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

Due to the poisonousness, explosiveness, and diffuseness of some continuous objects (e.g., toxic gas, nuclear radiation, and 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 I tracking is designed by communication range calibration and global wake-up region establishing. To eliminate the redundant boundary nodes in the tracking process, stage II tracking is designed by intercluster gap eliminating, virtual node generating, and local wake-up region establishing. Then, a state transition model (STM) based on finite state machines is designed to awaken nodes selectively. Finally, with the STM 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.