Performability Modeling and Analysis for Real-Time Object Detection on UAV Systems

With the widespread application of Uncrewed Aerial Vehicles (UAVs) in various real-world surveillance scenarios, the quality analysis of UAV-based monitoring systems has become an emergent challenge. Previous model-based studies often made theoretical assumptions to estimate the performance and availability of UAV computing systems, without detailed considerations of the interaction between UAVs and fog computing nodes, as well as computation steps of object detection algorithms. In this paper, we propose Stochastic Reward Nets (SRNs) to capture computational behavior and analyze performance, availability, and performability metrics of a UAV system that utilizes computation offloading. In order to obtain more realistic parameters for model-based analysis, we conduct empirical experiments using an edge computing device to emulate real-time object detection on a UAV. We measure the throughput of real-time object detection process in three stages by experiments that are fed into parameters for numerical analysis on the proposed model. Through the sensitivity analysis, we demonstrate the impact of different computation modes and video resolutions on performance and availability metrics, providing insights for improving UAV system design and operation.