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

TL;DR

This paper presents optimized trajectory planning and task assignment algorithms for UAV-assisted fog computing, considering obstacles and aiming to improve energy efficiency in IoT networks.

Contribution

It introduces novel algorithms based on Ant Colony and Particle Swarm Optimization that account for obstacles, enhancing energy efficiency in UAV-assisted fog computing.

Findings

Significantly improved energy efficiency over benchmark algorithms

Effective obstacle avoidance in UAV trajectory planning

Validation through extensive simulation experiments

Abstract

Fog computing is an emerging distributed computing model for the Internet of Things (IoT). It extends computing and caching functions to the edge of wireless networks. Uncrewed Aerial Vehicles (UAVs) provide adequate support for fog computing. UAVs can not only act as a relay between mobile users and physically remote edge devices to avoid costly long-range wireless communications but also are equipped with computing facilities that can take over specific tasks. In this paper, we aim to optimize the energy efficiency of a fog computing system assisted by a single UAV by planning the trajectories of the UAV and assigning computing tasks to different devices, including the UAV itself. We propose two algorithms based on the classical Ant Colony and Particle Swarm Optimization techniques and solve the problem by continuous convex approximation. Unlike most existing studies where the trajectories are assumed to be straight lines, we account for the effect of obstacles, such as buildings, and deliberately avoid them during the trajectory planning phase. Through extensive simulation experiments, we demonstrate that our proposed approach can achieve significantly better energy efficiency than existing benchmark algorithms.