The Markov-modulated Poisson process (MMPP) cookbook
Performance Evaluation
Modeling IP traffic using the batch Markovian arrival process
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Stochastic properties of the random waypoint mobility model
Wireless Networks
Access and mobility of wireless PDA users
ACM SIGMOBILE Mobile Computing and Communications Review
The message delay in mobile ad hoc networks
Performance Evaluation - Performance 2005
Building realistic mobility models from coarse-grained traces
Proceedings of the 4th international conference on Mobile systems, applications and services
Analysis and implications of student contact patterns derived from campus schedules
Proceedings of the 12th annual international conference on Mobile computing and networking
Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
Crossing over the bounded domain: from exponential to power-law inter-meeting time in MANET
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Power law and exponential decay of inter contact times between mobile devices
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Toward stochastic anatomy of inter-meeting time distribution under general mobility models
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
Modeling spatial and temporal dependencies of user mobility in wireless mobile networks
IEEE/ACM Transactions on Networking (TON)
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We present an analytically tractable mathematical approach for accurately modeling the distribution of inter-contact times between mobile devices carried by users. The contribution of this paper is two-fold: (1) we show how to employ a Markov-modulated Poisson process (MMPP) for characterizing long-term dependencies in the mobility behavior, and (2) we propose to employ a graph-based clustering approach for taking into account different user groups with inhomogeneous mobility patterns. We illustrate the effectiveness of the proposed approach by considering two comprehensive real-world trace data sets. The presented quantitative results show that the proposed modeling approach closely approximates the dichotomy of the distribution of human inter-contact times into an exponential and power-law distribution observed in recent studies of real-world trace data. As the presented modeling approach for inter-contact times is both analytically tractable and captures long-term dependencies in the mobility behavior, it possesses clear advantages over methods previously introduced for analyzing the performance of opportunistic networking protocols.