Work-in-Progress: Q-Learning Based Routing for Transiently Powered Wireless Sensor Network

被引:0
|
作者
Jia, Zhenge [1 ]
Wu, Yawen [1 ]
Hu, Jingtong [1 ]
机构
[1] Univ Pittsburgh, Elect & Comp Engn Dept, Pittsburgh, PA 15260 USA
来源
INTERNATIONAL CONFERENCE ON COMPILERS, ARCHITECTURE, AND SYNTHESIS FOR EMBEDDED SYSTEMS (CODES +ISSS) 2019 | 2019年
关键词
D O I
10.1145/3349567.3351732
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Reliable communication is a critical concern in power-limited energy harvesting wireless sensor networks (EH-WSNs). The communication optimization is needed since the protocols in battery-powered WSNs cannot adapt to the intermittent harvestable energy sources. In this paper, a novel reinforcement learning (RL) based routing algorithm that fully exploits the capability of wake-up radio (WuR) is presented. This routing strategy aims at increasing the packet delivery rate by leveraging wake-up radio devices to enable receiver nodes to make the decentralized forwarding decision. Simulation results show that the performance of the proposed learning approach, which requires only limited knowledge of the energy harvesting process, has only a small degradation compared to the optimal routing decision with full knowledge of energy harvesting process.
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页数:2
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