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dc.contributor.authorChang, Yuchaoen
dc.contributor.authorYuan, Xiaobingen
dc.contributor.authorNiyato, Dusiten
dc.contributor.authorAl-Dhahir, Naofalen
dc.contributor.authorLi, Baoqingen
dc.identifier.citationChang, Y., Yuan, X., Li, B., Niyato, D., & Al-Dhahir, N. (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.2018.2885934en
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 state-of-the-art algorithms at an acceptable computational complexity.en
dc.format.extent14 p.en
dc.relation.ispartofseriesIEEE Accessen
dc.rights© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.subjectMachine Learning (ML)en
dc.subjectGenetic Algorithms (GAs)en
dc.titleMachine-learning-based parallel genetic algorithms for multi-objective optimization in ultra-reliable low-latency WSNsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.description.versionPublished versionen
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