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

• Main Authors: Chang, Yuchao, Yuan, Xiaobing, Niyato, Dusit, Al-Dhahir, Naofal, Li, Baoqing
• Other Authors: School of Computer Science and Engineering
• Format: 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
• Institution: Nanyang Technological University

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...