Dynamic Spatial Network with Random (Truncated) Levy Walk Mobility

<p>The mobility model used is an truncated levy walk model.</p

Mobility data of the city of Cologne (Germany)

<p>The vehicular mobility dataset is mainly based on the data made available by the TAPAS Cologne project [1]</p> <p>[1] S. Uppoor, O. Trullols-Cruces, M. Fiore, J.M. Barcelo-Ordinas, Generation and Analysis of a Large-scale Urban Vehicular Mobility Dataset, IEEE Transactions on Mobile Computing, 2013. http://dx.doi.org/10.1109/TMC.2013.27</p

Enhancing Information Dissemination using Human Mobility in Realistic Environment

<p>Number of users in each cell over time determined from dataset available at D4D challenge.</p

Enhancing Information Dissemination using Human Mobility in Realistic Environment

<p>Dissemination of information in mobile adhoc networks has lately picked up lot of interest. Some studies argue that the dissemination in these networks should be constraint while some argue that it should not. Research has found that it depends on the type of the application that is considered. For example, dissemination of mobile viruses should definitely be contained however, dissemination of emergency information should not. Towards this, we would like to propose a mechanism for enhancing dissemination of information in mobile adhoc environment. We use the concept of metapoulation model, epidemic model, beamforming and the mobility results obtained after the mining the dataset provided by the D4D Organizers. In this paper we first propose addition of three latent states to the existing SIR model and then add beamforming concepts. In this paper, we study the transient states to see the evolution of number of devices having the information and the difference in the number of devices having the information when compared with different cases considered in this paper. Our results show that enhancement can be considerably achieved in terms of number of devices having the information.</p

Enhancing Information Dissemination using Human Mobility in Realistic Environment

<p>Enhancing Information Dissemination using Human Mobility in Realistic Environment</p

Spreading process of mobile data through Ivory Coast using mobility data available during the 2013 D4D challenge

<p>Diffusion process using mobility data available during the 2013 D4D challenge</p

Dynamic Population density estimation of Milan City

Video of the evolution of the Z-Score of each cell of the Milan’s grid across time during the month of April 201

Dynamic Population density estimation of Turin City

Video of the evolution of the Z-Score of each cell of the Turin’s grid across time during the month of April 201

Spatial Graph generated from mobility data of the city of Cologne (Germany)

<p>Spatial Graph generated from mobility data of the city of Cologne (Germany). The vehicular mobility dataset is mainly based on the data made available by the TAPAS Cologne project [1]</p> <p> </p> <p>[1] S. Uppoor, O. Trullols-Cruces, M. Fiore, J.M. Barcelo-Ordinas, Generation and Analysis of a Large-scale Urban Vehicular Mobility Dataset, IEEE Transactions on Mobile Computing, 2013. http://dx.doi.org/10.1109/TMC.2013.27</p