Domain Knowledge-Based Evolutionary Reinforcement Learning for Sensor Placement

Sensors

Wenyin Gong¹  Mingxuan Song¹  Xuesong Yan¹  Chengyu Hu¹ 

[1] School of Computer Science, China University of Geosciences, Wuhan 430074, China;

关键词: sensor placement;    evolutionary reinforcement learning;    domain knowledge;    combinatorial optimization;   

DOI  :  10.3390/s22103799

来源: DOAJ

【 摘 要 】

Reducing pollutant detection time based on a reasonable sensor combination is desirable. Clean drinking water is essential to life. However, the water supply network (WSN) is a vulnerable target for accidental or intentional contamination due to its extensive geographic coverage, multiple points of access, backflow, infrastructure aging, and designed sabotage. Contaminants entering WSN are one of the most dangerous events that may cause sickness or even death among people. Using sensors to monitor the water quality in real time is one of the most effective ways to minimize negative consequences on public health. However, it is a challenge to deploy a limited number of sensors in a large-scale WSN. In this study, the sensor placement problem (SPP) is modeled as a sequential decision optimization problem, then an evolutionary reinforcement learning (ERL) algorithm based on domain knowledge is proposed to solve SPP. Extensive experiments have been conducted and the results show that our proposed algorithm outperforms meta-heuristic algorithms and deep reinforcement learning (DRL).

【 授权许可】

Unknown