We propose a distributed access point selection scheme by which nodes select an appropriate access point to associate with based upon each individual device's channel utilization. In this paper, we define channel utilization as the ratio of required bandwidth to estimated available bandwidth. By incorporating channel utilization into the access point selection protocol, we can effectively reduce unnecessary reassociations and improve upper layer performance such as throughput and packet delivery delay. We have further enhanced our association protocol by using reinforcement learning to dynamically schedule the probing of neighboring access points (APs), ultimately bringing down the probing overhead by learning from past experience. When channel utilization is combined with adaptive probing, we observe a significant performance improvement compared to traditional association approaches.
['Tingting Sun', 'Yanyong Zhang', 'Wade Trappe']
Improving Access Point Association Protocols Through Channel Utilization and Adaptive Probing