Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

TL;DR

This paper proposes an energy-efficient joint optimization framework for UAV-assisted mobile edge computing in IoT, integrating task offloading, trajectory, and resource allocation to minimize total energy consumption.

Contribution

It introduces a novel optimization model for UAV-assisted MEC that jointly considers computation, communication, and flight energy, with an efficient near-optimal solution approach.

Findings

Proposed algorithm reduces total energy consumption effectively.

Near-optimal solutions are obtained within a dozen iterations.

Numerical results show the approach outperforms benchmark schemes.

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is non-convex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.