{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:17:05Z","timestamp":1760710625544,"version":"3.40.5"},"reference-count":30,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mobile Information Systems"],"published-print":{"date-parts":[[2021,7,2]]},"abstract":"<jats:p>Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server\u2019s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>k<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula> subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>k<\/c:mi>\n                     <\/c:math>\n                  <\/jats:inline-formula> number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.<\/jats:p>","DOI":"10.1155\/2021\/9993946","type":"journal-article","created":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T00:19:33Z","timestamp":1625271573000},"page":"1-13","source":"Crossref","is-referenced-by-count":4,"title":["Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4249-0696","authenticated-orcid":true,"given":"Abdullah","family":"Numani","sequence":"first","affiliation":[{"name":"Telecommunications and Networking (TeleCoN) Research Lab, GIK Institute of Engineering Sciences and Technology, Topi, Swabi 23640, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3862-8066","authenticated-orcid":true,"given":"Zaiwar","family":"Ali","sequence":"additional","affiliation":[{"name":"Telecommunications and Networking (TeleCoN) Research Lab, GIK Institute of Engineering Sciences and Technology, Topi, Swabi 23640, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4466-0089","authenticated-orcid":true,"given":"Ziaul Haq","family":"Abbas","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Swabi 23640, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3836-1373","authenticated-orcid":true,"given":"Ghulam","family":"Abbas","sequence":"additional","affiliation":[{"name":"Faculty of Computer Sciences and Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Swabi 23640, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5166-4873","authenticated-orcid":true,"given":"Thar","family":"Baker","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Sharjah, Sharjah, UAE"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9170-0568","authenticated-orcid":true,"given":"Dhiya","family":"Al-Jumeily","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Liverpool John Moores University, Liverpool, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"doi-asserted-by":"publisher","key":"1","DOI":"10.3390\/s20144047"},{"doi-asserted-by":"publisher","key":"2","DOI":"10.1109\/vtcspring.2016.7504110"},{"doi-asserted-by":"publisher","key":"3","DOI":"10.1016\/j.comnet.2020.107496"},{"doi-asserted-by":"publisher","key":"4","DOI":"10.1109\/access.2020.3028240"},{"doi-asserted-by":"publisher","key":"5","DOI":"10.1155\/2019\/8049804"},{"doi-asserted-by":"publisher","key":"6","DOI":"10.1109\/tvt.2019.2904244"},{"issue":"2","key":"7","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.32604\/cmc.2020.013743","article-title":"A comprehensive utility function for resource allocation in mobile edge computing","volume":"66","author":"Z. 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