Energy Optimization of Distributed Video Processing System in Dynamic Environment

To create a future society based on cyber-physical systems, we need real-time digital twins made with cameras and sensors. The challenge is making this energy-efficient. A model in [1] suggests dividing video analysis tasks among terminals, edge servers, and cloud servers to minimize power consumption. This paper addresses energy optimization in dynamic environments with a two-level approach: detecting and categorizing environmental changes (LEC and SEC) and using a two-level adaptive evolutionary algorithm (TAEA) to make corresponding adjustments. A case study with a differential evolution algorithm demonstrates its effectiveness in minimizing energy costs and improving processing accuracy and latency violations.