Toward Proximity Surveillance and Data Collection in Industrial IoT: A Multi-Stage Statistical Optimization Design

This letter considers a heterogeneous traffic scenario in Industrial Internet of Things (IIoT), where a multi-functional robot performs proximity surveillance task concurrent with data collection from sensors. Under a practical no-go zone constraint in IIoT, a novel multi-stage statistical optimization is formulated, for ensuring video quality and sensor queue stability. Then, a novel algorithm is proposed to jointly optimize the robot's trajectory and resource allocation, which decouples the original multi-stage statistical problem into a series of deterministic problems without violating the causality of the system knowledge. Simulation results confirm the superiority of the proposed algorithm in both maintaining data queue stability and surveillance distance performance.