Name
Title | ||
|---|---|---|
Optimizing the network energy of cloud assisted internet of things by using the adaptive neural learning approach in wireless sensor networks. |
Abstract | ||
|---|---|---|
•A reinforcement-based learning technique, Adaptive Q-Learning (AQL) for improving network performance in CIoT is proposed.•AQL operates in two distinct phases for cluster head selection and forwarder selection.•The decision making system used to qualify node based on their past behavior over transmission.•AQL improves both inter and intra cluster communication optimization through adaptive forwarder and header selection. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compind.2019.01.004 | Computers in Industry |
Keywords | Field | DocType |
Cloud-assisted internet of things (CIoT),Wireless sensor networks,Energy optimization,Adaptive neural learning,Principal component analysis,Routing protocol | Forwarder,Virtual machine,Computer network,Control engineering,Header,Engineering,Cluster analysis,Energy consumption,Wireless sensor network,Network performance,Cloud computing | Journal |
Volume | ISSN | Citations |
106 | 0166-3615 | 3 |
PageRank | References | Authors |
0.38 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdulaziz Alarifi | 1 | 3 | 3.76 |
| Amr Tolba | 2 | 177 | 29.10 |