Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Modeling the sociological aspects of mobility in ad hoc networks
MSWIM '03 Proceedings of the 6th ACM international workshop on Modeling analysis and simulation of wireless and mobile systems
Stochastic properties of the random waypoint mobility model
Wireless Networks
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
Designing mobility models based on social network theory
ACM SIGMOBILE Mobile Computing and Communications Review
Characterizing pairwise inter-contact patterns in delay tolerant networks
Proceedings of the 1st international conference on Autonomic computing and communication systems
Mobile call graphs: beyond power-law and lognormal distributions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SIMPS: using sociology for personal mobility
IEEE/ACM Transactions on Networking (TON)
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Understanding how humans move is a key factor for the design and evaluation of networking protocols and mobility management solutions in mobile networks. This is particularly true for mobile scenarios in which conventional single-hop access to the infrastructure is not always possible, and multi-hop wireless forwarding is a must. We specifically focus on one of the most recent mobile networking paradigms, i.e., opportunistic networks. In this paradigm the communication takes place directly between the personal devices (e.g., smartphones and PDAs) that the users carry with them during their daily activities, without any assumption about pre-existing infrastructures. Among all mobility characteristics that may affect the performance of opportunistic networks, the users' travelling patterns have recently gained a lot of attention due to their impact on the spreading of both viruses and messages in such a network. In this paper we consider a social-based mobility model (HCMM) and we extend this model to account for the typical travelling behaviour of users. To the best of our knowledge, the resulting mobility model is the first model in which movements driven by social relations also match statistical features of travelling patterns as measured in reality. Finally, we evaluate our proposal through simulations over a wide range of scenarios, emphasizing the effect of finite sampling on the obtained results.