell System},   year={2020},  volume={},  number={},  pages={1-10},  doi={10.1109/JSYST.2020.2999474}}

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Gupta, Rajesh & Kumari, Aparna & Tanwar, Sudeep. (2020). A Taxonomy of Blockchain Envisioned Edge-as-a-Connected Autonomous Vehicles. Transactions on Emerging Telecommunications Technologies. 10.1002/ett.4009.

BibText

@article{article,
author = {Gupta, Rajesh and Kumari, Aparna and Tanwar, Sudeep},
year = {2020},
month = {05},
pages = {},
title = {A Taxonomy of Blockchain Envisioned Edge-as-a-Connected Autonomous Vehicles},
journal = {Transactions on Emerging Telecommunications Technologies},
doi = {10.1002/ett.4009}
}

Plain Text

Bodkhe, Umesh & Tanwar, Sudeep & Bhattacharya, Pronaya & Kumar, Neeraj. (2020). Blockchain for Precision Irrigation: Opportunities and Challenges. Transactions on Emerging Telecommunications Technologies. 10.1002/ett.4059.

BibText

@article{article,
author = {Bodkhe, Umesh and Tanwar, Sudeep and Bhattacharya, Pronaya and Kumar, Neeraj},
year = {2020},
month = {06},
pages = {},
title = {Blockchain for Precision Irrigation: Opportunities and Challenges},
journal = {Transactions on Emerging Telecommunications Technologies},
doi = {10.1002/ett.4059}
}

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K. Patel et al., "Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges," in IEEE Access, vol. 8, pp. 90495-90519, 2020, doi: 10.1109/ACCESS.2020.2993803.

BibText

@ARTICLE{9091188,  author={Patel, Keyur and Mehta, Dev and Mistry, Chinmay and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj and Alazab, Mamoun},  journal={IEEE Access},   title={Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges},   year={2020},  volume={8},  number={},  pages={90495-90519},  doi={10.1109/ACCESS.2020.2993803}}

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Kumari, Aparna & Gupta, Rajesh & Tanwar, Sudeep & Kumar, Neeraj. (2020). Blockchain and AI Amalgamation for Energy Cloud Management: Challenges, Solutions, and Future Directions. Journal of Parallel and Distributed Computing. 143. 10.1016/j.jpdc.2020.05.004.

BibText

@article{article,
author = {Kumari, Aparna and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj},
year = {2020},
month = {05},
pages = {},
title = {Blockchain and AI Amalgamation for Energy Cloud Management: Challenges, Solutions, and Future Directions},
volume = {143},
journal = {Journal of Parallel and Distributed Computing},
doi = {10.1016/j.jpdc.2020.05.004}
}

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P. Jay, V. Kalariya, P. Parmar, S. Tanwar, N. Kumar and M. Alazab, "Stochastic Neural Networks for Cryptocurrency Price Prediction," in IEEE Access, vol. 8, pp. 82804-82818, 2020, doi: 10.1109/ACCESS.2020.2990659.

BibText

@ARTICLE{9079491,  author={Jay, Patel and Kalariya, Vasu and Parmar, Pushpendra and Tanwar, Sudeep and Kumar, Neeraj and Alazab, Mamoun},  journal={IEEE Access},   title={Stochastic Neural Networks for Cryptocurrency Price Prediction},   year={2020},  volume={8},  number={},  pages={82804-82818},  doi={10.1109/ACCESS.2020.2990659}}

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Jain, Preksha & Gupta, Akhil & Tanwar, Sudeep & Kumar, Neeraj. (2020). Customized NOMA and Sector Model for Battery Efficient Beyond 5G Green Networks. IEEE Network. 10.1109/MNET.011.2000187.

BibText

@article{article,
author = {Jain, Preksha and Gupta, Akhil and Tanwar, Sudeep and Kumar, Neeraj},
year = {2020},
month = {05},
pages = {},
title = {Customized NOMA and Sector Model for Battery Efficient Beyond 5G Green Networks},
journal = {IEEE Network},
doi = {10.1109/MNET.011.2000187}
}

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Shaik, Vaseem Akram & Malik, Praveen & Singh, Rajesh & Gehlot, Anita & Tanwar, Sudeep. (2020). Adoption of blockchain technology in various realms: Opportunities and challenges. Security and Privacy. 3. e109. 10.1002/spy2.109.

