# Energy Prediction of Access Points in Wi-Fi Networks According to Users' Behaviour
## 2 Related Works  
Access Points play an important role in the planning, prediction or optimisation of wireless networks. Several of these aspects, such as optimal location and radio characteristics of the devices, have a certain effect on the energy levels of the APs and other communication elements. For example, there are methods for reducing power consumption in communication terminals by adjusting the radio transmission range [3] and for minimising the energy consumption in wireless networks [4]. Other aspects related with users' behaviour have an energy impact too. Our work focuses on the connection between the number of roannings established by the users and the energy consumption in the APs. Like this focus, security levels and energy consumption are connected in a module for the Internet of Things (IoT) based on Wi-Fi protocol [5]. Many prediction tasks for the deployment or maintenance of Wi-Fi networks consider APs as a basic element to take into account. Thus, predicting the density of neighbouring APs and the corresponding data traffic of Wi-Fi networks [6] allows the sharing of cellular and Wi-Fi networks in the same spectrum [7], and the prediction of the data quota that user management facilitates to design network infrastructures where data pricing is significant [8]. The prediction efforts in wireless networks also focus on the users' location and data traffic. Principally, a location prediction method based on nonlinear time series analysis of the arrival and residence times of users in relevant places is applied to predict their future movements [9]. Secondly, a method that considers terminal distribution to predict the throughput of an AP in a simulated network is proposed in [10]. In addition, although we could know the traffic demand (with the corresponding energy impact), it is difficult to predict it due to its highly dynamic nature, which should be considered when applying routing solutions. For example, tools for network routing under dynamic data demand are shown in [11], where the traffic is predicted by studying the traces collected at APs by means of time series analysis; and a network-based routing algorithm [12] and a meta-direct framework [13] were developed to obtain optimal paths in cloud computing massive infrastructures in order to minimise the energy consumed by the users' requests. Mobility and services are also predicted in wireless networks. For example, mobility is described in [14] by the mobile node's next AP using prediction agents related to an AP in Wi-Fi networks. Besides, there are networks where the demand of resources and services can be predicted for optimisation tasks. Thus, application workloads in enterprises are predicted in [15] considering performance modelling and capacity planning, according to demand patterns in order to design recommendation services. Our prediction methodology finds common points with other works where, although they consider different aspects of the Wi-Fi networks, machine learning and big data analysis offer an interesting starting point to predicting tasks. For example, statistical [16] and learning methods [17] are considered for prediction purposes, like traffic patterns and database workloads. The optimal placement of wireless devices, such as APs or routers, has been also widely studied in [18]. These studies are interesting for our purposes. The initial deployment of the APs can determine the probablity of a determined device to be removed facing certain maintenance works as space reorganisation. Thus, if we have different options to plan initial deployments, we should choose the solution that offers less probability of assigning APs in places with a high risk to be changed in the future (for example, proximity to divisible rooms). Many of these studies of initial optimal deployments consider different parameters as optimisation goals; for our interest, we should consider coverage [19], rather than lifetime, economic cost, failure risks, etc.