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Fog computing is an emerging distributed computing model for the Internet ofThings (IoT). It extends computing and caching functions to the edge ofwireless networks. Uncrewed Aerial Vehicles (UAVs) provide adequate support forfog computing. UAVs can not only act as a relay between mobile users andphysically remote edge devices to avoid costly long-range wirelesscommunications but also are equipped with computing facilities that can takeover specific tasks. In this paper, we aim to optimize the energy efficiency ofa fog computing system assisted by a single UAV by planning the trajectories ofthe UAV and assigning computing tasks to different devices, including the UAVitself. We propose two algorithms based on the classical Ant Colony andParticle Swarm Optimization techniques and solve the problem by continuousconvex approximation. Unlike most existing studies where the trajectories areassumed to be straight lines, we account for the effect of obstacles, such asbuildings, and deliberately avoid them during the trajectory planning phase.Through extensive simulation experiments, we demonstrate that our proposedapproach can achieve significantly better energy efficiency than existingbenchmark algorithms.

Optimal Trajectory Planning and Task Assignment for UAV-assisted Fog Computing