Edge-coordinated energy-efficient video analytics for digital twin in 6G

Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital twin. In this paper, with the aim of developing energy-efficient digital twin in 6G, we investigate real-time video analytics based on cameras mounted on mobile devices with edge coordination. This problem is challenging because 1) mobile devices are with limited battery life and lightweight computation capability, and 2) the captured video frames of mobile devices are continuous changing, which makes the corresponding tasks arrival uncertain. To achieve energy-efficient video analytics in digital twin, by taking energy consumption, analytics accuracy, and latency into consideration, we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework, which can utilized digital twin models to achieve joint offloading decision and configuration selection. The edge nodes help to collect the information on network topology and task arrival. Extensive simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.