HNIO: A Hybrid Nature-Inspired Optimization Algorithm for Energy Minimization in UAV-Assisted Mobile Edge Computing

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...

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Veröffentlicht in:	IEEE eTransactions on network and service management Jg. 19; H. 3; S. 3264 - 3275
Hauptverfasser:	Chen, Yang, Pi, Dechang, Yang, Shengxiang, Xu, Yue, Chen, Junfu, Mohamed, Ali Wagdy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Autonomous aerial vehicles Computational modeling computational task offloading Computing time Continuity (mathematics) convergence analysis Design optimization discrete optimization Edge computing Energy consumption Global optimization Mobile computing Mobile edge computing Mutation nature-inspired algorithms Optimization Optimization algorithms Radio equipment Servers Sociology Statistics Task analysis UAV
ISSN:	1932-4537, 1932-4537
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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.
AbstractList	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.
Author	Chen, Yang Chen, Junfu Yang, Shengxiang Pi, Dechang Mohamed, Ali Wagdy Xu, Yue
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SubjectTerms	Algorithms Autonomous aerial vehicles Computational modeling computational task offloading Computing time Continuity (mathematics) convergence analysis Design optimization discrete optimization Edge computing Energy consumption Global optimization Mobile computing Mobile edge computing Mutation nature-inspired algorithms Optimization Optimization algorithms Radio equipment Servers Sociology Statistics Task analysis UAV
Title	HNIO: A Hybrid Nature-Inspired Optimization Algorithm for Energy Minimization in UAV-Assisted Mobile Edge Computing
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