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Smart home's wireless sensor networks lifetime optimizing using Q-learning
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DOI：10.1109/IECON48115.2021.9589460.png)
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Abstract
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Wireless sensor networks (WSN) have known an increased utilization in the last years in different domains including smart homes. One of the most inconvenient of these networks is the energy consumption because generally the nodes are supplied by small batteries with short autonomy. In this paper, we propose a sophisticated routing protocol approach based on Q-learning (QLRP) attempts to optimize the lifetime of the WSN used for smart home applications. The QLRP takes the benefits of the Q-learning to learn for the optimal routing path for data transmission with optimal energy consumption. We compare the proposed routing approach with two other routing protocols which are the direct routing to the sink based on a star topology and the hierarchical routing protocol. The simulation results show that the QLRP has promising advantages in terms of optimization of the energy consumption of the WSN.
Keywords:
WSN
lifetime optimization
smart home
Q-learning
reinforcement learning
energy efficiency
routing protocol
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