S
Sudeep Tanwar
Researcher at Nirma University of Science and Technology
Publications - 410
Citations - 11253
Sudeep Tanwar is an academic researcher from Nirma University of Science and Technology. The author has contributed to research in topics: Computer science & Smart grid. The author has an hindex of 43, co-authored 263 publications receiving 5402 citations. Previous affiliations of Sudeep Tanwar include Bharat Institute of Technology & University Institute of Technology, Burdwan University.
Papers
More filters
Journal ArticleDOI
Scalable priority-based resource allocation scheme for M2M communication in LTE/LTE-A network
Upendra Kumar Singh,Amit Dua,N Vignesh Kumar,Sudeep Tanwar,Rabat Iqbal,Mohammad Hijji,Saad Amin,Ravi Sharma +7 more
TL;DR: In this article , the authors proposed a scalable priority-based resource allocation scheme for M2M communication in the LTE/LTE-Advance network, which aims to balance resource utilization and application priority support.
Journal ArticleDOI
GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles
Raj Parekh,Nisarg P. Patel,Rajesh Gupta,Nilesh Jadav,Sudeep Tanwar,Abdullah Alharbi,Amr Tolba,Bogdan-Constantin Neagu,Maria Simona Raboaca +8 more
TL;DR: In this article , the authors introduced the concept of gradient encryption in federated learning (FL), which preserves the users' privacy without the additional computation requirements, and the computational power present in the edge devices helps to fine tune the local model and encrypt the input data to preserve privacy without any drop in performance.
Journal ArticleDOI
A Zero-Sum Game-Based Secure and Interference Mitigation Scheme for Socially Aware D2D Communication With Imperfect CSI
Rajesh Gupta,Sudeep Tanwar +1 more
TL;DR: Simulation results prove the proposed zero-sum game-based system’s superiority, considering average network sum rate and average channel secrecy capacity, and the goal of the D2D transmitter, i.e., maximize the message security.
Journal ArticleDOI
A survey on energy‐efficient resource allocation schemes in device‐to‐device communication
TL;DR: 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 is presented.
Journal ArticleDOI
A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors
Harsh Mankodiya,Priyal Palkhiwala,Rajesh Gupta,Nilesh Jadav,Sudeep Tanwar,Bogdan-Constantin Neagu,Gheorghe Grigoras,Fayez Alqahtani,Ahmed Mahmoud Shehata +8 more
TL;DR: A machine-learning-based model is proposed for COVID-19 prediction with these parameters as input and the support vector machine, which performed the best, received an F1-score of 96.64% and an accuracy score of 95.57%.