Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
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
Designing mobility models based on social network theory
ACM SIGMOBILE Mobile Computing and Communications Review
Bubble rap: social-based forwarding in delay tolerant networks
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
SIMPS: using sociology for personal mobility
IEEE/ACM Transactions on Networking (TON)
Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks
IEEE Journal on Selected Areas in Communications
Markov modulated Bi-variate gaussian processes for mobility modeling and location prediction
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
SAGA: socially- and geography-aware mobility modeling framework
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
International Journal of Communication Systems
Research on social relations cognitive model of mobile nodes in Internet of Things
Journal of Network and Computer Applications
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Human mobility modeling plays an essential role in accurately understanding the performance of data forwarding protocols in mobile networks and has been attracting increasing research interest in recent years. People's movement behaviors are strongly affected by their social interactions with each other, which, however, are not sufficiently considered in most existing mobility models. Recent studies in social network theory have provided many theoretical and experimental results, which are useful and powerful for modeling the realistic social dimension of human mobility. In this article we present a novel human mobility model based on heterogeneous centrality and overlapping community structure in social networks. Instead of extracting communities from artificially generated social graphs, our model manages to construct the k-clique overlapping community structure which satisfies the common statistical features observed from distinct real social networks. This approach achieves a good trade-off between complexity and reality. The simulation results of our model exhibit characteristics observed from real human motion traces, especially heterogeneous human mobility popularity, which has significant impact on data forwarding schemes but has never been considered by existing mobility models.