Machine-learning-based parallel genetic algorithms for multi-objective optimization in ultra-reliable low-latency WSNs

Machine-learning-based parallel genetic algorithms for multi-objective optimization in ultra-reliable low-latency WSNs

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 mach...

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Bibliographic Details
Main Authors: Chang, Yuchao, Yuan, Xiaobing, Niyato, Dusit, Al-Dhahir, Naofal, Li, Baoqing
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2019
Subjects:
DRNTU::Engineering::Computer science and engineering
Machine Learning (ML)
Genetic Algorithms (GAs)
Online Access:https://hdl.handle.net/10356/104803
http://hdl.handle.net/10220/48646

Internet

https://hdl.handle.net/10356/104803
http://hdl.handle.net/10220/48646

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