Wireless 802.11 users often experience connectivity problems while using 802.11 networks. The task of diagnosing and fixing these problems by looking at usage patterns is one of the major challenges that campus and corporate 802.11 network administrators face. In this paper we identify a usage pattern that we name "abrupt ending" of 802.11 connections and that happens when a large number of sessions in the same access point (AP) end within a one second window. We observe up to 40 sessions ending at the same second and over 150k abrupt endings in a two and a half year period from 2006 to 2009 in the Faculty of Engineering of the University of Porto. We describe the data set and identify anomaly-related patterns such as AP halt/crash, AP overload, interference, interference across the vicinity of an AP, and AP persistent interference as well as user authentication failure and intermittent connectivity. We validate our analysis by density clustering of the abrupt ending data. In addition we crosscheck the existence of abrupt endings on a 2011 data set of the same location in Porto and on 2011 data from the University of Minho, which was deployed and is managed independently from the one in Porto.

Wireless and in particular 802.11 is one of the major technologies for accessing the Internet at home, in coffee shops or other public places, and in enterprises and university campuses. While most recent work on modeling wireless sites focuses on user mobility, this paper presents and compares a number of models for characterizing access point (AP) usage; moreover, rather than looking at throughput we focus on daily counts of keep-alive events that mobile devices generate every 15 minutes they are connected to the wireless network. Our models are trained and evaluated on data collected from a Porto hotspot of Eduroam, the European academic wireless network. The models we present are generative, in the sense they can be used to generate synthetic daily event counts for a single AP or a collection of APs. We provide standard crossvalidation comparison of models using the log-likelihood of the models on training and test data.

Wireless and in particular 802.11 is one of the major technologies for accessing the Internet at home, in coffee shops or other public places, and in enterprises and university campuses. While most recent work on modeling wireless sites focuses on user mobility, this paper presents and compares a number of models for characterizing access point (AP) usage; moreover, rather than looking at throughput we focus on daily counts of keep-alive events that mobile devices generate every 15 minutes they are connected to the wireless network. Our models are trained and evaluated on data collected from a Porto hotspot of Eduroam, the European academic wireless network. The models we present are generative, in the sense they can be used to generate synthetic daily event counts for a single AP or a collection of APs. We provide standard crossvalidation comparison of models using the log-likelihood of the models on training and test data.