A Continuous Object Tracking Scheme Based on Two-Stage Prediction in Industrial Internet of Things

Abstract

Due to the poisonousness, explosiveness, and diffuseness of some continuous objects (e.g., toxic gas, nuclear radiation, industrial dust), continuous object tracking has a pivotal role in protecting the safety of the people, especially in hazardous industries. To improve production safety, the Industrial Internet of Things (IIoT) has become a promising technology for continuous object tracking. However, IIoT can hardly satisfy the requirements of both energy-efficiency and tracking accuracy due to diffusion characteristics, redundant packets, unnecessary awakened nodes, etc. To address these challenges, we propose a Two-stage Continuous Object predictive Tracking scheme based on a State Transition Model (TCOT-STM). First, the predictive tracking process of TCOT-STM is partitioned into two stages to determine wake-up regions where the future continuous objects are located. Considering the high diffusion speed in the tracking process, stage 1 tracking is designed by communication range calibration and global wake-up region establishing. To eliminate the redundant boundary nodes in the tracking process, stage 2 tracking is designed by inter-cluster gap eliminating, virtual node generating, and local wake-up region establishing. Then, a state transition model based on finite state machines is designed to awaken nodes selectively. Finally, with the state transition model and the wake-up regions determined by two-stage tracking, the potential boundary nodes are proactively awakened for predictive tracking. Simulation results demonstrate that the proposed TCOT-STM can reduce energy consumption and communication cost while improving tracking accuracy.