BibText

@article{article,
author = {Shaik, Vaseem Akram and Malik, Praveen and Singh, Rajesh and Gehlot, Anita and Tanwar, Sudeep},
year = {2020},
month = {04},
pages = {e109},
title = {Adoption of blockchain technology in various realms: Opportunities and challenges},
volume = {3},
journal = {Security and Privacy},
doi = {10.1002/spy2.109}
}

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Bakshi, Aditya, et al. "3T-FASDM: Linear discriminant analysis-based three-tier face anti-spoofing detection model using support vector machine." International Journal of Communication Systems 33.12 (2020): e4441

BibText

@article{https://doi.org/10.1002/dac.4441,
author = {Bakshi, Aditya and Gupta, Sunanda and Gupta, Akhil and Tanwar, Sudeep and Hsiao, Kuei-Fang},
title = {3T-FASDM: Linear discriminant analysis-based three-tier face anti-spoofing detection model using support vector machine},
journal = {International Journal of Communication Systems},
volume = {33},
number = {12},
pages = {e4441},
keywords = {face anti-spoofing, image quality assessment (IQA), linear discriminant analysis (LDA), support vector machine (SVM)},
doi = {https://doi.org/10.1002/dac.4441},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.4441},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.4441},
note = {e4441 IJCS-20-0308.R1},
year = {2020}
}

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U. Bodkhe et al., "Blockchain for Industry 4.0: A Comprehensive Review," in IEEE Access, vol. 8, pp. 79764-79800, 2020, doi: 10.1109/ACCESS.2020.2988579.

BibText

@ARTICLE{9069885,  author={Bodkhe, Umesh and Tanwar, Sudeep and Parekh, Karan and Khanpara, Pimal and Tyagi, Sudhanshu and Kumar, Neeraj and Alazab, Mamoun},  journal={IEEE Access},   title={Blockchain for Industry 4.0: A Comprehensive Review},   year={2020},  volume={8},  number={},  pages={79764-79800},  doi={10.1109/ACCESS.2020.2988579}}

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I. Budhiraja, N. Kumar, S. Tyagi, S. Tanwar and M. Guizani, "An Energy-Efficient Resource Allocation Scheme for SWIPT-NOMA Based Femtocells Users With Imperfect CSI," in IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7790-7805, July 2020, doi: 10.1109/TVT.2020.2991466.

BibText

@ARTICLE{9082809,  author={Budhiraja, Ishan and Kumar, Neeraj and Tyagi, Sudhanshu and Tanwar, Sudeep and Guizani, Mohsen},  journal={IEEE Transactions on Vehicular Technology},   title={An Energy-Efficient Resource Allocation Scheme for SWIPT-NOMA Based Femtocells Users With Imperfect CSI},   year={2020},  volume={69},  number={7},  pages={7790-7805},  doi={10.1109/TVT.2020.2991466}}

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Gomes, Joao & Rodrigues, Joel & Rabelo, Ricardo & Tanwar, Sudeep & Al?Muhtadi, Jalal & Kozlov, Sergey. (2020). A novel Internet of things-based plug-and-play multigas sensor for environmental monitoring. Transactions on Emerging Telecommunications Technologies. 10.1002/ett.3967.

BibText

@article{article,
author = {Gomes, Joao and Rodrigues, Joel and Rabelo, Ricardo and Tanwar, Sudeep and Al?Muhtadi, Jalal and Kozlov, Sergey},
year = {2020},
month = {04},
pages = {},
title = {A novel Internet of things-based plug-and-play multigas sensor for environmental monitoring},
journal = {Transactions on Emerging Telecommunications Technologies},
doi = {10.1002/ett.3967}
}

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U. Bodkhe, D. Mehta, S. Tanwar, P. Bhattacharya, P. K. Singh and W. Hong, "A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems," in IEEE Access, vol. 8, pp. 54371-54401, 2020, doi: 10.1109/ACCESS.2020.2981415.

BibText

@ARTICLE{9039576,  author={Bodkhe, Umesh and Mehta, Dhyey and Tanwar, Sudeep and Bhattacharya, Pronaya and Singh, Pradeep Kumar and Hong, Wei-Chiang},  journal={IEEE Access},   title={A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems},   year={2020},  volume={8},  number={},  pages={54371-54401},  doi={10.1109/ACCESS.2020.2981415}}

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A. Kumari, R. Gupta, S. Tanwar, S. Tyagi and N. Kumar, "When Blockchain Meets Smart Grid: Secure Energy Trading in Demand Response Management," in IEEE Network, vol. 34, no. 5, pp. 299-305, September/October 2020, doi: 10.1109/MNET.001.1900660.

