A Mobility Model for the Realistic Simulation of Social Context

Simulation is a fundamental means for evaluating mobile applications based on ad-hoc networks. In recent years, the new breed of social mobility models (SMMs) has risen. Contrary to most classical mobility models, SMMs model the social aspects of human mobility, i.e. which users meet, when and how often. Such information is indispensable for the simulation of a wide range of socially-aware communication protocols mostly based on delay-tolerant networks, including opportunistic ad-hoc routing and data dissemination systems. Each SMM needs a model of the relations between a set of relevant people (called social network model -- SNM) in order to simulate their mobility. Existing SMMs lack flexibility since each of them is implicitly restricted to a specific, simplifying SNM. We present GeSoMo (General Social Mobility Model), a new SMM that separates the core mobility model from the structural description of the social network underlying the simulation. This simple and elegant design principle gives GeSoMo generalizing power: Arbitrary existing and future SNMs can be used without changing GeSoMo itself. Our evaluation results show that GeSoMo produces simulations that are coherent with a broad range of empirical data describing real-world human social behavior and mobility

The heterogeneity of inter-contact time distributions: its importance for routing in delay tolerant networks

Prior work on routing in delay tolerant networks (DTNs) has commonly made the assumption that each pair of nodes shares the same inter-contact time distribution as every other pair. The main argument in this paper is that researchers should also be looking at heterogeneous inter-contact time distributions. We demonstrate the presence of such heterogeneity in the often-used Dartmouth Wi-Fi data set. We also show that DTN routing can benefit from knowing these distributions. We first introduce a new stochastic model focusing on the inter-contact time distributions between all pairs of nodes, which we validate on real connectivity patterns. We then analytically derive the mean delivery time for a bundle of information traversing the network for simple single copy routing schemes. The purpose is to examine the theoretic impact of heterogeneous inter-contact time distributions. Finally, we show that we can exploit this user diversity to improve routing performance.Comment: 6 page

In Vivo Evaluation of the Secure Opportunistic Schemes Middleware using a Delay Tolerant Social Network

Over the past decade, online social networks (OSNs) such as Twitter and Facebook have thrived and experienced rapid growth to over 1 billion users. A major evolution would be to leverage the characteristics of OSNs to evaluate the effectiveness of the many routing schemes developed by the research community in real-world scenarios. In this paper, we showcase the Secure Opportunistic Schemes (SOS) middleware which allows different routing schemes to be easily implemented relieving the burden of security and connection establishment. The feasibility of creating a delay tolerant social network is demonstrated by using SOS to power AlleyOop Social, a secure delay tolerant networking research platform that serves as a real-life mobile social networking application for iOS devices. SOS and AlleyOop Social allow users to interact, publish messages, and discover others that share common interests in an intermittent network using Bluetooth, peer-to-peer WiFi, and infrastructure WiFi.Comment: 6 pages, 4 figures, accepted in ICDCS 2017. arXiv admin note: text overlap with arXiv:1702.0565

Evaluating Mobility Pattern Space Routing for DTNs

Because a delay tolerant network (DTN) can often be partitioned, the problem of routing is very challenging. However, routing benefits considerably if one can take advantage of knowledge concerning node mobility. This paper addresses this problem with a generic algorithm based on the use of a high-dimensional Euclidean space, that we call MobySpace, constructed upon nodes' mobility patterns. We provide here an analysis and the large scale evaluation of this routing scheme in the context of ambient networking by replaying real mobility traces. The specific MobySpace evaluated is based on the frequency of visit of nodes for each possible location. We show that the MobySpace can achieve good performance compared to that of the other algorithms we implemented, especially when we perform routing on the nodes that have a high connection time. We determine that the degree of homogeneity of mobility patterns of nodes has a high impact on routing. And finally, we study the ability of nodes to learn their own mobility patterns.Comment: IEEE INFOCOM 2006 preprin