Work place: Thapar Institute of Engineering and Technology, Patiala, India
E-mail: deepak.rakesh@thapar.edu
Website: https://orcid.org/0000-0003-0400-4749
Research Interests:
Biography
Deepak K. Rakesh is an Assistant Professor in the Department of Computer Science and Engineering at Thapar
Institute of Engineering & Technology, Patiala. He received his Ph.D. in Machine Learning from IIT (ISM)
Dhanbad, where he specialized in designing feature selection algorithms for complex classification problems.
He has also served as a Visiting Researcher at the University of Economics and Human Sciences (UEHS),
Poland. His research interests span machine learning, explainable artificial intelligence (XAI), fairness in AI,
and wireless sensor networks. Dr. Rakesh has published widely in reputed international journals such as IEEE
Transactions on Information Theory, Pattern Recognition, and Soft Computing, and has presented his work at
multiple inter-national conferences. He is also an inventor of a granted patent on AI-enabled neurological
disorder detection. In addition to his research, Dr. Rakesh has actively mentored undergraduate and postgraduate students, served as a reviewer for several peer-reviewed journals, and is engaged in collaborative research on fairness and bias in AI.
By Parikesh Dhal Narendra Kumar Kamila Deepak Kumar Rakesh
DOI: https://doi.org/10.5815/ijwmt.2026.02.13, Pub. Date: 8 Apr. 2026
Wireless Sensor Networks (WSNs) are fundamental to security and surveillance applications such as military defense, disaster management, and intrusion monitoring. The performance of these networks depends largely on the efficiency of routing protocols. This paper examines the Ad hoc On-Demand Distance Vector (AODV) routing protocol in multi-hop WSN environments for target tracking, evaluating critical metrics including Packet Delivery Ratio (PDR), End-to-End Delay, Energy Consumption, and Network Lifetime. Simulation results illustrate the impact of node depletion due to transmission loss, affecting network stability and robustness in target detection. In-network detection in WSNs presents trade-offs between real-time data transmission, energy efficiency, and trajectory lifespan. Inefficient routing optimization may result in increased latency, packet loss, and premature node failure, ultimately reducing localization accuracy. While AODV’s reactive path-based approach offers manageable overhead, its performance degrades under increased energy consumption and route rediscovery delays. This study systematically evaluates AODV’s strengths and limitations in time-sensitive detection scenarios. Findings indicate that AODV ensures reliable data transmission in early network stages but suffers significant performance deterioration as node energy declines, impacting coverage and responsiveness. To enhance AODV’s target tracking capabilities, this paper proposes adaptive energy-saving techniques and hybrid routing schemes. These strategies contribute to ongoing research aimed at optimizing routing protocols to balance accuracy and node longevity for real-time WSN applications.
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