{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:29:27Z","timestamp":1771025367370,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of the People\u2032s Republic of China","award":["2018YFB2100100"],"award-info":[{"award-number":["2018YFB2100100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mobile edge computing (MEC) has become an effective solution for insufficient computing and communication problems for the Internet of Things (IoT) applications due to its rich computing resources on the edge side. In multi-terminal scenarios, the deployment scheme of edge nodes has an important impact on system performance and has become an essential issue in end\u2013edge\u2013cloud architecture. In this article, we consider specific factors, such as spatial location, power supply, and urgency requirements of terminals, with respect to building an evaluation model to solve the allocation problem. An evaluation model based on reward, energy consumption, and cost factors is proposed. The genetic algorithm is applied to determine the optimal edge node deployment and allocation strategies. Moreover, we compare the proposed method with the k-means and ant colony algorithms. The results show that the obtained strategies achieve good evaluation results under problem constraints. Furthermore, we conduct comparison tests with different attributes to further test the performance of the proposed method.<\/jats:p>","DOI":"10.3390\/s22186719","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Deployment and Allocation Strategy for MEC Nodes in Complex Multi-Terminal Scenarios"],"prefix":"10.3390","volume":"22","author":[{"given":"Danyang","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1829-4719","authenticated-orcid":false,"given":"Yuxing","family":"Mao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"given":"Xueshuo","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"}]},{"given":"Siyang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China"},{"name":"Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Yundaxilu, Kunming 650217, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","article-title":"Deep learning in mobile and wireless networking: A survey.IEEE","volume":"21","author":"Zhang","year":"2019","journal-title":"Commun. 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