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...
Main Authors: | Chang, Yuchao, Yuan, Xiaobing, Niyato, Dusit, Al-Dhahir, Naofal, Li, Baoqing |
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Other Authors: | School of Computer Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/104803 http://hdl.handle.net/10220/48646 |
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