{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:28:54Z","timestamp":1784996934797,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011447","name":"project of Research on Key Technologies for water and sediment simulation and intelligent decision of Yellow River","doi-asserted-by":"publisher","award":["201400211000"],"award-info":[{"award-number":["201400211000"]}],"id":[{"id":"10.13039\/501100011447","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users\u2019 historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user\u2019s moving speed, when the speed is less 12.96 m\/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.<\/jats:p>","DOI":"10.3390\/e24101464","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:04:55Z","timestamp":1665965095000},"page":"1464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3473-4099","authenticated-orcid":false,"given":"Pu","family":"Han","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"},{"name":"National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450000, China"},{"name":"Nanyang Institute of Technology, No.80, Changjiang Road, Nanyang 473000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Han","sequence":"additional","affiliation":[{"name":"National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8401-321X","authenticated-orcid":false,"given":"Bo","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Informatics, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeng-Shyang","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiandong","family":"Shang","sequence":"additional","affiliation":[{"name":"National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aci.2016.11.002","article-title":"Mobile cloud computing for computation offloading: Issues and challenges","volume":"14","author":"Akherfi","year":"2018","journal-title":"Appl. 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