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Improved AP association management using machine learning

Improved AP association management using machine learning,10.1145/1942268.1942271,Mobile Computing and Communications Review,Tingting Sun,Wade Trappe,

Improved AP association management using machine learning  
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We propose a distributed scheme by which nodes select an appropriate access point to associate with using each individual device's channel utilization. Specifically, we define a new metric, channel utilization, which is defined as the ratio of required bandwidth to available bandwidth estimation. By incorporating channel utilization into the access point selection protocol, we effectively reduce unnecessary reassociations and improve upper layer performance in terms of throughput, packet delivery delay, etc. We further enhance our protocol by using reinforcement learning to adapt the scheduling of probing neighboring access points (APs), ultimately reducing probing overhead by learning from past experience whether the current operational scenario would suffer from undesirable overhead. When channel utilization is combined with adaptive probing, we observed a significant performance improvement compared to traditional association approaches.
Journal: Mobile Computing and Communications Review - MCCR , vol. 14, no. 4, pp. 4-6, 2011
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