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 ap...

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Bibliographic Details
Main Authors: Yuchao Chang, Xiaobing Yuan, Baoqing LI, Dusit Niyato, Naofal Al-Dhahir
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Machine learning (ML)
genetic algorithms (GAs)
multi-objective optimization
near-optimal clustering network topology
ultra-reliable and low-latency wireless sensor networks (uRLLWSNs)
Online Access:https://ieeexplore.ieee.org/document/8571230/

Internet

https://ieeexplore.ieee.org/document/8571230/

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