NIST UNIVERSITY
Institute Park, Berhampur, Odisha-761008, India

Bhabani Prasad Mishra

Mr. Bhabani Prasad Mishra

Assistant Professor

Computer Science and Engineering (SCHOOL OF ENGINEERING)

bhabani.mishra@nist.ed
9439524795
GAL - 105

Education

M.Tech (CSE) in Computer Science and Engineering
National Institute Of Science and Technology (NIST), Berhampur, BPUT, Odisha
MCA in Computer Science
Institute for Advanced Computer Research (IACR), Rayagada, BPUT, Odisha

Work Experience

Assistant Professor
Roland Institute of Technology and Engineering
Assistant Professor
Gandhi Academy of Technology & Engineering
Lecturer
Gandhi Institute of Industrial Technology

Research Interests

  • No research interests available.

Publications

  1. Mishra, B.P., Rajput, N.S. and Satapathy, S.K., 2024, December. Deep Reinforcement Learning Based Intelligent Approach to Channel Allocation for Effective Communications in Vehicle Platoon Systems. In 2024 IEEE 8th International Conference on Information and Communication Technology (CICT) (pp. 1-6). IEEE.
  2. Mishra, B.P., Rajput, N.S. and Satapathy, S.K., 2024, December. Investigating IEEE 802.11 p Communications for Transmitting Basic Safety Messages in Vehicle Platoons on Expressways. In 2024 IEEE 8th International Conference on Information and Communication Technology (CICT) (pp. 1-6). IEEE.
  3. Vehicle platoons are the applications that provide greener, safer, faster, and economical transport as part of modern intelligent transportation systems. The advent of deep learning and reinforcement learning made it possible to address standard communication problems effectively using data-driven techniques. In this short research paper, we discuss the realm of vehicular platoon where deep and reinforcement learning can be applied and requires attention. We also propose an application of deep reinforcement learning to solve the resource allocation problem intelligently, addressing the specific needs of vehicle platoons. This application should pave the way for using deep reinforcement learning implements in vehicle platoon communications per se. We also discuss the scope for improvements required to well adopt deep reinforcement learning in this regime.
  4. Vehicle platooning is a full-of-promise technology for the future of intelligent transportation systems. A key requirement for vehicle platoons is to maintain strong communication between the vehicles to prevent the platoon from splitting. In India, maintaining resilient communications between vehicles is hard due to extensive road networks, challenging conditions, and high traffic volume. While resolving connectivity issues on normal roadways may take time, modern Indian expressways offer potential venues for operating vehicle platoons. In this work, we investigate the feasibility of having resilient vehicle platoon communications over IEEE802.11p and hence, having stable platoons on Indian expressways. We have performed experiments for realistic platoon scenarios on National Expressway-1 and have an extensive analysis of various network parameters. It is evident from the results that Indian expressways could be ventured for vehicle platoon operations using IEEE802.11p.
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