Paper Info
Title
HNIO: A Hybrid Nature-Inspired Optimization Algorithm for Energy Minimization in UAV-Assisted Mobile Edge Computing
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.
Year
DOI
Venue
2022
10.1109/TNSM.2022.3176829
IEEE Transactions on Network and Service Management
Keywords
DocType
Volume
Mobile edge computing,UAV,nature-inspired algorithms,computational task offloading,discrete optimization,convergence analysis
Journal
19
Issue
ISSN
Citations 
3
1932-4537
0
PageRank 
References 
Authors
0.34
31
5
Name
Order
Citations
PageRank
Lydia Y. Chen143252.24
d pi200.34
Shengxiang Yang33703185.98
y xu400.34
Kevin J. Chen5117.09