Hidden terminal-aware access point selection for IEEE 802.11ah networks

2021 ◽  
Vol 173 ◽  
pp. 1-13
Author(s):  
Jung-Han Han ◽  
Seung-Jae Han
Keyword(s):  
Access Point ◽  
Point Selection ◽  
Hidden Terminal ◽  
Ieee 802.11Ah ◽  
Selection For

Smart access point selection for dense WLANs: A use-case

2018 ◽  
Author(s):  
A. Raschella ◽  
F. Bouhafs ◽  
M. Mackay ◽  
K. Zachariou ◽  
V. Pilavakis ◽  
...  
Keyword(s):  
Access Point ◽  
Use Case ◽  
Point Selection ◽  
Selection For

scholarly journals Network-assisted Smart Access Point Selection for Pervasive Real-time mHealth Applications

2015 ◽  
Vol 63 ◽  
pp. 317-324 ◽  
Author(s):  
Norbert Varga ◽  
Esa Piri ◽  
László Bokor
Keyword(s):  
Real Time ◽  
Access Point ◽  
Point Selection ◽  
Selection For

A Group-Discrimination-Based Access Point Selection for WLAN Fingerprinting Localization

2014 ◽  
Vol 63 (8) ◽  
pp. 3967-3976 ◽  
Author(s):  
Tsung-Nan Lin ◽  
Shih-Hau Fang ◽  
Wei-Han Tseng ◽  
Chung-Wei Lee ◽  
Jeng-Wei Hsieh
Keyword(s):  
Access Point ◽  
Point Selection ◽  
Selection For

scholarly journals Energy Efficiency Oriented Access Point Selection for Cognitive Sensors in Internet of Things

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
ChunHua Ju ◽  
Qi Shao
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Distributed Learning ◽  
Access Point ◽  
Selection Problem ◽  
Potential Game ◽  
Point Selection ◽  
Selection For

This paper studies the distributed energy efficient access point (AP) selection for cognitive sensors in the Internet of Things (IoT). The energy consumption is critical for the wireless sensor network (WSN), and central control would cause extremely high complexity due to the dense and dynamic deployment of sensors in the IoT. The desired approach is the one with lower computation complexity and much more flexibility, and the global optimization is also expected. We solve the multisensors AP selection problem by using the game theory and distributed learning algorithm. First, we formulate an energy oriented AP selection problem and propose a game model which is proved to be an exact potential game. Second, we design a distributed learning algorithm to obtain the globally optimal solution to the problem in a distributed manner. Finally, simulation results verify the theoretic analysis and show that the proposed approach could achieve much higher energy efficiency.

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