Open Access

ARTICLE

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

Hanzhong Zheng¹, Dejie Shi^2,*

1 Department of Computer Science, The University of Pittsburgh, Pittsburgh, PA, 15213, USA.
2 School of Computer and Information Engineering, Hunan University of Technology and Business, Changsha, 410205, China

* Corresponding Author: Dejie Shi. Email:

Journal of Cyber Security 2020, 2(2), 85-96. https://doi.org/10.32604/jcs.2020.010086

Received 10 February 2020; Accepted 28 June 2020; Issue published 14 July 2020

Abstract

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 network. However, 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 transmission. High frequency communication also has high probability of failure because of long distance data transmission. In this paper, we developed a novel approach to multi-sensor environment monitoring network using the idea of distributed system. Its communication network can overcome the difficulties of high communication cost and Single Point of Failure (SPOF) through the decentralized approach, which performs in-network computation. Our 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 network. Our 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.

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Citations