A Joint Unsupervised Learning and Genetic Algorithm Approach for Topology Control in Energy-Efficient Ultra-Dense Wireless Sensor Networks

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dc.contributor.authorChang, Y.
dc.contributor.authorYuan, X.
dc.contributor.authorLi, B.
dc.contributor.authorNiyato, D.
dc.contributor.authorAl-Dhahir, Naofal
dc.contributor.utdAuthorAl-Dhahir, Naofal
dc.date.accessioned2019-06-28T18:31:03Z
dc.date.available2019-06-28T18:31:03Z
dc.date.created2018-09-18
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates.
dc.description.abstractEnergy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to construct a close-to-optimum network topology using unsupervised learning in probability. Moreover, it schedules some of the cluster members to sleep to conserve the node energy using geographically adaptive fidelity. Simulation results demonstrate the superior performance of the proposed algorithm by improving energy efficiency in comparison with state-of-the-art algorithms at an acceptable computational complexity.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationChang, Y., X. Yuan, B. Li, D. Niyato, et al. 2018. "A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks." IEEE Communications Letters 22(11): 2370-2373, doi:10.1109/LCOMM.2018.2870886
dc.identifier.issn1558-25588
dc.identifier.issue11
dc.identifier.urihttps://hdl.handle.net/10735.1/6632
dc.identifier.volume22
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.urihttp://dx.doi.org/10.1109/LCOMM.2018.2870886
dc.rights©2018 IEEE
dc.source.journalIEEE Communications Letters
dc.subjectCytology
dc.subjectAlgorithms
dc.subjectEnergy consumption
dc.subjectElectric network topology
dc.subjectDetectors
dc.subjectWireless sensor networks
dc.subjectBioinformatics
dc.subjectElectric power-plants--Efficiency--Computer programs
dc.subjectBioenergetics
dc.subjectTopology
dc.subjectEnergy consumption
dc.titleA Joint Unsupervised Learning and Genetic Algorithm Approach for Topology Control in Energy-Efficient Ultra-Dense Wireless Sensor Networks
dc.type.genrearticle

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