Optimizing Energy-Efficient Resource Allocation in Wireless Sensor Networks using Reinforcement Learning
Author(s):
Dr.Varakumari Samudrala, M. Bhavana Sri, M. Deepanvitha, K. Sai Kiran, N. Renuka Devi
Keywords:
Reinforcement Learning, Energy Harvesting, Wireless Sensor Networks, Resource Allocation, Energy Efficiency
Abstract
Wireless Sensor Networks (WSNs) with Energy Harvesting (EH) sensors, the efficient allocation of resources is pivotal to maximize energy utilization and network performance. This abstract presents an approach that leverages Reinforcement Learning (RL) algorithms for resource allocation, aiming to address this critical challenge. We formulate the problem as an RL task, with the state space encompassing vital network parameters such as sensor energy levels, channel conditions, and traffic loads. An action space is defined to encompass various resource allocation decisions, including time slot assignments, transmission power levels, and data compression techniques. A reward function is designed to quantify the trade-off between energy efficiency and overall network performance. The RL agent learns an optimal policy through interactions with the environment, continuously adapting its resource allocation strategies to varying energy availability, network conditions, and traffic patterns. This approach promises adaptability and efficiency in EH-WSNs, with the potential to extend sensor lifetimes and enhance data transmission capabilities while aligning with the dynamic nature of such networks. The ReLeC protocol demonstrated superior performance, surpassing LEACH by 88.32% and outperforming PEGASIS by 28.9% in network lifespan. This remarkable efficiency highlights ReLeC's effectiveness in prolonging the operational lifetime of wireless sensor networks, showcasing its potential for energy-efficient and sustainable applications.
Article Details
Unique Paper ID: 162361

Publication Volume & Issue: Volume 10, Issue 9

Page(s): 309 - 315
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