BibText

@ARTICLE{9165556,  author={Kumari, Aparna and Gupta, Rajesh and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},  journal={IEEE Network},   title={When Blockchain Meets Smart Grid: Secure Energy Trading in Demand Response Management},   year={2020},  volume={34},  number={5},  pages={299-305},  doi={10.1109/MNET.001.1900660}}

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Vatsal Chauhan, Meetu Patel, Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar, IoT Enabled real-Time urban transport management system, Computers & Electrical Engineering, Volume 86, 2020, 106746, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2020.106746.

BibText

@article{CHAUHAN2020106746,
title = {IoT Enabled real-Time urban transport management system},
journal = {Computers & Electrical Engineering},
volume = {86},
pages = {106746},
year = {2020},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2020.106746},
url = {https://www.sciencedirect.com/science/article/pii/S0045790620306017},
author = {Vatsal Chauhan and Meetu Patel and Sudeep Tanwar and Sudhanshu Tyagi and Neeraj Kumar}
}

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Hathaliya, Jigna & Tanwar, Sudeep. (2020). An Exhaustive Survey on Security and Privacy Issues in Healthcare 4.0. Computer Communications. 153. 10.1016/j.comcom.2020.02.018.

BibText

@article{article,
author = {Hathaliya, Jigna and Tanwar, Sudeep},
year = {2020},
month = {02},
pages = {},
title = {An Exhaustive Survey on Security and Privacy Issues in Healthcare 4.0},
volume = {153},
journal = {Computer Communications},
doi = {10.1016/j.comcom.2020.02.018}
}

Plain Text

Gupta, Rajesh & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj. (2020). Machine Learning Models for Secure Data Analytics: A Taxonomy and Threat Model. Computer Communications. 153. 10.1016/j.comcom.2020.02.008.

BibText

@article{article,
author = {Gupta, Rajesh and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2020},
month = {02},
pages = {},
title = {Machine Learning Models for Secure Data Analytics: A Taxonomy and Threat Model},
volume = {153},
journal = {Computer Communications},
doi = {10.1016/j.comcom.2020.02.008}
}

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Bodkhe, Umesh & Tanwar, Sudeep. (2020). Secure Data Dissemination Techniques for IoT Applications: Research Challenges and Opportunities. Software Practice and Experience. 10.1002/spe.2811.

BibText

@article{article,
author = {Bodkhe, Umesh and Tanwar, Sudeep},
year = {2020},
month = {02},
pages = {},
title = {Secure Data Dissemination Techniques for IoT Applications: Research Challenges and Opportunities},
journal = {Software Practice and Experience},
doi = {10.1002/spe.2811}
}

Plain Text

Mehta, Parimal & Gupta, Rajesh & Tanwar, Sudeep. (2020). Blockchain envisioned UAV networks: Challenges, solutions, and comparisons. Computer Communications. 151. 10.1016/j.comcom.2020.01.023.

BibText

@article{article,
author = {Mehta, Parimal and Gupta, Rajesh and Tanwar, Sudeep},
year = {2020},
month = {02},
pages = {},
title = {Blockchain envisioned UAV networks: Challenges, solutions, and comparisons},
volume = {151},
journal = {Computer Communications},
doi = {10.1016/j.comcom.2020.01.023}
}

Plain Text

R. Gupta, S. Tanwar, F. Al-Turjman, P. Italiya, A. Nauman and S. W. Kim, "Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges," in IEEE Access, vol. 8, pp. 24746-24772, 2020, doi: 10.1109/ACCESS.2020.2970576.

BibText

@ARTICLE{8976143,  author={Gupta, Rajesh and Tanwar, Sudeep and Al-Turjman, Fadi and Italiya, Prit and Nauman, Ali and Kim, Sung Won},  journal={IEEE Access},   title={Smart Contract Privacy Protection Using AI in Cyber-Physical Systems: Tools, Techniques and Challenges},   year={2020},  volume={8},  number={},  pages={24746-24772},  doi={10.1109/ACCESS.2020.2970576}}

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S. Tanwar, Q. Bhatia, P. Patel, A. Kumari, P. K. Singh and W. Hong, "Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward," in IEEE Access, vol. 8, pp. 474-488, 2020, doi: 10.1109/ACCESS.2019.2961372.

