Energy-Aware Holistic Optimization in UAV-Assisted Fog Computing: Attitude, Trajectory, Task Assignment

TL;DR

This paper presents a comprehensive framework for optimizing UAV-assisted fog computing by jointly controlling attitude, trajectory, resource allocation, and task assignment to minimize latency and energy use.

Contribution

It introduces a novel integrated approach combining fuzzy-enhanced reinforcement control, an improved Ant Colony System, and Particle Swarm Optimization for holistic UAV system optimization.

Findings

Significant reduction in latency and energy consumption compared to existing methods.

Effective joint optimization of attitude, trajectory, and task assignment modules.

Demonstrated robustness and efficiency through extensive simulations.

Abstract

Unmanned Aerial Vehicles (UAVs) have significantly enhanced fog computing by acting as both flexible computation platforms and communication mobile relays. In this paper, we consider four important and interdependent modules: attitude control, trajectory planning, resource allocation, and task assignment, and propose a holistic framework that jointly optimizes the total latency and energy consumption for UAV-assisted fog computing in a three-dimensional spatial domain with varying terrain elevations and dynamic task generations. We first establish a fuzzy-enhanced adaptive reinforcement proportional-integral-derivative control model to control the attitude. Then, we propose an enhanced Ant Colony System (ACS) based algorithm, that includes a safety value and a decoupling mechanism to overcome the convergence issue in classical ACS, to compute the optimal UAV trajectory. Finally, we design an algorithm based on the Particle Swarm Optimization technique, to determine where each offloaded task should be executed. Under our proposed framework, the outcome of one module would affect the decision-making in another, providing a holistic perspective of the system and thus leading to improved solutions. We demonstrate by extensive simulation results that our proposed framework can significantly improve the overall performance, measured by latency and energy consumption, compared to existing mainstream approaches.