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

TL;DR

This paper introduces a multi-objective optimization framework for multi-UAV-assisted mobile edge computing, jointly optimizing task offloading, resource allocation, and UAV trajectories to improve delay, energy efficiency, and task offloading.

Contribution

It proposes a novel joint optimization approach (JTORATC) that effectively solves a complex, coupled MINLP problem by splitting it into manageable sub-problems with tailored solutions.

Findings

JTORATC outperforms benchmark methods in simulations.

The approach effectively balances delay, energy consumption, and offloaded tasks.

Sub-problems are efficiently solved using specialized methods like KKT and SCA.

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.