{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T07:28:43Z","timestamp":1761636523560,"version":"build-2065373602"},"reference-count":27,"publisher":"Institution of Engineering and Technology (IET)","issue":"10","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51677065"],"award-info":[{"award-number":["51677065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Communications"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In order to maximize the work efficiency of wireless mobile charger, a payoff\u2010maximization\u2010based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi\u2010node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush\u2013Kuhn\u2013Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade\u2010off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.<\/jats:p>","DOI":"10.1049\/cmu2.12307","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T10:20:35Z","timestamp":1637058035000},"page":"1218-1229","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Payoff\u2010maximization\u2010based adaptive hierarchical wireless charging algorithm for mobile charger in IoT"],"prefix":"10.1049","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8032-355X","authenticated-orcid":false,"given":"Guo","family":"Haobo","sequence":"first","affiliation":[{"name":"The Department of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Electrical &amp; Electronic Engineering North China Electric Power University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Runze","sequence":"additional","affiliation":[{"name":"The Department of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Electrical &amp; Electronic Engineering North China Electric Power University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Bing","sequence":"additional","affiliation":[{"name":"The Department of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Electrical &amp; Electronic Engineering North China Electric Power University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Bing","sequence":"additional","affiliation":[{"name":"The Department of State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Electrical &amp; Electronic Engineering North China Electric Power University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"issue":"99","key":"e_1_2_11_2_1","first-page":"1","article-title":"Collaborative learning\u2010based industrial IoT API recommendation for software\u2010defined devices: The implicit knowledge discovery perspective","author":"Gao H.","year":"2020","journal-title":"IEEE Trans. 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