A Multi-Agent System for Environmental Monitoring Using Boolean Networks and Reinforcement Learning

作     者：Hanzhong Zheng Dejie Shi 

作者机构：Department of Computer ScienceThe University of PittsburghPittsburghPA 15213USA School of Computer and Information EngineeringHunan University of Technology and BusinessChangsha410205China 

出 版 物：《Journal of Cyber Security》 (网络安全杂志（英文）)

年 卷 期：2020年第2卷第2期

页      面：85-96页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This research is supported by Natural Science Foundation of Hunan Province(No.2019JJ40145) Scientific Research Key Project of Hunan Education Department(No.19A273) open Fund of Key Laboratory of Hunan Province(2017TP1026) 

主　　题：Multi-agent system reinforcement learning environment monitoring 

摘      要：Distributed wireless sensor networks have been shown to be effective for environmental monitoring tasks,in which multiple sensors are deployed in a wide range of the environments to collect information or monitor a particular event,Wireless sensor networks,consisting of a large number of interacting sensors,have been successful in a variety of applications where they are able to share information using different transmission protocols through the communication ***,the irregular and dynamic environment requires traditional wireless sensor networks to have frequent communications to exchange the most recent information,which can easily generate high communication cost through the collaborative data collection and data *** frequency communication also has high probability of failure because of long distance data *** this paper,we developed a novel approach to multi-sensor environment monitoring network using the idea of distributed *** communication network can overcome the difficulties of high communication cost and Single Point of Failure(SPOF)through the decentralized approach,which performs in-network *** approach makes use of Boolean networks that allows for a non-complex method of corroboration and retains meaningful information regarding the dynamics of the communication *** approach also reduces the complexity of data aggregation process and employee a reinforcement learning algorithm to predict future event inside the environment through the pattern recognition.