BibText

@ARTICLE{8938741,  author={Tanwar, Sudeep and Bhatia, Qasim and Patel, Pruthvi and Kumari, Aparna and Singh, Pradeep Kumar and Hong, Wei-Chiang},  journal={IEEE Access},   title={Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward},   year={2020},  volume={8},  number={},  pages={474-488},  doi={10.1109/ACCESS.2019.2961372}}

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S. Tanwar et al., "Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol," in IEEE Consumer Electronics Magazine, vol. 9, no. 1, pp. 88-94, 1 Jan. 2020, doi: 10.1109/MCE.2019.2941456.

BibText

@ARTICLE{8922842,  author={Tanwar, Sudeep and Vora, Jayneel and Kaneriya, Shriya and Tyagi, Sudhanshu and Kumar, Neeraj and Sharma, Vishal and You, Ilsun},  journal={IEEE Consumer Electronics Magazine},   title={Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol},   year={2020},  volume={9},  number={1},  pages={88-94},  doi={10.1109/MCE.2019.2941456}}

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J. Bhatia, P. Kakadia, M. Bhavsar and S. Tanwar, "SDN-Enabled Network Coding-Based Secure Data Dissemination in VANET Environment," in IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6078-6087, July 2020, doi: 10.1109/JIOT.2019.2956964.

BibText

@ARTICLE{8918280,  author={Bhatia, Jitendra and Kakadia, Parth and Bhavsar, Madhuri and Tanwar, Sudeep},  journal={IEEE Internet of Things Journal},   title={SDN-Enabled Network Coding-Based Secure Data Dissemination in VANET Environment},   year={2020},  volume={7},  number={7},  pages={6078-6087},  doi={10.1109/JIOT.2019.2956964}}

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Tanwar, Sudeep & Parekh, Karan & Evans, Richard. (2020). Blockchain-based electronic healthcare record system for healthcare 4.0 applications. Journal of Information Security and Applications. 50. 102407. 10.1016/j.jisa.2019.102407.

BibText

@article{article,
author = {Tanwar, Sudeep and Parekh, Karan and Evans, Richard},
year = {2020},
month = {02},
pages = {102407},
title = {Blockchain-based electronic healthcare record system for healthcare 4.0 applications},
volume = {50},
journal = {Journal of Information Security and Applications},
doi = {10.1016/j.jisa.2019.102407}
}

Plain Text

Hathaliya, Jigna & Tanwar, Sudeep & Evans, Richard. (2020). Securing Electronic Healthcare Records: A Mobile-based Biometric Authentication Approach. Journal of Information Security and Applications. 1-13. 10.1016/j.jisa.2020.102528.

BibText

@article{article,
author = {Hathaliya, Jigna and Tanwar, Sudeep and Evans, Richard},
year = {2020},
month = {04},
pages = {1-13},
title = {Securing Electronic Healthcare Records: A Mobile-based Biometric Authentication Approach},
journal = {Journal of Information Security and Applications},
doi = {10.1016/j.jisa.2020.102528}
}

Plain Text

Mungra, Dhara & Agrawal, Anjali & Sharma, Priyanka & Tanwar, Sudeep. (2020). PRATIT: A CNN-Based Emotion Recognition System using Histogram Equalization and Data Augmentation. Multimedia Tools and Applications. 10.1007/s11042-019-08397-0.

BibText

@article{article,
author = {Mungra, Dhara and Agrawal, Anjali and Sharma, Priyanka and Tanwar, Sudeep},
year = {2020},
month = {01},
pages = {},
title = {PRATIT: A CNN-Based Emotion Recognition System using Histogram Equalization and Data Augmentation},
journal = {Multimedia Tools and Applications},
doi = {10.1007/s11042-019-08397-0}
}

Plain Text

Naman Kabra, Pronaya Bhattacharya, Sudeep Tanwar, Sudhanshu Tyagi, MudraChain: Blockchain-based framework for automated cheque clearance in financial institutions, Future Generation Computer Systems, Volume 102, 2020, Pages 574-587, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.08.035

