Robust and Secure Computation Offloading and Trajectory Optimization for Multi-UAV MEC Against Aerial Eavesdropper

2025-09-18

AI Summary

This paper addresses the challenges of aerial eavesdroppers, task uncertainty, and flexible trajectory optimization in multi-UAV mobile edge computing (MEC) systems. Method: We propose a joint secure task offloading and robust trajectory optimization framework. Task uncertainty is modeled via a novel integration of distributionally robust optimization (DRO) and conditional value-at-risk (CVaR), enabling a latency-sensitive, security-aware, and energy-efficient co-optimization. A globally convergent block coordinate descent algorithm is developed, leveraging second-order cone programming (SOCP) and successive convex approximation (SCA) to handle non-convex, nonlinear constraints. Contribution/Results: Simulation results demonstrate that the proposed method achieves significant improvements in secrecy rate and task completion ratio, with only a 2% increase in energy consumption. It outperforms baseline schemes in robustness and strikes an effective trade-off among security, energy efficiency, and computational performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The unmanned aerial vehicle (UAV) based multi-access edge computing (MEC) appears as a popular paradigm to reduce task processing latency. However, the secure offloading is an important issue when occurring aerial eavesdropping. Besides, the potential uncertainties in practical applications and flexible trajectory optimizations of UAVs pose formidable challenges for realizing robust offloading. In this paper, we consider the aerial secure MEC network including ground users, service unmanned aerial vehicles (S-UAVs) integrated with edge servers, and malicious UAVs overhearing transmission links. To deal with the task computation complexities, which are characterized as uncertainties, a robust problem is formulated with chance constraints. The energy cost is minimized by optimizing the connections, trajectories of S-UAVs and offloading ratios. Then, the proposed non-linear problem is tackled via the distributionally robust optimization and conditional value-at-risk mechanism, which is further transformed into the second order cone programming forms. Moreover, we decouple the reformulated problem and design the successive convex approximation for S-UAV trajectories. The global algorithm is designed to solve the sub-problems in a block coordinate decent manner. Finally, extensive simulations and numerical analyses are conducted to verify the robustness of the proposed algorithms, with just 2% more energy cost compared with the ideal circumstance.

Problem

• Robust computation offloading for UAV MEC under uncertainties
• Secure task transmission against aerial eavesdropping threats
• Joint optimization of UAV trajectories and offloading ratios

Innovation

• Distributionally robust optimization for uncertainty handling
• Successive convex approximation for trajectory optimization
• Second order cone programming transformation