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

Shuaijun Liu, Jinqiu Du, Yaxin Zheng, Jiaying Yin, Yuhui Deng, Jingjin Wu

TL;DR

This work tackles the energy-aware joint optimization of attitude control, 3D trajectory planning, and task/resource allocation in UAV-assisted fog computing over terrain-rich environments. It introduces FEAR-PID for robust attitude control, ACS-DS for deadlock-resistant 3D trajectory planning with safety values, and a PSO-based scheme for task assignment and resource allocation, all coordinated to minimize the aggregate cost $\mathbb{S}=\sum_{t=0}^T[\mathbb{D}(t)+\epsilon\mathbb{E}(t)]$. The framework demonstrates substantial gains in latency and energy efficiency over baseline methods, with ablation studies confirming the necessity and synergy of each module. Practically, the approach enables more reliable, energy-efficient UAV fog networks in dynamic IoT settings, facilitating real-time data processing and edge computing under realistic terrain constraints.

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.

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

TL;DR

This work tackles the energy-aware joint optimization of attitude control, 3D trajectory planning, and task/resource allocation in UAV-assisted fog computing over terrain-rich environments. It introduces FEAR-PID for robust attitude control, ACS-DS for deadlock-resistant 3D trajectory planning with safety values, and a PSO-based scheme for task assignment and resource allocation, all coordinated to minimize the aggregate cost . The framework demonstrates substantial gains in latency and energy efficiency over baseline methods, with ablation studies confirming the necessity and synergy of each module. Practically, the approach enables more reliable, energy-efficient UAV fog networks in dynamic IoT settings, facilitating real-time data processing and edge computing under realistic terrain constraints.

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.
Paper Structure (47 sections, 2 theorems, 28 equations, 14 figures, 7 tables, 2 algorithms)

This paper contains 47 sections, 2 theorems, 28 equations, 14 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Attitude Control
  4. Trajectory Planning
  5. Task Assignment
  6. Joint Optimization
  7. System Model
  8. Network Structure and Components
  9. The Controllable Structure of a Quadrotor UAV
  10. Rotor Propeller Design of Quadrotor UAV
  11. Transmission Model
  12. Energy Consumption Model
  13. UAV movement
  14. Energy consumption for transmission
  15. Energy consumption for computation
  16. ...and 32 more sections

Key Result

Proposition 1

In Algorithm Algorithm2, for the guidance factor $\sigma_{\mu\nu}$ on any edge $(\mu,\nu)$ generated by the ants during the searching process, there exists a maximum value $g(s^*)$ as $h \to \infty$.

Figures (14)

  • Figure 1: The structure of UAV-assisted fog computing network.
  • Figure 2: Schematic diagram of a quadrotor UAV in motion, where subscripts $e$ and $b$ denote the earth and body frames, respectively, and $(x_e,y_e,z_e)$ and $(x_b,y_b,z_b)$ denote their coordinate axes associated with the translational directions $x,y,z$.
  • Figure 3: Schematic of quadrotor UAV propeller hardware.
  • Figure 4: Propeller radius in relationship to wheelbase for quadrotor UAV.
  • Figure 5: Schematic structure of FEAR-PID control system for UAV.
  • ...and 9 more figures

Theorems & Definitions (4)

  • Proposition 1
  • proof
  • Corollary 1
  • proof