A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks

• Main Authors: Chang, Yuchao, Yuan, Xiaobing, Li, Baoqing, Niyato, Dusit, Al-Dhahir, Naofal
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Unsupervised Learning; Genetic Algorithms
• Online Access: https://hdl.handle.net/10356/140391
• Institution: Nanyang Technological University

Energy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes acc...