Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Analysis of a campus-wide wireless network
Proceedings of the 8th annual international conference on Mobile computing and networking
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
Towards a model of user mobility and registration patterns
ACM SIGMOBILE Mobile Computing and Communications Review
Weighted waypoint mobility model and its impact on ad hoc networks
ACM SIGMOBILE Mobile Computing and Communications Review
Classifying the mobility of users and the popularity of access points
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Modeling Roaming in Large-scaleWireless Networks Using Real Measurements
WOWMOM '06 Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
The QoS-RWP mobility and user behavior model for public area wireless networks
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
A Battery-Aware Algorithm for Supporting Collaborative Applications
Mobile Networks and Applications
HotCity: enhancing ubiquitous maps with social context heatmaps
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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Modeling movements of users is important for simulating wireless networks, but current models often do not reflect real movements. Using real mobility traces, we can build a mobility model that reflects reality. In building a mobility model, it is important to note that while the number of handheld wireless devices is constantly increasing, laptops are still the majority in most cases. As a laptop is often disconnected from the network while a user is moving, it is not feasible to extract the exact path of the user from network messages. Thus, instead of modeling individual user's movements, we model movements in terms of the influx and outflux of users between access points (APs). We first counted the hourly visits to APs in the syslog messages recorded at APs. We found that the number of hourly visits has a periodic repetition of 24 hours. Based on this observation, we aggregated multiple days into a single day by adding the number of visits of the same hour in different days. We then clustered APs based on the different peak hour of visits. We found that this approach of clustering is effective; we ended up with four distinct clusters and a cluster of stable APs. We then computed the average arrival rate and the distribution of the daily arrivals for each cluster. Using a standard method (such as thinning) for generating non-homogeneous Poisson processes, synthetic traces can be generated from our model.