@Article{jcs.2020.010086,
AUTHOR = {Hanzhong Zheng, Dejie Shi},
TITLE = {A Multi-Agent System for Environmental Monitoring Using Boolean Networks and Reinforcement Learning},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {85--96},
URL = {http://www.techscience.com/JCS/v2n2/39508},
ISSN = {2579-0064},
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.},
DOI = {10.32604/jcs.2020.010086}
}