Predictive Boundary Tracking Based on Motion Behavior Learning for Continuous Objects in Industrial Wireless Sensor Networks

The diffusion of toxic gas, biochemical material, and radio-active contamination - known as continuous objects - endangers the safe production of the petrochemical and nuclear industries. To mitigate these well known hazards, the new paradigm of industrial wireless sensor networks (IWSNs) shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial fields. In order to prolong the lifetime of these networks, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively, due to the difficulty in predicting the spatiotemporal evolution of diffusive hazards. In this article, we propose a motion behavior learning predictive tracking (MBLPT) algorithm for continuous objects in IWSNs. Considering the relatively unpredictable patterns exhibited by continuous objects, the MBLPT uses a data-driven approach for motion state recognition, and then utilizes Bayesian model averaging (BMA) for future boundary prediction. The prediction of the MBLPT provides the knowledge for establishing a wake-up zone, in which standby nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that the MBLPB achieves superior energy efficiency while keeping effective tracking accuracy.

Automated summary

This article proposes a motion behavior learning predictive tracking algorithm for continuous objects in industrial wireless sensor networks (IWSNs). The algorithm uses a data-driven approach to recognize motion states and utilizes Bayesian model averaging for future boundary prediction. The prediction provides the knowledge for establishing a wake-up zone, where standby nodes are activated in advance to participate in boundary tracking.