Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81896
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorYuan, Wen_US
dc.creatorGanganath, Nen_US
dc.creatorCheng, CTen_US
dc.creatorQing, Gen_US
dc.creatorLau, FCMen_US
dc.creatorZhao, Yen_US
dc.date.accessioned2020-03-27T02:55:29Z-
dc.date.available2020-03-27T02:55:29Z-
dc.identifier.issn1551-3203en_US
dc.identifier.urihttp://hdl.handle.net/10397/81896-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication W. Yuan, N. Ganganath, C. Cheng, G. Qing, F. C. M. Lau and Y. Zhao, "Path-Planning-Enabled Semiflocking Control for Multitarget Monitoring in Mobile Sensor Networks," in IEEE Transactions on Industrial Informatics, vol. 16, no. 7, pp. 4778-4787, July 2020 is available at https://dx.doi.org/10.1109/TII.2019.2959330en_US
dc.subjectSemi-flockingen_US
dc.subjectPath planningen_US
dc.subjectMobility mapsen_US
dc.subjectMobile sensor networksen_US
dc.subjectArea coverageen_US
dc.subjectTarget trackingen_US
dc.titlePath-planning-enabled semiflocking control for multitarget monitoring in mobile sensor networksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage4778en_US
dc.identifier.epage4787en_US
dc.identifier.volume16en_US
dc.identifier.issue7en_US
dc.identifier.doi10.1109/TII.2019.2959330en_US
dcterms.abstractMobile sensor networks (MSNs) are good candidates for large-scale unattended surveillance applications. However, it is challenging to track moving targets due to their complex dynamic behaviors. Semiflocking algorithms have been proven to be efficient in controlling MSNs in both area coverage and target tracking applications. While many existing literatures on the study of semiflocking algorithms often assume an area of interest (AoI) to be regular and with unified traversal cost, the uneven and rough landscapes in real-life applications have imposed extra challenges and raised demands for new management strategies. In this article, a mobility map is used to incorporate different costs associated with irregular terrains which results in different maximum allowed speeds on nodes in different regions. In order to reduce target detection time and node energy consumption, a heuristic search algorithm is developed to find time-efficient and feasible paths between nodes and sensing targets. Under the proposed algorithm, nodes can effectively select a target to track or search for new targets in the AoI. Results of extensive experiments show that semiflocking-controlled nodes together with path planning can reach their targets faster with lower energy consumption compared to three exiting flocking-based algorithms.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on industrial informatics, July 2020, v. 16, no. 7, p. 4778-4787en_US
dcterms.isPartOfIEEE transactions on industrial informaticsen_US
dcterms.issued2020-07-
dc.identifier.isiWOS:000522523000044-
dc.identifier.eissn1941-0050en_US
dc.description.validate202003 bcrc-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0427-n01en_US
dc.description.pubStatusPublisheden_US
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