{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:22:00Z","timestamp":1763810520368,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11036-021-01751-3","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T12:02:34Z","timestamp":1618920154000},"page":"1347-1358","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Distributed Power Controller of Massive Wireless Body Area Networks based on Deep Reinforcement Learning"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4288-1649","authenticated-orcid":false,"given":"Peng","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhui","family":"Lan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengnan","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linhai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhidu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"issue":"3","key":"1751_CR1","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1109\/SURV.2013.121313.00064","volume":"16","author":"S Movassaghi","year":"2014","unstructured":"Movassaghi S, Abolhasan M, Lipman J, Smith D, Jamalipour A (2014) Wireless body area networks: A survey. IEEE Commun Surveys Tutor 16(3):1658\u20131686","journal-title":"IEEE Commun Surveys Tutor"},{"issue":"6","key":"1751_CR2","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MWC.001.1900108","volume":"26","author":"R Wang","year":"2019","unstructured":"Wang R, Liu H, Wang H, Yang Q, Wu D (2019) Distributed security architecture based on blockchain for connected health: architecture, challenges, and approaches. IEEE Wirel Commun 26(6):30\u201336","journal-title":"IEEE Wirel Commun"},{"issue":"4","key":"1751_CR3","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1109\/COMST.2019.2916583","volume":"21","author":"NC Luong","year":"2019","unstructured":"Luong NC, Hoang DT, Gong S, Niyato D, Wang P, Liang YC, Kim DI (2019) Applications of deep reinforcement learning in communications and networking: A survey. IEEE Commun Surv Tutor 21(4):3133\u20133174","journal-title":"IEEE Commun Surv Tutor"},{"key":"1751_CR4","doi-asserted-by":"publisher","first-page":"101655","DOI":"10.1016\/j.sysarc.2019.101655","volume":"101","author":"M Hussain","year":"2019","unstructured":"Hussain M, Mehmood A, Khan S, Khan MA, Iqbal Z (2019) A survey on authentication techniques for wireless body area networks. J Syst Archit 101:101655","journal-title":"J Syst Archit"},{"issue":"6","key":"1751_CR5","doi-asserted-by":"publisher","first-page":"9266","DOI":"10.1109\/JIOT.2018.2888543","volume":"6","author":"D Wu","year":"2019","unstructured":"Wu D, Zhang Z, Wu S, Yang J, Wang R (2019) Biologically inspired resource allocation for network slices in 5g-enabled internet of things. IEEE Internet Things J 6(6):9266\u20139279","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"1751_CR6","first-page":"224","volume":"10","author":"N Javaid","year":"2014","unstructured":"Javaid N, Abbas Z, Fareed MS, Khan ZA, Alrajeh N (2014) RE-ATTEMPT: A new energy-efficient routing protocol for wireless body area sensor networks. Procedia Comput Sci 10(4):224\u2013231","journal-title":"Procedia Comput Sci"},{"key":"1751_CR7","doi-asserted-by":"publisher","first-page":"11413","DOI":"10.1109\/ACCESS.2017.2716344","volume":"5","author":"T Wu","year":"2017","unstructured":"Wu T, Wu F, Redoute JM, Yuce MR (2017) An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5:11413\u201311422","journal-title":"IEEE Access"},{"key":"1751_CR8","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/s13638-019-1415-3","volume":"1","author":"M Mohamed","year":"2019","unstructured":"Mohamed M, Joseph W, Vermeeren G, Tanghe E, Cheffena M (2019) Characterization of dynamic wireless body area network channels during walking. EURASIP J Wirel Commun Netw 1:104","journal-title":"EURASIP J Wirel Commun Netw"},{"issue":"8","key":"1751_CR9","doi-asserted-by":"publisher","first-page":"5497","DOI":"10.1109\/TWC.2016.2560820","volume":"15","author":"H Moosavi","year":"2016","unstructured":"Moosavi H, Bui FM (2016) Optimal relay selection and power control with quality-of-service provisioning in wireless body area networks. IEEE Trans Wirel Commun 15(8):5497\u20135510","journal-title":"IEEE Trans Wirel Commun"},{"key":"1751_CR10","doi-asserted-by":"crossref","unstructured":"Yang Y, Smith DB, Seneviratne S (2019) Deep learning channel prediction for transmit power control in wireless body area networks. In: 2019 International conference on communications (ICC). IEEE, pp 1\u20136","DOI":"10.1109\/ICC.2019.8761432"},{"key":"1751_CR11","doi-asserted-by":"crossref","unstructured":"Kazemi R, Vesilo R, Dutkiewicz E, Liu R (2011) Dynamic power control in wireless body area networks using reinforcement learning with approximation. In: 2011 International symposium on personal, indoor and mobile radio