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Multiple agent based reinforcement learning for energy efficient routing in WSN

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Abstract

Wireless sensor network (WSN) is an evergreen research area, which always looks for energy efficiency. The main challenge for attaining energy efficiency in WSN is data transmission. It is well-explored that a good routing algorithm helps in achieving energy efficiency and contributes to enhance the network lifetime. Inspired by the potential of reinforcement learning (RL), this article presents an RL based routing algorithm for WSN, which frames routes by considering the current status of the network. This results in the detection of optimal routes, such that the transmission delay is minimized and the reliability is increased by the choice of reward functions. Understanding the importance of reward functions, this work selects three trustworthy reward functions for the computation of Q-value. The performance of the proposed work is analyzed and compared with the existing approaches to justify the effectiveness of the proposed routing technique in terms of packet delivery rate, average latency, energy consumption and network lifetime.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KLN College of Engineering, Sivagangai, TN, India

    D. Prabhu

  2. School of Computing, SASTRA Deemed University, Thanjavur, TN, India

    R. Alageswaran

  3. Department of Information Technology, KLN College of Engineering, Sivagangai, TN, India

    S. Miruna Joe Amali

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  1. D. Prabhu
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Correspondence to D. Prabhu.

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Prabhu, D., Alageswaran, R. & Miruna Joe Amali, S. Multiple agent based reinforcement learning for energy efficient routing in WSN. Wireless Netw 29, 1787–1797 (2023). https://doi.org/10.1007/s11276-022-03198-0

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