Reliable Data Delivery Using Fuzzy Reinforcement Learning in Wireless Sensor Networks

Reliable Data Delivery Using Fuzzy Reinforcement Learning in Wireless Sensor Networks

Автор: Sateesh Gudla, Kuda Nageswara Rao

Журнал: International Journal of Computer Network and Information Security @ijcnis

Статья в выпуске: 4 vol.15, 2023 года.

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Wireless sensor networks (WSNs) has been envisioned as a potential paradigm in sensing technologies. Achieving energy efficiency in a wireless sensor network is challenging since sensor nodes have confined energy. Due to the multi-hop communication, sensor nodes spend much energy re-transmitting dropped packets. Packet loss may be minimized by finding efficient routing paths. In this research, a routing using fuzzy logic and reinforcement learning procedure is designed for WSNs to determine energy-efficient paths; to achieve reliable data delivery. Using the node’s characteristics, the reward is determined via fuzzy logic. For this paper, we employ reinforcement learning to improve the rewards, computed by considering the quality of the link, available free buffer of node, and residual energy. Further, simulation efforts have been made to illustrate the proposed mechanism’s efficacy in energy consumption, delivery delay of the packets, number of transmissions and lifespan.

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Wireless Sensor Networks, Q-learning, Reinforcement Learning Algorithm, Fuzzy Logic, Energy Efficiency, Reliable Paths, Network Lifetime

Короткий адрес: https://sciup.org/15018637

IDR: 15018637   |   DOI: 10.5815/ijcnis.2023.04.09

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