Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104803
Title: Machine-learning-based parallel genetic algorithms for multi-objective optimization in ultra-reliable low-latency WSNs
Authors: Chang, Yuchao
Yuan, Xiaobing
Niyato, Dusit
Al-Dhahir, Naofal
Li, Baoqing
Keywords: DRNTU::Engineering::Computer science and engineering
Machine Learning (ML)
Genetic Algorithms (GAs)
Issue Date: 2018
Source: Chang, 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.2018.2885934
Series/Report no.: IEEE Access
Abstract: Different 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.
URI: https://hdl.handle.net/10356/104803
http://hdl.handle.net/10220/48646
DOI: 10.1109/ACCESS.2018.2885934
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 http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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