Particle Swarm Optimization with Latin Hypercube Sampling for Mobility-Aware Task Offloading in D2D-Assisted MEC Networks

Particle Swarm Optimization with Latin Hypercube Sampling for Mobility-Aware Task Offloading in D2D-Assisted MEC Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Particle Swarm Optimization with Latin Hypercube Sampling for Mobility-Aware Task Offloading in D2D-Assisted MEC Networks Changping Song, Jun Tao, Yu Gao, Yifan Xu, Weice Sun, Haotian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4105847/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this paper, we consider a Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) system in which the edge server collaboratively performs tasks with users who possess mobile characteristics. The computational resources of the edge server and the users are limited. Our objective is to minimize latency and energy consumption while maximizing system stability by determining the offloading decisions for all tasks. However, solving this problem is particularly challenging due to the coupling between offloading decisions and user mobility characteristics. To address this challenge, a Particle Swarm Optimization (PSO) algorithm based on Latin Hypercube Sampling (LHS) is proposed. PSO can be employed for iterative computation to obtain the optimal solution by treating the offloading decisions for all tasks as particles. Furthermore, employing LHS for the initialization and updating of the particle swarm can significantly enhance the performance and convergence of the PSO. Simulation results indicate that our proposed algorithm outperforms other algorithms significantly in terms of latency, energy consumption, and system stability while also exhibiting excellent robustness. Task offloading D2D Mobile edge computing Particle swarm optimization Full Text Additional Declarations No competing interests reported. Supplementary Files ParticleSwarmOptimizationwithLatinHypercubeSamplingforMobilityAwareTaskOffloadinginD2DAssistedMECNetworks.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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