# Energy Prediction of Access Points in Wi-Fi Networks According to Users' Behaviour
## 8 Discussion  
Figure 6 allows us to know the amount of energy in each access point that increases or decreases after removing \(AP_{8}\) in the Wi-Fi network, as we can see in Figure 10. This information is very useful for  
Figure 8: Energy distribution among the APs in the simulated network (**A**) and new network (**B**).  
Figure 9: Proportional point relationship between energy (\(TNE\)) and roamings (\(TPR\)) in the APs during the period for the new network, given by the coefficients \(CN\).  
planning maintenance tasks in the network infrastructure. Of course, we can apply this methodology to any access point, repeating the calculations accordingly for the simulated environment.  
It is important to emphasize that the relationship between predicted roamings and energy is based on coefficients obtained from real data. This guarantees that each point coefficient takes into account the particular characteristics of the corresponding access point (wire length, transmission power, interferences, etc.). These particular and different characteristics of each access point explains how we observe higher energies in some cases where the number of roamings is smaller, and vice versa. Consequently, our energy prediction proposal has the advantage of being based not on theoretical constructions, but on experimental frameworks that take into account the particular factors of each wireless infrastructure.  
Future works can improve our proposal. In this sense, we plan to validate the prediction method by checking the effect of cancelling other access points different to \(AP_{8}\) (considering enough period size) and considering other wireless infrastructures and environments where users reflect other behaviours. Besides, we would like to go more deeply into the relationship between roamings and energy, extending the point coefficients formula to obtain a more accurate one and considering smaller sampling periods for collecting energy data.  
This work was partially funded by the Government of Extremadura (Spain) under the project IB16002, by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, EU) under the contract TIN2016-76259-P (PROTEIN project). [PERSON] is supported by grant CONICYT/FONDECYT/REGULAR/1171243, [PERSON] is supported by grant CONICYT/FONDECYT/REGULAR/1160455. We also thank \"Proyecto CORFO 14 ENI2-26905 Nueva Ingenieria para el 2030\".  
[PERSON] and [PERSON] proposed the energy prediction methodology, programmed the algorithms, performed the experiments and supplied the technological assumptions of the network; [PERSON] contributed writing the article and analysing the results; [PERSON] collected and filtered the energy data; [PERSON] and [PERSON] reviewed the article and suggested some conclusions.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
The following notations are used in this manuscript:  
\begin{tabular}{l l} AP & Access Point \\ \(\beta\) & Learning Rate \\ C & Point Coefficients for simulated network \\ CN & Point Coefficients for new network \\ CNR & Current Roamings matrix of new network \\ CR & Current Roamings matrix \\ \end{tabular}  
Figure 10: Increase of the energy in the APs after removing \(AP_{8}\).