Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs

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dc.contributor.authorChang, Yuchao
dc.contributor.authorYuan, Xiaobing
dc.contributor.authorLi, Baoqing
dc.contributor.authorNiyato, Dusit
dc.contributor.authorAl-Dhahir, Naofal
dc.contributor.utdAuthorAl-Dhahir, Naofal
dc.date.accessioned2020-04-27T20:47:55Z
dc.date.available2020-04-27T20:47:55Z
dc.date.issued2018-12-10
dc.descriptionFull text access is available from the publisher. Use the Link to Article
dc.description.abstractDifferent from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs' performance by applying machine learning techniques and genetic algorithms. Using the K -means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives' dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to stateof-the-art algorithms at an acceptable computational complexity.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorshipNPRP under Grant 8-627-2-260 from the Qatar National Research Fund
dc.identifier.bibliographicCitationChang, Yuchao, Xiaobing Yuan, Baoqing Li, Dusit Niyato, et al. 2019. "Machine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs." IEEE Access 7: 4913-4926, doi: 10.1109/ACCESS.2018.2885934
dc.identifier.issn2169-3536
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2018.2885934
dc.identifier.urihttps://hdl.handle.net/10735.1/8288
dc.identifier.volume7
dc.language.isoen
dc.publisherInstitute of Electrical Electronics Engineers Inc
dc.rightsOpen Access Publishing Agreement
dc.rights©2018 IEEE
dc.rights.urihttps://open.ieee.org/index.php/about-ieee-open-access/faqs/
dc.source.journalIEEE Access
dc.subjectClustering algorithms
dc.subjectTelecommunication
dc.subjectMachine learning
dc.subjectMathematical optimization
dc.subjectElectric network topology
dc.subjectWireless sensor networks
dc.titleMachine-Learning-Based Parallel Genetic Algorithms for Multi-Objective Optimization in Ultra-Reliable Low-Latency WSNs
dc.type.genrearticle

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