The impact of mobility on cellular network configuration
Wireless Networks
The predictive user mobility profile framework for wireless multimedia networks
IEEE/ACM Transactions on Networking (TON)
Characterizing mobility and network usage in a corporate wireless local-area network
Proceedings of the 1st international conference on Mobile systems, applications and services
Characterizing and modeling user mobility in a cellular data network
PE-WASUN '05 Proceedings of the 2nd ACM international workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
A User Pattern Learning Strategy for Managing Users' Mobility in UMTS Networks
IEEE Transactions on Mobile Computing
Mobility Patterns in Microcellular Wireless Networks
IEEE Transactions on Mobile Computing
On the Effectiveness of Movement Prediction to Reduce Energy Consumption in Wireless Communication
IEEE Transactions on Mobile Computing
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
User mobility modeling and characterization of mobility patterns
IEEE Journal on Selected Areas in Communications
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
A novel framework for handoff analysis under generalized session and mobility statistics
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
A markov routing algorithm for mobile DTNs based on spatio-temporal modeling of human movement data
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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Due to multifaceted human behavior, synthetic models are inept at realistically modeling long term mobility characteristics of users. The diversity in mobility character adds yet another dimension to this complex problem. Empirical studies are essential and are capable of providing realistic user models. This paper examines the real-time mobility traces of users and identifies key mobility parameters, which are used to classify users and create homogenized groups. Based on mobility and degree of predictability, a mobile user classification is attempted. As per-user mobility management schemes proposed in the literature are difficult to implement, it is essential to adopt a class or group based approach to facilitate implementation of dynamic schemes. Further, this paper characterizes in-session and out-of-session Cell Residence Time (CRT), the feature that critically influences several management tasks. The out-of-session CRT distribution has been represented using a heavy tail distribution. The applicability of the model for various classes of users has been studied. The results of this study can be used to spawn a more realistic user model, for simulation studies.