A cooperative image object recognition framework and task offloading optimization in edge computing

The development of AIoT (Artificial Intelligence of Things) and communication technologies has recently brought with it many promising applications. Intelligent surveillance is one of the new models of applications that synthesizes both technologies of IoTs and the object recognition method of artificial intelligence. However, this system suffers from the heavy computation load problem, but the processing devices are generally lightweight with only limited memory and computation power. In this paper, we propose a cooperative image object recognition framework to integrate the process of AIoT devices into the edge computing environment to overcome this problem. The proposed scheme offloads some captured surveillance video frames to nearby GPU equipped edge servers and then integrates the returned results to enhance the overall recognition accuracy of the surveillance system. We also formulate the AIoT Task Offloading scheduling to a mathematical programming Problem (named ATOP) and prove it to be an NP-complete problem. A heuristic Particle Swarm Optimization algorithm is also proposed to determine near optimal offloading scheduling for the ATOP such that the overall object recognition precision is maximized. Although the object recognition precision is enhanced, there still remains a gap to the ground truth, and the cumulative gap might cause significant errors as times goes by. In this research, we also develop a mechanism to help the designer to automatically diagnose when the cumulative errors become significant. Simulation results demonstrate that the proposed framework is feasible and the proposed Particle Swarm Optimization algorithm for the ATOP outperforms the greedy approach and the genetic algorithm. The simulation results also show that the abnormal data diagnosis mechanism can figure out when the cumulative errors happen using a real-world human-flow counting data set.