{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:24:01Z","timestamp":1768415041751,"version":"3.49.0"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,11,1]],"date-time":"2020-11-01T00:00:00Z","timestamp":1604188800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"India\u2013Canada Centre for Innovative Multidisciplinary Partnership to Accelerate Community Transformation and Sustainability","award":["11R18083"],"award-info":[{"award-number":["11R18083"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Syst. Man Cybern, Syst."],"published-print":{"date-parts":[[2020,11]]},"DOI":"10.1109\/tsmc.2019.2920390","type":"journal-article","created":{"date-parts":[[2019,6,17]],"date-time":"2019-06-17T19:26:35Z","timestamp":1560799595000},"page":"4197-4211","source":"Crossref","is-referenced-by-count":23,"title":["Deep Reinforced Learning Tree for Spatiotemporal Monitoring With Mobile Robotic Wireless Sensor Networks"],"prefix":"10.1109","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7152-8230","authenticated-orcid":false,"given":"Jiahong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4321-2615","authenticated-orcid":false,"given":"Tongxin","family":"Shu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9042-9211","authenticated-orcid":false,"given":"Teng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5871-639X","authenticated-orcid":false,"given":"Clarence W.","family":"de Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2318282"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2017.2658601"},{"key":"ref3","first-page":"235","article-title":"Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies","volume":"9","author":"Krause","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913488427"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511546877"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/s17081735"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21722"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2004.03.007"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1325651.1325655"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2007.1046"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2012.11.018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2013.2258336"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102385"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2674"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2014.6846750"},{"key":"ref16","volume-title":"Statistics for Spatial Data","author":"Cressie","year":"2015"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2015.2503382"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21767"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2014.6942706"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2017.8123205"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911406761"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2596772"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2015.2500027"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2874393"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.1998.712192"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2017.7510466"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2018.7511144"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907494"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390187"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/nn.2304"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596468"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/11564096_32"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2358639"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref36","article-title":"Deep reinforcement learning: An overview","volume":"abs\/1701.07274","author":"Li","year":"2017","journal-title":"Comput. Res. Repository"},{"key":"ref37","article-title":"Playing Atari with deep reinforcement learning","volume":"abs\/1312.5602","author":"Mnih","year":"2013","journal-title":"Comput. Res. Repository"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref39","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2649387.2649442"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3912"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989182"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989184"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2011.08.005"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2018.2820085"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3390\/s17112551"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2911295"},{"key":"ref51","volume-title":"Interpolation of Spatial Data: Some Theory for Kriging","author":"Stein","year":"2012"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2009.2034192"},{"key":"ref53","volume-title":"System Identification Toolbox 7: Getting Started Guide","author":"Ljung","year":"2008"},{"key":"ref54","volume-title":"Estimate State-Space Model Using Subspace Method\u2014MATLAB n4sid","year":"2017"},{"key":"ref55","volume-title":"Naval Oceanographic Office Regional Navy Coastal Ocean Model (NCOM)","year":"2017"},{"key":"ref56","volume-title":"Coastal Water Temperature Guide","year":"2018"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-87-3-327"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1115\/1.3662552"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2014.6859098"},{"key":"ref60","volume-title":"Data Mining: Concepts and Techniques","author":"Han","year":"2011"},{"key":"ref61","first-page":"1563","article-title":"Near-optimal regret bounds for reinforcement learning","volume":"11","author":"Jaksch","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref62","volume-title":"Reinforcement Learning and Dynamic Programming Using Function Approximators","volume":"39","author":"Busoniu","year":"2010"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2015.11.005"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139177801.004"},{"key":"ref65","volume-title":"Multivariate Analysis","author":"Johnson","year":"2002"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1080\/00031305.2016.1164756"},{"key":"ref67","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref68","first-page":"2094","article-title":"Deep reinforcement learning with double Q-learning","volume-title":"Proc. AAAI","volume":"16","author":"Van Hasselt"},{"key":"ref69","volume-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems","author":"Abadi","year":"2015"}],"container-title":["IEEE Transactions on Systems, Man, and Cybernetics: Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221021\/9224203\/08737882.pdf?arnumber=8737882","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:28:06Z","timestamp":1706056086000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8737882\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11]]},"references-count":69,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tsmc.2019.2920390","relation":{},"ISSN":["2168-2216","2168-2232"],"issn-type":[{"value":"2168-2216","type":"print"},{"value":"2168-2232","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11]]}}}