Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing

TL;DR

The paper addresses latency-sensitive MEC in a multi-UAV setting by formulating a multi-objective optimization to minimize $T_{\mathrm{total}}$ and $E_{\mathrm{total}}$ while maximizing $K_{\mathrm{total}}$ through joint task offloading, computation resource allocation, and UAV trajectory control. It introduces JTORATC, which transforms the problem into a single objective $\rho$ with weights $w_1,w_2,w_3$ and solves three coupled subproblems via distributed splitting for offloading, KKT for resource allocation, and SCA for trajectory optimization, using threshold rounding and CVX-based solvers. The algorithm iteratively updates $\boldsymbol{A}$, $\boldsymbol{F}$, and $\boldsymbol{Q}$ with convergence guarantees and polynomial-time complexity, yielding scalable performance. Simulations demonstrate that JTORATC consistently outperforms several benchmarks in objective value, delay, and UAV energy consumption, and exhibits robustness across varying UAV capacities, workloads, and user counts, highlighting its practical relevance for flexible UAV-MEC deployments.

Abstract

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective optimization approach for multi-UAV-assisted MEC systems. First, we formulate a multi-objective optimization problem \textcolor{b2}{aiming} at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading, computation resource allocation, and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem which is challenging, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach to solve the problem. \textcolor{b3}{However, since the decision variables of task offloading, computation resource allocation, and UAV trajectory control are coupled with each other, the original problem is split into three sub-problems, i.e., task offloading, computation resource allocation, and UAV trajectory control, which are solved individually to obtain the corresponding decisions.} \textcolor{b2}{Moreover, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method.} Simulation results show that the proposed JTORATC has superior performance compared to the other benchmark methods.

Figures (6)

• Figure 1: Multi-UAV-assisted MEC for task offloading.
• Figure 2: System performance with UAV computation capacity. (a) Objective function value. (b) Total task completion delay. (c) Total UAV energy consumption. (d) Total amount of offloaded tasks.
• Figure 3: System performance with numbers of UAVs. (a) Objective function value. (b) Total task completion delay. (c) Total UAV energy consumption. (d) Total amount of offloaded tasks.
• Figure 4: System performance with task computation intensity. (a) Objective function value. (b) Total task completion delay. (c) Total UAV energy consumption. (d) Total amount of offloaded tasks.
• Figure 5: System performance with task size. (a) Objective function value. (b) Total task completion delay. (c) Total UAV energy consumption. (d) Total amount of offloaded tasks.

Theorems & Definitions (7)

• Theorem 1
• Remark 1
• Theorem 2
• Theorem 3
• Theorem 4
• Lemma 1
• Theorem 5