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A Learning Automata Based Stable and Energy-Efficient Routing Algorithm for Discrete Energy Harvesting Mobile Wireless Sensor Network

  • Sheng Hao
  • Hu-yin ZhangEmail author
  • Jing Wang
Article
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Abstract

Wireless sensor networks (WSN) have been widely used in urban network system and networked monitoring system, which provide easy connectivity and high physical data rate. Considering the battery-limited property of sensor nodes, recently, energy harvesting (EH) technology is introduced into WSN, which can alleviate traditional WSN problems (energy consumption, energy equilibrium, transmission efficiency, etc.). Current EH-WSN routing algorithms generally use the continuous energy harvesting mode, therefore, how to design an efficient routing algorithm for discrete energy harvesting mode and ensure the overall energy balance and conservation is still a great challenge and needs to be solved. Especially, under the mobile environment, the impact of route stability needs to be considered, which makes the design more complicated. To address the above problems, we propose a learning automata (LA) theory based stable and energy-efficient routing algorithm for discrete EH-mobile WSN (DEH-LA-SERA, for short). Firstly, we construct a multi-factors measurement model for sensor nodes, which contains node stability model, energy ratio function, expected harvesting energy model (using Markov decision process method) and direction judgement model. On this basis, we derive the node weighted value, i.e., selecting probability, which can be used to determine whether a node can be chosen as relay node. Secondly, with the help of LA theory, we construct a feedback mechanism to adjust the optimal path. With this solution, we can ensure the overall energy balance and conservation while holding the stability of selected path. As demonstrated in simulation experiments, our algorithm, DEH-LA-SERA, achieved the best performance in route survival time, energy consumption, energy balance and acceptable performance in end-to-end delay and packets delivery ratio.

Keywords

Energy harvesting wireless sensor network Mobile environment Routing algorithm Multi-factors measurement model Markov decision process Learning automata theory Stability and energy optimization 

Notes

Acknowledgements

The work is supported by the National Natural Science Foundation of China (No.61772386).

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanPeople’s Republic of China

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