UAV-Assisted Split Computing System: Design and Performance Optimization

In the conventional split computing approach based on the external computing node (e.g., cloud), Internet of Things (IoT) devices suffer from high network latency. In this paper, we introduce an unmanned aerial vehicle (UAV)-assisted split computing system (USCS) where UAV patrols around IoT device and IoT device offloads the performing the tail model inference to UAV. To minimize the energy consumption while maintaining a sufficiently low inference completion time, IoT device makes two types of decisions: 1) the timing of starting the split computing (i.e., whether to conduct the split computing or delay); and 2) the splitting point. By formulating a constrained Markov decision process (CMDP) problem and converting the CMDP model into a linear programming (LP) model, the decisions of IoT device can be optimized. The evaluation results show that USCS can significantly reduce energy consumption while satisfying the inference completion time requirement.