communications. IEEE, pp 2203\u20132208","DOI":"10.1109\/PIMRC.2011.6139908"},{"issue":"12","key":"1751_CR12","doi-asserted-by":"publisher","first-page":"12152","DOI":"10.1109\/TVT.2018.2872960","volume":"67","author":"Z Liu","year":"2018","unstructured":"Liu Z, Liu B, Chen CW (2018) Joint power-rate-slot resource allocation in energy harvesting-powered wireless body area networks. IEEE Trans Vehic Technol 67(12):12152\u201312164","journal-title":"IEEE Trans Vehic Technol"},{"key":"1751_CR13","doi-asserted-by":"crossref","unstructured":"Li S, Hu F, Xu Z, Mao Z, Ling Z, Liu H (2020) Joint power allocation in classified WBANs with wireless information and power transfer. IEEE Internet of Things Journal, early access","DOI":"10.1109\/JIOT.2020.3010950"},{"key":"1751_CR14","doi-asserted-by":"crossref","unstructured":"Wu D, Yan J, Wang H, Wang R (2020) User-centric edge sharing mechanism in software-defined ultra-dense networks. IEEE Journal on Selected Areas in Communications, early access","DOI":"10.1109\/JSAC.2020.2986871"},{"issue":"1","key":"1751_CR15","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/TNSE.2018.2865183","volume":"7","author":"Y He","year":"2018","unstructured":"He Y, Liang C, Yu R, Han Z (2018) Trust-based social networks with computing, caching and communications: A deep reinforcement learning approach. IEEE Trans Netw Sci Eng 7(1):66\u201379","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"1751_CR16","unstructured":"He X, Wang K, Huang H, Miyazaki T, Wang Y, Guo S (2018) Green resource allocation based on deep reinforcement learning in content-centric IoT. IEEE Transactions on Emerging Topics in Computing, early access"},{"key":"1751_CR17","volume-title":"Markov decision processes: discrete stochastic dynamic programming","author":"ML Puterman","year":"2014","unstructured":"Puterman ML (2014) Markov decision processes: discrete stochastic dynamic programming. Wiley, New York"},{"key":"1751_CR18","unstructured":"Hausknecht M, Stone P (2015) Deep recurrent q-learning for partially observable mdps. In: AAAI Fall symposium series"},{"key":"1751_CR19","unstructured":"Dabney WC (2014) DAdaptive step-sizes for reinforcement learning. In: Ph.D. dissertation"},{"issue":"3-4","key":"1751_CR20","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF00992698","volume":"8","author":"CJ Watkins","year":"1992","unstructured":"Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 8(3-4):279\u20138C292","journal-title":"Mach Learn"},{"issue":"Nov","key":"1751_CR21","first-page":"1039","volume":"4","author":"J Hu","year":"2003","unstructured":"Hu J, Wellman MP (2003) Nash q-learning for general-sum stochastic games. J Mach Learn Res 4(Nov):1039\u20131069","journal-title":"J Mach Learn Res"},{"key":"1751_CR22","doi-asserted-by":"crossref","unstructured":"Mismar FB, Brian LE, Ahmed A (2019) Deep reinforcement learning for 5G networks: joint beamforming: Power control, and interference coordination. arXiv:1907.00123","DOI":"10.1109\/TCOMM.2019.2961332"},{"key":"1751_CR23","volume-title":"Membranes, ions, and impulses: A chapter of classical biophysics, vol 5","author":"KS Cole","year":"1972","unstructured":"Cole KS (1972) Membranes, ions, and impulses: A chapter of classical biophysics, vol 5. University of California Press, Berkeley"},{"issue":"11","key":"1751_CR24","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1088\/0031-9155\/41\/11\/002","volume":"41","author":"S Gabriel","year":"1996","unstructured":"Gabriel S, Lau RW, Gabriel C (1996) The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Phys Med Biol 41(11):2251\u20138C2269","journal-title":"Phys Med Biol"},{"key":"1751_CR25","doi-asserted-by":"crossref","unstructured":"He P, Liu Z, Fu L, Tao Z, Liu J, Tang T, Li Z (2020) Intelligent power controller of wireless body area networks based on deep reinforcement learning. In: International conference on bio-inspired information and communications technologies","DOI":"10.1007\/978-3-030-57115-3_21"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-021-01751-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-021-01751-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-021-01751-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,25]],"date-time":"2021-07-25T15:09:36Z","timestamp":1627225776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-021-01751-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,20]]},"references-count":25,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1751"],"URL":"https:\/\/doi.org\/10.1007\/s11036-021-01751-3","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"type":"print","value":"1383-469X"},{"type":"electronic","value":"1572-8153"}],"subject":[],"published":{"date-parts":[[2021,4,20]]},"assertion":[{"value":"30 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}