Tradeoff of Computation Bits and Computing Speed in an Edge Computing System for Sensor Networks

Jiaying Huang,Fahui Wu,Yawen Shi,Gengxin Sun

doi:10.1155/2021/5775953

Abstract

Unmanned aerial vehicle (UAV) enabled mobile-edge computing (MEC) has been recognized as a promising approach for providing enhanced coverage and computation capability to Internet of Things (IoT), especially in the scenario with limited or without infrastructure. In this paper, we consider the UAV assisted partial computation offloading mode MEC system, where ground sensor users are served by a moving UAV equipped with computing server. Computation bits (CB) and computation efficiency (CE) are two vital metrics describe the computation performance of system. To reveal the CB-CE tradeoff, an optimization problem is formulated to maximize the weighted sum of the above two metrics, by optimizing the UAV trajectory jointly with communication resource, as well as the computation resource. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory optimization and resource allocation optimization. We propose an iterative algorithm to solve the two sub-problems by Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Extensive simulation results demonstrate that our proposed resource allocation optimization scheme can achieve better computational performance than the other schemes. Moreover, the proposed alternative algorithm can converge with a few iterations.

Highlights

With the increasing popularity of computer terminals and the emergence of new applications, mobile data traffic continues to grow at a high speed, and users’ demands for computing power and quality of experience (QoE) are increasing [1]
1) The Unmanned aerial vehicle (UAV) flies from the initial position to final position follow a straight trajectory
2) The UAV flies from the initial position to final position follow a semi-circle trajectory

Summary

Introduction

With the increasing popularity of computer terminals and the emergence of new applications (e.g. online games, face recognition, smart home, etc.), mobile data traffic continues to grow at a high speed, and users’ demands for computing power and quality of experience (QoE) are increasing [1]. The limited battery lifetime and low computing capacity make it difficult for mobile terminal to provide good QoE [2]. Mobile edge computing (MEC) has received widely attention as an advanced technology that can overcome these challenges [3]. The MEC serves are at the edge of the wireless sensor network, providing communication, computing, storage and other services to a large number of end users who are tightly deployed [2]. Terrestrial MEC systems have limitations in application scenario, such as infrastructures are destroyed due to natural disasters [4,5,6]

Objectives

Results

Conclusion