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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorTsoi, PY-
dc.creatorCheng, CT-
dc.creatorGanganath, N-
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2014 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 Tsoi, P. -., Cheng, C. -., & Ganganath, N. (2014). A k-means-based formation algorithm for the delay-aware data collection network structure. Paper presented at the 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), p. 384-388 is available at 10.1109/CyberC.2014.73en_US
dc.subjectData collection processen_US
dc.subjectK-means algorithmsen_US
dc.subjectResources managementen_US
dc.subjectWireless sensor networksen_US
dc.titleA k-means-based formation algorithm for the delay-aware data collection network structureen_US
dc.typeConference Paperen_US
dcterms.abstractA wireless sensor network (WSN) consists of a large number of wireless sensor nodes that collect information from their sensing terrain. Wireless sensor nodes are, in general, battery-powered devices with limited processing and transmission power. Therefore, the lifetime of WSNs heavily depends on their energy efficiency. Multiple-cluster 2-hop (MC2H) network structure is commonly used in WSNs to reduce energy consumption due to long-range communications. However, networks with the MC2H network structure are commonly associated with long data collection processes. The delay-aware data collection network structure (DADCNS) is proposed to shorten the duration of data collection processes without sacrificing network lifetime. In this paper, a k-means-based formation algorithm for the DADCNS, namely DADCNS-RK, is proposed. The proposed algorithm can organize a network into the DADCNS, while minimizing the total communication distance among connected sensor nodes by performing k-means clustering recursively. Simulation results show that, when comparing with other DADCNSs formed by different algorithms, the proposed algorithm can reduce the total communication distances of networks significantly.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 13-15 Oct. 2014, Shanghai, China, p. 384–388-
dc.relation.conferenceInternational Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery [CyberC]-
dc.description.ros2014-2015 > Academic research: refereed > Refereed conference paper-
dc.description.oaAccepted Manuscripten_US
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