BibText

@article{KABRA2020574,
title = {MudraChain: Blockchain-based framework for automated cheque clearance in financial institutions},
journal = {Future Generation Computer Systems},
volume = {102},
pages = {574-587},
year = {2020},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2019.08.035},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X19311896},
author = {Naman Kabra and Pronaya Bhattacharya and Sudeep Tanwar and Sudhanshu Tyagi},
keywords = {Blockchain, QR code, Two-factor authentication, Cheque clearance, Hyperledger fabric},
abstract = {Currently, the burden on the cheque clearing houses in financial institutions is increasing day-by-day, which necessitates the upgrading of the existing cheque truncation system (CTS). It is a manual process which uses Magnetic Ink Character Recognition (MICR), where cheques have been scanned and sent to the clearing house for further processing. The limitations of existing CTS are — illegal duplication of cheque images, invisible ink usage, visibility issues in beneficiary name, and amount on the cheque. To handle the aforementioned issues of the existing CTS, blockchain has emerged as a new technology which is a distributed ledger that is timestamped and immutable. Being immutable, forgeries related to images of cheques during clearance cycles are not allowed. This provides trust and consensus among all participating entities in the network. Motivated by the above discussion, in this paper, we propose a framework named MudraChain for automated cheque clearance, where clearance operations are handled by the blockchain network, instead of existing CTS. It includes: (i) A multi-level authentication scheme to make the blockchain-based framework secure and tamper-proof among participating financial stakeholders, (ii) A quick-response (QR) code generation algorithm which performs digital signing of a cheque, and (iii) A novel two-factor authentication protocol to generate a time based one-time password (TOTP) for secure funds transfer. The obtained results are examined against state-of-the-art approaches to indicate the supremacy of the proposed framework. Thus, MudraChain allows a seamless flow of clearance operation via blockchain for the payer and the payee without any intermediaries. Finally, it addresses the requirements of building a secure application for cheque clearance in view of decentralized blockchain 4.0 applications.}
}

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Bhattacharya, Pronaya & Tanwar, Sudeep & Bodkhe, Umesh & Tyagi, Sudhanshu & Kumar, Neeraj. (2019). BinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications. IEEE/ACM Transactions on Networking. PP. 1-1. 10.1109/TNSE.2019.2961932.

BibText

@article{article,
author = {Bhattacharya, Pronaya and Tanwar, Sudeep and Bodkhe, Umesh and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2019},
month = {12},
pages = {1-1},
title = {BinDaaS: Blockchain-Based Deep-Learning as-a-Service in Healthcare 4.0 Applications},
volume = {PP},
journal = {IEEE/ACM Transactions on Networking},
doi = {10.1109/TNSE.2019.2961932}
}

Plain Text

Singh, Rajeev & Tanwar, Sudeep & Sharma, Teek. (2019). Utilization of Blockchain for Mitigating the Distributed Denial of Service Attacks. Security and Privacy. 3. 10.1002/spy2.96.

BibText

@article{article,
author = {Singh, Rajeev and Tanwar, Sudeep and Sharma, Teek},
year = {2019},
month = {10},
pages = {},
title = {Utilization of Blockchain for Mitigating the Distributed Denial of Service Attacks},
volume = {3},
journal = {Security and Privacy},
doi = {10.1002/spy2.96}
}

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Gupta, Rajesh & Tanwar, Sudeep & Kumar, Neeraj & Tyagi, Sudhanshu. (2019). Blockchain-based Security Attack Resilience Schemes for Autonomous Vehicles in Industry 4.0: A Systematic Review. Computers & Electrical Engineering. 86. 10.1016/j.compeleceng.2020.106717.

BibText

@article{article,
author = {Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj and Tyagi, Sudhanshu},
year = {2019},
month = {09},
pages = {},
title = {Blockchain-based Security Attack Resilience Schemes for Autonomous Vehicles in Industry 4.0: A Systematic Review},
volume = {86},
journal = {Computers & Electrical Engineering},
doi = {10.1016/j.compeleceng.2020.106717}
}

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Bhatia, Jitendra & Dave, Ridham & Bhayani, Heta & Tanwar, Sudeep & Nayyar, Anand. (2019). SDN-based real-time urban traffic analysis in VANET environment. Computer Communications. 149. 162-175. 10.1016/j.comcom.2019.10.011.

BibText

@article{article,
author = {Bhatia, Jitendra and Dave, Ridham and Bhayani, Heta and Tanwar, Sudeep and Nayyar, Anand},
year = {2019},
month = {10},
pages = {162-175},
title = {SDN-based real-time urban traffic analysis in VANET environment},
volume = {149},
journal = {Computer Communications},
doi = {10.1016/j.comcom.2019.10.011}
}

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Mistry, Ishan & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj. (2019). Blockchain for 5G-Enabled IoT for Industrial Automation: A Systematic Review, Solutions, and Challenges. Mechanical Systems and Signal Processing. 135. 10.1016/j.ymssp.2019.106382.

