# Energy Prediction of Access Points in Wi-Fi Networks According to Users' Behaviour
## 1 Introduction  
The Access Point (AP) is one of the most important communication devices present in Wi-Fi networks. In any wireless network deployment, maintenance or enhance task, it is desirable to preserve a good balance between economic cost and performance, especially when we deal with environments subjected to a high number of users, such as a university campus, where students, professors and employees demand a lot of network resources. The economic cost comes from not only the number of devices and their unitary cost, but also the workload due to the users demanding network access through APs. The usual operations of the APs involve certain energy levels, different for each device according to its location and physical characteristics. Usually, current APs are powered by Power Over Ethernet (PoE) systems, which transmit data and electric power together using Ethernet cables. This technology speeds up the network deployment, with the corresponding cost savings due to the reduction of the materials needed. The possibility to use the popular RJ-45 Ethernet connector for this purpose [1] simplifies the maintenance works of the infrastructure and allows the control and measurement of both data and energy in the AP by means of a specialised software. Thus, software tools can assign the power in the APs analysing the frequency spectrum in order to select the optimal power and channel, according to the interferences and regional rules. Nevertheless, once this power basis is selected, the main source of variability in the energy levels come from the operations oriented to satisfy thedemand of network access by the users. In this work, we focus our research interest in this second energy component, where the energy is strongly related with the users' activity. The users' activity can be registered using software tools that monitor modern wireless networks. These tools identify the interaction between users and APs, as well as the energy levels in the devices. The longer we monitor the network, the more data we obtain. A careful study of this data can give us valuable information about the users' behavior and its effect on the energy levels in the APs. This information is useful for planning the network deployment or enhancing it through maintenance tasks related with the APs, whose energy impact should be previously evaluated. Particularly, in this work we focus our attention on general maintenance, consisting of removing a determined AP due to changes in the organisation of spaces, rooms and buildings (which is very usual in academic environments). This removal produces a certain energy impact on the remaining APs in the network, coming from the users' behavior when they request network access, resulting in a different infrastructure. In this situation, if we knew how the energy pattern of the network would be after removing a determined AP, we could study which of the remaining APs should be reinforced if the energy consumption increases too much. This reinforcement can be either adding another device to the proximity of the punished AP, or replacing it with another device with higher performance. Both possibilities imply a new expense. Therefore, predicting the energy consumption is very useful for us in order to analyse determined situations with the purpose of reducing economic costs and guaranteeing the quality of service. What is the energy impact in the APs after removing one of them? This question can be answered by predicting the number of roamings for the new infrastructure and establishing a relationship between the number of roamings and the energy levels in the access points. For this purpose, it is necessary to collect data of the network usage by a high number of network users during enough time in order to reflect the users' behavior 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 machine learning and big data analysis.