Realistic mobility models are fundamental to evaluate the performance of protocols in mobile ad hoc networks. Unfortunately, there are no mobility models that capture the non-homogeneous behaviors in both space and time commonly found in reality, while at the same time being easy to use and analyze. Motivated by this, we propose a time-variant community mobility model, referred to as the TVC model, which realistically captures spatial and temporal correlations. We devise the communities that lead to skewed location visiting preferences, and time periods that allow us to model time dependent behaviors and periodic re-appearances of nodes at specific locations. To demonstrate the power and flexibility of the TVC model, we use it to generate synthetic traces that match the characteristics of a number of qualitatively different mobility traces, including wireless LAN traces, vehicular mobility traces, and human encounter traces. More importantly, we show that, despite the high level of realism achieved, our TVC model is still theoretically tractable. To establish this, we derive a number of important quantities related to protocol performance, such as the average node degree, the hitting time, and the meeting time, and provide examples of how to utilize this theory to guide design decisions in routing protocols.
As a final note, in @cite_13 , the authors assume the attraction of a community (i.e., a geographical area) to a mobile node is derived from the number of friends of this node currently residing in the community. In our paper we assume that the nodes make movement decisions independently of the others (nonetheless, node sharing the same community will exhibit mobility correlation, capturing the social feature indirectly). Mobility models with inter-node dependency require a solid understanding of the social network structure, which is an important area under development. We plan to work further in this direction in the future.
@cite_13
2095702309 2031089293 2295520632 2154489778
Understanding the spatial networks formed by the trajectories of mobile users can be beneficial to applications ranging from epidemiology to local search. Despite the potential for impact in a number of fields, several aspects of human mobility networks remain largely unexplored due to the lack of large-scale data at a fine spatiotemporal resolution. Using a longitudinal dataset from the location-based service Foursquare, we perform an empirical analysis of the topological properties of place networks and note their resemblance to online social networks in terms of heavy-tailed degree distributions, triadic closure mechanisms and the small world property. Unlike social networks however, place networks present a mixture of connectivity trends in terms of assortativity that are surprisingly similar to those of the web graph. We take advantage of additional semantic information to interpret how nodes that take on functional roles such as 'travel hub', or 'food spot' behave in these networks. Finally, motivated by the large volume of new links appearing in place networks over time, we formulate the classic link prediction problem in this new domain. We propose a novel variant of gravity models that brings together three essential elements of inter-place connectivity in urban environments: network-level interactions, human mobility dynamics, and geographic distance. We evaluate this model and find it outperforms a number of baseline predictors and supervised learning algorithms on a task of predicting new links in a sample of one hundred popular cities. Recent discovery of the mixture (power-law and exponential) behavior of inter-meeting time distribution of mobile nodes presents new challenge to the problem of mobility modeling and its effect on the network performance. Existing studies on this problem via the average inter-meeting time become insufficient when the inter-meeting time distribution starts to deviate from exponential one. This insufficiency necessarily leads to the increasing difficulty in the performance analysis of forwarding algorithms in mobile ad-hoc networks (MANET). In this paper, we analyze the effect of mobility patterns on the inter-meeting time distribution. We first identify the critical timescale in the inter-meeting distribution, at which the transition from power-law to exponential takes place, in terms of the domain size and the statistics of the mobility pattern. We then prove that stronger correlations in mobility patterns lead to heavier (non-exponential) 'head' of the inter-meeting time distribution. We also prove that there exists an invariance property for several contact-based metrics such as inter-meeting, contact, inter-any-contact time under both distance-based (Boolean) and physical interference (SINR) based models, in that the averages of those contact-based metrics do not depend on the degree of correlations in the mobility patterns. Our results collectively suggest a convex ordering relationship among inter-meeting times of various mobility models indexed by their degrees of correlation, which is in good agreement with the ordering of network performance under a set of mobility patterns whose inter-meeting time distributions have power-law 'head' followed by exponential 'tail'. With the emerging of location-based social networks, study on the relationship between human mobility and social relationships becomes quantitatively achievable. Understanding it correctly could result in appealing applications, such as targeted advertising and friends recommendation. In this paper, we focus on mining users’ relationship based on their mobility information. More specifically, we propose to use distance between two users to predict whether they are friends. We first demonstrate that distance is a useful metric to separate friends and strangers. By considering location popularity together with distance, the difference between friends and strangers gets even larger. Next, we show that distance can be used to perform an effective link prediction. In addition, we discover that certain periods of the day are more social than others. In the end, we use a machine learning classifier to further improve the prediction performance. Extensive experiments on a Twitter dataset collected by ourselves show that our model outperforms the state-of-the-art solution by 30 . Mobile Ad Hoc networks, due to the unattended nature of the network itself and the dispersed location of nodes, are subject to several unique security issues. One of the most vexed security threat is node capture. A few solutions have already been proposed to address this problem; however, those solutions are either centralized or focused on theoretical mobility models alone. In the former case the solution does not fit well the distributed nature of the network while, in the latter case, the quality of the solutions obtained for realistic mobility models severely differs from the results obtained for theoretical models. The rationale of this paper is inspired by the observation that re-encounters of mobile nodes do elicit a form of social ties. Leveraging these ties, it is possible to design efficient and distributed algorithms that, with a moderated degree of node cooperation, enforce the emergent property of node capture detection. In particular, in this paper we provide a proof of concept proposing a set of algorithms that leverage, to different extent, node mobility and node cooperation - that is, identifying social ties - to thwart node capture attack. In particular, we test these algorithms on a realistic mobility scenario. Extensive simulations show the quality of the proposed solutions and, more important, the viability of the proposed approach.