BibText

@article{article,
author = {Mistry, Ishan and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2019},
month = {09},
pages = {},
title = {Blockchain for 5G-Enabled IoT for Industrial Automation: A Systematic Review, Solutions, and Challenges},
volume = {135},
journal = {Mechanical Systems and Signal Processing},
doi = {10.1016/j.ymssp.2019.106382}
}

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R. Gupta, S. Tanwar, S. Tyagi and N. Kumar, "Tactile-Internet-Based Telesurgery System for Healthcare 4.0: An Architecture, Research Challenges, and Future Directions," in IEEE Network, vol. 33, no. 6, pp. 22-29, Nov.-Dec. 2019, doi: 10.1109/MNET.001.1900063.

BibText

@ARTICLE{8933555,  author={Gupta, Rajesh and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},  journal={IEEE Network},   title={Tactile-Internet-Based Telesurgery System for Healthcare 4.0: An Architecture, Research Challenges, and Future Directions},   year={2019},  volume={33},  number={6},  pages={22-29},  doi={10.1109/MNET.001.1900063}}

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Shankar K, Lakshmanaprabu S.K., Ashish Khanna, Sudeep Tanwar, Joel J.P.C. Rodrigues, Nihar Ranjan Roy, Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier, Computers & Electrical Engineering, Volume 77, 2019, Pages 230-243, ISSN 0045-7906, https://doi.org/10.1016/j.compeleceng.2019.06.001.

BibText

@article{K2019230,
title = {Alzheimer detection using Group Grey Wolf Optimization based features with convolutional classifier},
journal = {Computers & Electrical Engineering},
volume = {77},
pages = {230-243},
year = {2019},
issn = {0045-7906},
doi = {https://doi.org/10.1016/j.compeleceng.2019.06.001},
url = {https://www.sciencedirect.com/science/article/pii/S0045790618325448},
author = {Shankar K and Lakshmanaprabu S.K. and Ashish Khanna and Sudeep Tanwar and Joel J.P.C. Rodrigues and Nihar Ranjan Roy},
keywords = {Alzheimer's disease, Classification, Feature extraction, Group Grey Wolf Optimization, Magnetic resonance imaging, Optimization},
abstract = {Alzheimer Detection (AD) is one of the most common memory depletion diseases that occurs at an older age and is also, a widely recognized type of dementia. In this paper, we propose a novel model for AD with Brain Image Analysis (BIA). Initially, the image database is considered for the removal of the unwanted region. Then an intermediate output is sent to extract the considerable features such as, texture, histogram, and scale-invariant transform from the magnetic resonance images of the brain. The Group Grey Wolf Optimization (GGWO) technique is used to increase the detection performance using Decision Tree, K-Nearest Neighbor, and Convolutional Neural Network classifiers. These are used to identify the reduced set of useful features without degrading the performance. The proposed approach achieved 96.23% of accuracy for the detection of Alzheimer disease, in comparison to the other competitive schemes in the existing literature.}
}

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Prasad, Vivek & Bhavsar, Madhuri & Tanwar, Sudeep. (2019). Influence of Montoring: Fog and Edge Computing. Scalable Computing. 20. 365-376. 10.12694/scpe.v20i2.1533.

BibText

@article{article,
author = {Prasad, Vivek and Bhavsar, Madhuri and Tanwar, Sudeep},
year = {2019},
month = {05},
pages = {365-376},
title = {Influence of Montoring: Fog and Edge Computing},
volume = {20},
journal = {Scalable Computing},
doi = {10.12694/scpe.v20i2.1533}
}

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Bhatia, Jitendra & Modi, Ysah & Tanwar, Sudeep & Bhavsar, Madhuri. (2019). Software Defined Vehicular Networks: A Comprehensive Review. International Journal of Communication Systems. 10.1002/dac.4005.

BibText

@article{article,
author = {Bhatia, Jitendra and Modi, Ysah and Tanwar, Sudeep and Bhavsar, Madhuri},
year = {2019},
month = {04},
pages = {},
title = {Software Defined Vehicular Networks: A Comprehensive Review},
journal = {International Journal of Communication Systems},
doi = {10.1002/dac.4005}
}

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I. Budhiraja, S. Tyagi, S. Tanwar, N. Kumar and J. J. P. C. Rodrigues, "DIYA: Tactile Internet Driven Delay Assessment NOMA-Based Scheme for D2D Communication," in IEEE Transactions on Industrial Informatics, vol. 15, no. 12, pp. 6354-6366, Dec. 2019, doi: 10.1109/TII.2019.2910532.

