iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1109/TII.2020.3016320
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:05:16Z","timestamp":1732039516868},"reference-count":21,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61941102"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xi'an Key Laboratory of Mobile Edge Computing and Security","award":["201805052ZD3CG36"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1109\/tii.2020.3016320","type":"journal-article","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T20:47:27Z","timestamp":1597351647000},"page":"4968-4977","source":"Crossref","is-referenced-by-count":188,"title":["Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks"],"prefix":"10.1109","volume":"17","author":[{"given":"Yueyue","family":"Dai","sequence":"first","affiliation":[]},{"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Sabita","family":"Maharjan","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2017.2717986"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2959300"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1900029"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2973705"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2903191"},{"key":"ref15","first-page":"525","article-title":"Backscatter-aided hybrid data offloading for mobile edge computing via deep reinforcement learning","author":"xie","year":"0","journal-title":"Proc Int Conf Mach Learn Intell Commun"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3013990"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2200\/S00271ED1V01Y201006CNT007"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2012.2230336"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IJCIME49369.2019.00095"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3010798"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700882"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2899679"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2705720"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2892767"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2876298"},{"key":"ref2","article-title":"The digital twin paradigm for future nasa and US air force vehicles","author":"glaessgen","year":"0","journal-title":"Proc 53\ufffdrd AIAA\/ASME\/ASCE\/AHS\/ASC Structures"},{"key":"ref1","article-title":"The growth in connected IoT devices is expected to generate 79.4 zb of data in 2025, according to a new IDC forecast","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2876804"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2017.2686839"},{"key":"ref21","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","author":"mnih","year":"0","journal-title":"Proc 33rd Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/9395328\/09166745.pdf?arnumber=9166745","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:52:17Z","timestamp":1652194337000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9166745\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":21,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tii.2020.3016320","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}