{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:09:22Z","timestamp":1727064562410},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T00:00:00Z","timestamp":1551052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.<\/jats:p>","DOI":"10.3390\/a12020048","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T08:06:52Z","timestamp":1551082012000},"page":"48","source":"Crossref","is-referenced-by-count":20,"title":["Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2317-5359","authenticated-orcid":false,"given":"Ming","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Software, Central South University, Tianxin District, Changsha 410075, China"}]},{"given":"Ke","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Software, Central South University, Tianxin District, Changsha 410075, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1109\/TPDS.2015.2506573","article-title":"Enabling personalized search over encrypted outsourced data with efficiency improvement","volume":"27","author":"Fu","year":"2016","journal-title":"IEEE Trans. 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