CADET: Control-Aware Dynamic Edge Computing for Real-Time Target Tracking in UAV Systems

The autonomous operations of unmanned aerial vehicles (UAV) necessitate the real-time analysis of information-rich signals, such as camera and LiDAR feeds, where the analysis algorithms often take the form of extremely complex deep neural networks (DNN). The continuous execution of such models onboard the UAV imposes a considerable resource consumption (e.g., energy), while offloading the execution of the models to edge servers requires the transmission of the input signals over capacity-constrained, time-varying, wireless channels. In this paper, we propose an innovative approach – CADET – to control where sensor signals are processed in the system. In addition to traditional features and measures, such as channel state, energy consumption, and channel usage, CADET makes dynamic task routing decisions – local computing vs edge computing – based on the state of the flight controller. The proposed methodology is based on Markov jump-switched linear systems, where an embedded filter predicts and controls the state of the joint motion/computing dynamics.