A Data Agent Inspired by Interpersonal Interaction Behaviors for Wireless Sensor Networks

Event monitoring is the main purpose of monitoring applications based on wireless sensor networks (WSNs). To realize the autonomous representation of tunnel disasters in WSNs, this work proposes a data agent (DA) inspired by interpersonal interaction behaviors, which bridges the gap between data and humans. The DA divides event monitoring into four states: 1) active perception; 2) understanding; 3) thinking; and 4) representation, to realize a kind of human-like event monitoring. First, a DA behavior model based on a finite-state machine is proposed, which can adaptively switch among these four states. Second, four methods, including active perception based on a dual neuron perception structure, understanding for forming the information granularity, thinking for forming the disaster knowledge graph, and representation using grayscale and knowledge graphs are proposed to realize the four states of the DA. The experimental results show that the DA inspired by interpersonal interaction can make WSNs not only achieve efficient and accurate disaster self-monitoring but also represent the disaster situation intuitively and quickly through grayscale and knowledge graphs.