Name
Title | ||
|---|---|---|
A Joint Unsupervised Learning and Genetic Algorithm Approach for Topology Control in Energy-Efficient Ultra-Dense Wireless Sensor Networks. |
Abstract | ||
|---|---|---|
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 according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LCOMM.2018.2870886 | IEEE Communications Letters |
Keywords | Field | DocType |
Wireless sensor networks,Sensors,Clustering algorithms,Biological cells,Network topology,Unsupervised learning,Genetic algorithms | Topology control,Computer science,Efficient energy use,Performance metric,Real-time computing,Network topology,Unsupervised learning,Wireless sensor network,Energy consumption,Genetic algorithm,Distributed computing | Journal |
Volume | Issue | ISSN |
22 | 11 | 1089-7798 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchao Chang | 1 | 4 | 0.78 |
| Xiaobing Yuan | 2 | 19 | 3.49 |
| Baoqing Li | 3 | 114 | 20.13 |
| Niyato Dusit | 4 | 9486 | 547.06 |
| Naofal Al-Dhahir | 5 | 2755 | 319.65 |