Enhancing V2I and V2V Communication in Vehicular Networks through GSA-BPSO Optimized Neural Network
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Dr. Sheela S is an accomplished academician and researcher with over 15 years of combined experience in teaching and industry. She currently serves as an Assistant Professor in School of Computer Science and Engineering, RV University Bengaluru.
She holds a Ph.D. from Visvesvaraya Technological University in the area of Vehicular Networks and Neural Networks, along with a Post Graduate Diploma in Artificial Intelligence and Machine Learning from The University of Texas at Austin. Her academic foundation includes an M.Tech in Digital Communication Engineering and a B.E. in Electronics and Communication Engineering.
Dr. Sheela’s research spans Intelligent Transportation Systems, Machine Learning, Deep Learning, Internet of Things, and Blockchain applications. She has published over 46 research articles in reputed Scopus and IEEE-indexed journals and conferences, contributed to multiple book chapters, and holds patents in AI-based traffic management and intelligent systems.
She has been a recipient of several awards, including Best Paper recognition, and has been invited as a session chair and reviewer at prestigious international conferences. A dedicated mentor, she has guided numerous undergraduate and postgraduate projects, many of which have received recognition and funding from national agencies.
Dr. Sheela actively contributes to academic development through organizing faculty development programs, hackathons, and workshops, and serves in key administrative roles such as NAAC/NBA coordinator, Alumni coordinator, and NIRF coordinator.
Passionate about blending research with practical applications, she continues to explore innovative solutions in AI, IoT, and emerging technologies, aiming to address societal challenges through technology.
Google Scholar ID: YBdVZZIAAAAJ
Scopus ID: 57212699160
Knowledge finds its true value when it solves real problems.
Writeup: This research focuses on improving both Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication by integrating a Gravitational Search Algorithm (GSA) with Binary Particle Swarm Optimization (BPSO) in neural networks. The optimized model ensures robust connectivity, high data transfer rates, and reduced packet loss in vehicular networks. By addressing the challenges of dynamic topologies and high-speed mobility, the study demonstrates significant performance improvements over existing allocation and routing methods. The results hold strong potential for deployment in next-generation intelligent transportation systems, ensuring efficient and reliable vehicular communication. Source link: DOI: 10.22266/ijies2023.1231.26
Writeup: This study introduces a hybrid optimization method combining Cuckoo Search (CS) and Grey Wolf Optimization (GWO) to enhance resource allocation in Vehicle-to-Vehicle (V2V) communications. The approach improves channel utilization, reduces interference, and supports stable communication in highly dynamic vehicular environments. Simulation results show notable gains in throughput and latency reduction compared to traditional methods, making it highly suitable for Intelligent Transportation Systems (ITS). This research provides a scalable solution to the spectrum scarcity problem, enabling safer and more efficient V2V operations. Source link: https://ijisae.org/index.php/IJISAE/article/view/2961
An Efficient Approach for Traffic Congestion and Management using IoT Devices and FPGA – IoT-enabled FPGA system for real-time traffic monitoring, prediction, and congestion control. (India, 2020)
Real-Time Traffic Management System using Machine Learning and Image Processing – AI-driven traffic density detection and dynamic signal control for urban mobility. (Australia, 2021)
AI-based Smart System for Fast and Accurate Verdict Prediction in Legal Cases – AI tool for predicting court verdicts to improve legal decision-making efficiency. (India, 2024)
Awarded for ""An Efficient Vehicle-to-Vehicle Communication System Using Intelligent Transportation System"" at the IEEE-supported ICRASET 2023 conference.
Recognized for coordinating and delivering ""Exploratory Data Analysis in Python"" at GAT (Dec 2022 – Mar 2023).
Honored for conducting ""Data Analytics using Python"" for ECE students at SMVITM, Udupi (Feb 2023).
Acknowledged for delivering ""Deep Learning and its Applications"" organized by AI & DS Department, GAT (Apr 2022).
Served as resource person for two-week FDP on ""IoT, Embedded Systems, and AI"" sponsored by DST at JSSATE, Bangalore.