BibText

@ARTICLE{8688477,  author={Budhiraja, Ishan and Tyagi, Sudhanshu and Tanwar, Sudeep and Kumar, Neeraj and Rodrigues, Joel J. P. C.},  journal={IEEE Transactions on Industrial Informatics},   title={DIYA: Tactile Internet Driven Delay Assessment NOMA-Based Scheme for D2D Communication},   year={2019},  volume={15},  number={12},  pages={6354-6366},  doi={10.1109/TII.2019.2910532}}

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Alzubi, Jafar & Bharathikannan, Balasubramaniyan & Tanwar, Sudeep & MANIKANDAN, R. & Khanna, Ashish & Thaventhiran, Chandrasekar. (2019). Boosted Neural Network Ensemble Classification for Lung Cancer Disease Diagnosis. Applied Soft Computing. 80. 10.1016/j.asoc.2019.04.031.

BibText

@article{article,
author = {Alzubi, Jafar and Bharathikannan, Balasubramaniyan and Tanwar, Sudeep and MANIKANDAN, R. and Khanna, Ashish and Thaventhiran, Chandrasekar},
year = {2019},
month = {04},
pages = {},
title = {Boosted Neural Network Ensemble Classification for Lung Cancer Disease Diagnosis},
volume = {80},
journal = {Applied Soft Computing},
doi = {10.1016/j.asoc.2019.04.031}
}

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Hathaliya, Jigna & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj. (2019). Securing Electronics Healthcare Records in Healthcare 4.0 : A Biometric-based Approach. Computers & Electrical Engineering. 76. 10.1016/j.compeleceng.2019.04.017.

BibText

@article{article,
author = {Hathaliya, Jigna and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2019},
month = {04},
pages = {},
title = {Securing Electronics Healthcare Records in Healthcare 4.0 : A Biometric-based Approach},
volume = {76},
journal = {Computers & Electrical Engineering},
doi = {10.1016/j.compeleceng.2019.04.017}
}

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Tanwar, Sudeep & Tyagi, Sudhanshu & Budhiraja, Ishan & Kumar, Neeraj. (2019). Tactile Internet for Autonomous Vehicles: Latency and Reliability Analysis. IEEE Wireless Communications. 26. 10.1109/MWC.2019.1800553.

BibText

@article{article,
author = {Tanwar, Sudeep and Tyagi, Sudhanshu and Budhiraja, Ishan and Kumar, Neeraj},
year = {2019},
month = {04},
pages = {},
title = {Tactile Internet for Autonomous Vehicles: Latency and Reliability Analysis},
volume = {26},
journal = {IEEE Wireless Communications},
doi = {10.1109/MWC.2019.1800553}
}

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Gupta, Rajesh & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj. (2019). Tactile Internet and its Applications in 5G Era: A Comprehensive Review. International Journal of Communication Systems. 32. 10.1002/dac.3981.

BibText

@article{article,
author = {Gupta, Rajesh and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2019},
month = {04},
pages = {},
title = {Tactile Internet and its Applications in 5G Era: A Comprehensive Review},
volume = {32},
journal = {International Journal of Communication Systems},
doi = {10.1002/dac.3981}
}

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Verma, Jaiprakash & Tanwar, Sudeep & Garg, Sanjay & Gandhi, Ishit & Bachani, Nikita. (2019). Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques. International Journal of Business Analytics (IJBAN). 6. 1-15. 10.4018/IJBAN.2019070101.

BibText

@article{article,
author = {Verma, Jaiprakash and Tanwar, Sudeep and Garg, Sanjay and Gandhi, Ishit and Bachani, Nikita},
year = {2019},
month = {07},
pages = {1-15},
title = {Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques},
volume = {6},
journal = {International Journal of Business Analytics (IJBAN)},
doi = {10.4018/IJBAN.2019070101}
}

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Vora, Jayneel & Kaneriya, Shriya & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj & Obaidat,. (2019). TILAA: Tactile Internet-based Ambient Assistant Living In Fog Environment.

