The classical problem of finding the optimal parent node in wireless network tree routing is discussed in this study. Various indexes affecting the decision rules of tree routing are analyzed, such as weighted average received signal strength, buffer occupation rate and power consumption ratio. A system model of enhanced tree routing protocol and reinforcement learning algorithm based on reinforcement learning is proposed in wireless sensor networks. The basic operation of the proposed tree-based routing protocol is described in detail, and the algorithm is updated for cyclic detection of parent node. In order to make adaptive decisions in complex scenarios, a state space, an action set and an excitation function are defined. The optimal parent node with the highest excitation is identified through trial and error. Through simulation and comparative study, it is verified that the parent node selection scheme achieves reasonable tradeoff among the performance indicators such as end-to-end delay, reliability and energy consumption. Through simulation and comparative analysis, the efficacy of the parent node selection scheme is validated, demonstrating a judicious tradeoff among performance indicators such as end-to-end delay, reliability, and energy consumption.
ZHANG Huanan, LI Shijun, JIN Hong
. Research on Enhanced Routing for Reinforcement Learning in Wireless Sensor Networks[J]. Journal of Applied Sciences, 2024
, 42(1)
: 83
-93
.
DOI: 10.3969/j.issn.0255-8297.2024.01.007
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