Predicting Congestion Level in Wireless Networks Using an Integrated Approach of Supervised and Unsupervised Learning

The usage data from user devices can be analyzed to answer a number of possible questions in regards to congestion, access point (AP) load balancing, user mobility trends and efficient channel allocation. In this paper, we attempt to identify Wi-Fi usage trends in a dynamic environment and use it to further predict the congestion in various locations. To accomplish this, we use various supervised learning algorithms to find the existing patterns in spectrum usage inside University of Nevada, Reno. Using these patterns, we predict the values for certain key attributes that directly correlate to the congestion status of any location. Finally, we apply unsupervised learning algorithms to these predicted data instances to cluster them into different groups. Each group will determine the level of congestion for any building at any time of any day. This way, we will be able to ascertain whether or not any place at any time in the future might require additional resources, which can be utilized from places with low congestion rate, to be able to deliver wireless services efficiently.