We present a methodology based on matrix factorization and gradient descent to predict the number of sessions established in the access points of a Wi-Fi network according to the users' behavior. As the network considered in this work is monitored and controlled by software in order to manage users and resources in real time, we may consider it as a cyber-physical system that interacts with the physical world through access points, whose demands can be predicted according to users' activity. These predictions are useful for relocating or reinforcing some access points according to the changing physical environment. In this work we propose a prediction model based on machine learning techniques, which is validated by comparing the prediction results with real user's activity. Our experiments collected the activity of 1,095 users demanding 26,673 network sessions during one month in a Wi-Fi network composed of 10 access points, and the results are qualitatively valid with regard to the previous knowledge. We can conclude that our proposal is suitable for predicting the demand of sessions in access points when some devices are removed taking into account the usual activity of the network users.
Some maintenance tasks in Wi-Fi networks may involve removing an access point due to several reasons. As a result, the new infrastructure registers a different number of roamings in the access points according to the users' behaviour, with a certain energy impact added to the consumption caused by the own operations of the devices. This energy effect should be understood in order to tackle the measures aimed at planning the infrastructure deployment. In this work, we propose a methodology to predict the energy consumption in the access points of a Wi-Fi network when we remove a particular device, based on a twofold support. We predict the number of roamings following a method previously validated; on the other hand, we assess the relationship between roamings and energy in the full infrastructure, using the data collected from a high number of network users during a given time in order to reflect the users' behaviour with the maximum accuracy. From this knowledge, we can infer the energy prediction for a different environment where the roamings are predicted using techniques based on recommender systems and machine learning.
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