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

A mobility model for the realistic simulation of social context

The widespread use of user-carried devices with short-range communication leads to networks characterized by high dynamics, sporadic connectivity, and strong partitioning. In such networks, connectivity between mobile nodes is strongly influenced by sociological aspects. To enable the evaluation of mobile applications which communicate in such networks, we require an appropriate mobility model. In this thesis, we have designed and implemented a mobility model which focuses on the simulation of social context. It takes an arbitrary weighted social network as input and reflects its structural properties in its mobility scheme. Based on this approach, our model allows to integrate recent advances in the research of complex social networks. In addition, we focus on the simulation of different typical human characteristics such as the periodical reappearance at preferred locations and movement in groups. Furthermore, our model allows the integration of mobility models which concentrate on geographical aspects such as modeling obstacles or realistic movement between locations. We provide experimental results that show that our model reflects the input social network with an accuracy of up to 99%. In addition, we show that our model captures the characteristics measured in traces of human mobility, which shows the validity of our approach. The generalizational character of our model enables the fast integration of future research results in the areas of human mobility and complex social networks

Group behavior impact on an opportunistic localization scheme

In this paper we tackled the localization problem from an opportunistic perspective, according to which a node can infer its own spatial position by exchanging data with passing by nodes, called peers. We consider an opportunistic localization algorithm based on the linear matrix inequality (LMI) method coupled with a weighted barycenter algorithm. This scheme has been previously analyzed in scenarios with random deployment of peers, proving its effectiveness. In this paper, we extend the analysis by considering more realistic mobility models for peer nodes. More specifically, we consider two mobility models, namely the Group Random Waypoint Mobility Model and the Group Random Pedestrian Mobility Model, which is an improvement of the first one. Hence, we analyze by simulation the opportunistic localization algorithm for both the models, in order to gain insights on the impact of nodes mobility pattern onto the localization performance. The simulation results show that the mobility model has non-negligible effect on the final localization error, though the performance of the opportunistic localization scheme remains acceptable in all the considered scenarios