BibText

@article{VORA2019635,
title = {TILAA: Tactile Internet-based Ambient Assistant Living in fog environment},
journal = {Future Generation Computer Systems},
volume = {98},
pages = {635-649},
year = {2019},
issn = {0167-739X},
doi = {https://doi.org/10.1016/j.future.2019.01.035},
url = {https://www.sciencedirect.com/science/article/pii/S0167739X18326839},
author = {Jayneel Vora and Shriya Kaneriya and Sudeep Tanwar and Sudhanshu Tyagi and Neeraj Kumar and M.S. Obaidat},
keywords = {Tactile internet, Posture, Fog computing, Ambient assisted living, Efficiency, Accuracy},
abstract = {Ambient assisted living provides an environment to aid regular activities (such as exercise, medical diagnosis etc.) for well being of users. In this direction, posture correcting mechanisms can be developed to have a real-time detection and correction system, which allow doctors and caregivers to monitor the patients’ health without worrying about potential falls and backaches. Most of the data related to patient health resides on the cloud environment to be accessed from anywhere by different users. However, the existing cloud computing infrastructure does not support a quick feedback for posture correction by the patients located geographically separated locations.However, with the advent of 5G networks, a new networking paradigm of Tactile Internet emerges having low latency communication for ubiquitously connecting heterogeneous devices at the edge of the network. Keeping focus on these issues, we propose, TILAA, a framework for Tactile Internet-based Ambient Assistance Living in fog environment in this paper. It provides a collaborative communication feedback to the patients and doctors with reduced delay. TILAA consists of a micro controller, sensing devices such as-accelerometer and gyroscopes, and a battery holder. The straps on the chest wearable consist of actuators, which send vibrations as a Tactile feedback to the doctors or to the hospitals for taking proactive measures in case of emergency. Performance evaluation of TILAA shows that it has superior performance for the back posture of all patients as compared to pre-existing expensive detection and posture schemes.}
}

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Kumari, Aparna & Tanwar, Sudeep & Tyagi, Sudhanshu & Kumar, Neeraj. (2019). Verification and validation techniques for streaming big data analytics in internet of things environment. IET Networks. 8. 155-163. 10.1049/iet-net.2018.5187.

BibText

@article{article,
author = {Kumari, Aparna and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj},
year = {2019},
month = {05},
pages = {155-163},
title = {Verification and validation techniques for streaming big data analytics in internet of things environment},
volume = {8},
journal = {IET Networks},
doi = {10.1049/iet-net.2018.5187}
}

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Budhiraja, Ishan & Tyagi, Sudhanshu & Tanwar, Sudeep & Kumar, Neeraj & Guizani, Mohsen. (2019). Cross Layer NOMA Interference Mitigation for Femtocell Users in 5G Environment. IEEE Transactions on Vehicular Technology. PP. 10.1109/TVT.2019.2900922.

BibText

@article{article,
author = {Budhiraja, Ishan and Tyagi, Sudhanshu and Tanwar, Sudeep and Kumar, Neeraj and Guizani, Mohsen},
year = {2019},
month = {02},
pages = {},
title = {Cross Layer NOMA Interference Mitigation for Femtocell Users in 5G Environment},
volume = {PP},
journal = {IEEE Transactions on Vehicular Technology},
doi = {10.1109/TVT.2019.2900922}
}

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A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. S. Obaidat and J. J. P. C. Rodrigues, "Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions," in IEEE Wireless Communications, vol. 26, no. 3, pp. 47-53, June 2019, doi: 10.1109/MWC.2019.1800356.

BibText

@ARTICLE{8752482,  author={Kumari, Aparna and Tanwar, Sudeep and Tyagi, Sudhanshu and Kumar, Neeraj and Obaidat, Mohammad S. and Rodrigues, Joel J. P. C.},  journal={IEEE Wireless Communications},   title={Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions},   year={2019},  volume={26},  number={3},  pages={47-53},  doi={10.1109/MWC.2019.1800356}}

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I. Budhiraja, S. Tyagi, S. Tanwar, N. Kumar and J. J. P. C. Rodrigues, "Tactile Internet for Smart Communities in 5G: An Insight for NOMA-Based Solutions," in IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 3104-3112, May 2019, doi: 10.1109/TII.2019.2892763.

BibText

@ARTICLE{8611157,  author={Budhiraja, Ishan and Tyagi, Sudhanshu and Tanwar, Sudeep and Kumar, Neeraj and Rodrigues, Joel J. P. C.},  journal={IEEE Transactions on Industrial Informatics},   title={Tactile Internet for Smart Communities in 5G: An Insight for NOMA-Based Solutions},   year={2019},  volume={15},  number={5},  pages={3104-3112},  doi={10.1109/TII.2019.2892763}}