Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and...

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Autores principales:	Tawfiq Hasanin, Aisha Alsobhi, Adil Khadidos, Ayman Qahmash, Alaa Khadidos, Gabriel Ayodeji Ogunmola
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a0a5743d44dc4e2db2071a09f3a3ad4e
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spelling	oai:doaj.org-article:a0a5743d44dc4e2db2071a09f3a3ad4e2021-11-22T01:10:43ZEfficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm1754-210310.1155/2021/9014559https://doaj.org/article/a0a5743d44dc4e2db2071a09f3a3ad4e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9014559https://doaj.org/toc/1754-2103Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.Tawfiq HasaninAisha AlsobhiAdil KhadidosAyman QahmashAlaa KhadidosGabriel Ayodeji OgunmolaHindawi LimitedarticleBiotechnologyTP248.13-248.65Biology (General)QH301-705.5ENApplied Bionics and Biomechanics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5
spellingShingle	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5 Tawfiq Hasanin Aisha Alsobhi Adil Khadidos Ayman Qahmash Alaa Khadidos Gabriel Ayodeji Ogunmola Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm
description	Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.
format	article
author	Tawfiq Hasanin Aisha Alsobhi Adil Khadidos Ayman Qahmash Alaa Khadidos Gabriel Ayodeji Ogunmola
author_facet	Tawfiq Hasanin Aisha Alsobhi Adil Khadidos Ayman Qahmash Alaa Khadidos Gabriel Ayodeji Ogunmola
author_sort	Tawfiq Hasanin
title	Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm
title_short	Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm
title_full	Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm
title_fullStr	Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm
title_full_unstemmed	Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm
title_sort	efficient multiuser computation for mobile-edge computing in iot application using optimization algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a0a5743d44dc4e2db2071a09f3a3ad4e
work_keys_str_mv	AT tawfiqhasanin efficientmultiusercomputationformobileedgecomputinginiotapplicationusingoptimizationalgorithm AT aishaalsobhi efficientmultiusercomputationformobileedgecomputinginiotapplicationusingoptimizationalgorithm AT adilkhadidos efficientmultiusercomputationformobileedgecomputinginiotapplicationusingoptimizationalgorithm AT aymanqahmash efficientmultiusercomputationformobileedgecomputinginiotapplicationusingoptimizationalgorithm AT alaakhadidos efficientmultiusercomputationformobileedgecomputinginiotapplicationusingoptimizationalgorithm AT gabrielayodejiogunmola efficientmultiusercomputationformobileedgecomputinginiotapplicationusingoptimizationalgorithm
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Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

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