HINO: An optimization algorithm for energy minimization of UAV-assisted mobile edge computing

Date

2022-05-20

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

Abstract

Mobile edge computing (MEC) is an emerging computing paradigm that decreases the computing time and extends the lifespan of user equipments (UEs). In MEC, the computational tasks are offloaded from UEs to the base station (BS) at the edge of the network for processing. However, MEC cannot cope with environments where there are no BS or where communication facilities have been destroyed. In this paper, we study the problem of minimizing the energy consumption of UAV equipped with MEC servers as a mobile base station to serve users. The problem involves user offloading decision, UAV location and allocation with computational resources, and is a hybrid optimization problem with continuous and discrete variables. To address this problem, we propose a hybrid nature-inspired optimization algorithm (HNIO) and its version for discrete optimization, where HNIO incorporates mutation and population diversity detection mechanisms to boost its global optimization capability, and we design a probabilistic selection-based coding strategy for the discrete optimization version. The experimental study is conducted based on ten cases with different numbers of UEs. Comparing HNIO with several other state-of-the-art optimization algorithms, it is concluded from the Friedman and Wilcoxon’s test of the experimental results that HNIO shows better precision and stability in nine out of the ten cases with higher number of UEs.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Mobile edge computing, UAV, Nature-inspired algorithms, Computational task offloading, Discrete optimization, Convergence analysis

Citation

Y. Chen, D. Pi, S. Yang, Y. Xu, J, Chen, and A. W. Mohamed. (2022) HINO: An optimization algorithm for energy minimization of UAV-assisted mobile edge computing. IEEE Transactions on Network and Service Management, 19 (3), pp. 3264 - 3275

Rights

Research Institute