Large-Scale 802.11 Wireless Networks Data Analysis Based on Graph Clustering.

This paper analyzes a large-scale dataset of real-world Wi-Fi operating networks, collected from more than 9,000 access points (APs) for 1 year. The APs are distributed among more than 1,200 educational centers in the context of a nation-wide one-to-one computing program, being most of them primary and secondary schools. The data corresponds to RSSI measurements between APs used to build the conflict graphs for each school Wi-Fi network. We propose a simple embedding for the Wi-Fi network conflict graphs based on classical graph features, which proves to be useful to analyze the behavior of the wireless networks, showing a high discrimination power among the different school networks. Moreover, we discuss some practical applications of the embedding. In particular, it enables to study the Wi-Fi network dynamics at each school, analyzing the conflict graphs temporal variations through clustering techniques. The presented methodology allows us to successfully separate the most stable scenarios from those with more significant variability, which therefore require more technical resources to optimize the network. Besides, we also compared the behaviour of the Wi-Fi networks of the different schools, which enable us to reuse the optimal configuration found for one school in all those sites that have similar conflict graph patterns.

Managing Devices of a One-to-One Computing Educational Program Using an IoT Infrastructure.

Plan Ceibal is the name coined in Uruguay for the local implementation of the One Laptop Per Child (OLPC) initiative. Plan Ceibal distributes laptops and tablets to students and teachers, and also deploys a nationwide wireless network to provide Internet access to these devices, provides video conference facilities, and develops educational applications. Given the scale of the program, management in general, and specifically device management, is a very challenging task. Device maintenance and replacement is a particularly important process; users trigger such kind of replacement processes and usually imply several days without the device. Early detection of fault conditions in the most stressed hardware parts (e.g., batteries) would permit to prompt defensive replacement, contributing to reduce downtime, and improving the user experience. Seeking for better, preventive and scalable device management, in this paper we present a prototype of a Mobile Device Management (MDM) module for Plan Ceibal, developed over an IoT infrastructure, showing the results of a controlled experiment over a sample of the devices. The prototype is deployed over a public IoT infrastructure to speed up the development process, avoiding, in this phase, the need for local infrastructure and maintenance, while enforcing scalability and security requirements. The presented data analysis was implemented off-line and represents a sample of possible metrics which could be used to implement preventive management in a real deployment.