Reinforcement Learning Based Routing in EH-WSNs with Dual Alternative Batteries - 文章详情页

作者：

Zhao, Tian (Zhao, Tian.) | Wang, Luyao (Wang, Luyao.) | Chin, Kwan-Wu (Chin, Kwan-Wu.)

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EI Scopus

摘要：

This　paper　considers　an　Energy　Harvesting　Wireless　Sensor　Network　(EH-WSN)　where　nodes　have　a　dual　alternative　battery　system.　We　propose　a　stateless　distributed　reinforcement　learning　based　routing　algorithm,　named　QLRA,　where　each　node　learns　the　best　next　hop(s)　to　forward　its　data　based　on　the　battery　and　data　information　of　its　neighbors.　We　study　how　the　number　of　sources　and　path　exploration　probability　impacts　the　performance　of　QLRA.　Numerical　results　show　that　after　learning,　QLRA　is　able　to　achieve　minimal　end-to-end　delays　in　all　tested　scenarios,　which　is　about　18%　lower　than　the　average　end-to-end　delay　of　a　competing　routing　algorithm.　©　2020　ACM.

关键词：

Learning algorithms Sensor nodes Electric batteries Energy harvesting Reinforcement learning

作者机构：

[ 1 ] [Zhao, Tian]Beijing University of Technology, Beijing Advanced Innovation Center for Future Internet Technology, China
[ 2 ] [Wang, Luyao]Beijing University of Technology, Beijing Advanced Innovation Center for Future Internet Technology, China
[ 3 ] [Chin, Kwan-Wu]University of Wollongong, School of Electrical Computer and Telecommunication Engineering, China

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