Large scale model for information dissemination with device to device communication using call details records

In a network of devices in close proximity such as Device to Device ($D2D$) communication, we study the dissemination of public safety information at country scale level. In order to provide a realistic model for the information dissemination, we extract a spatial distribution of the population of Ivory Coast from census data and determine migration pattern from the Call Detail Records ($CDR$) obtained during the Data for Development ($D4D$) challenge. We later apply epidemic model towards the information dissemination process based on the spatial properties of the user mobility extracted from the provided $CDR$. We then propose enhancements by adding latent states to the epidemic model in order to model more realistic user dynamics. Finally, we study dynamics of the evolution of the information spreading through the population.

[1]  Zygmunt J. Haas,et al.  A new networking model for biological applications of ad hoc sensor networks , 2006, TNET.

[2]  Linda R Petzold,et al.  Efficient step size selection for the tau-leaping simulation method. , 2006, The Journal of chemical physics.

[3]  Donald F. Towsley,et al.  Performance modeling of epidemic routing , 2006, Comput. Networks.

[4]  T. Geisel,et al.  Natural human mobility patterns and spatial spread of infectious diseases , 2011, 1103.6224.

[5]  Ger Koole,et al.  The message delay in mobile ad hoc networks , 2005, Perform. Evaluation.

[6]  Alessandro Vespignani,et al.  Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. , 2007, Journal of theoretical biology.

[7]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Marco Conti,et al.  Performance modelling of opportunistic forwarding under heterogenous mobility , 2014, Comput. Commun..

[9]  D. Gillespie.  Approximate accelerated stochastic simulation of chemically reacting systems , 2001 .

[10]  Agarwal Rachit,et al.  Spreading process of mobile data through Ivory Coast using mobility data available during the 2013 D4D challenge , 2014 .

[11]  Monique Becker,et al.  Enhancing Information Dissemination in Dynamic Wireless Network using Stability and Beamforming , 2012, ArXiv.

[12]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[13]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[14]  Mads Haahr,et al.  Social Network Analysis for Information Flow in Disconnected Delay-Tolerant MANETs , 2009, IEEE Transactions on Mobile Computing.

[15]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[16]  Carl Wijting,et al.  Device-to-device communication as an underlay to LTE-advanced networks , 2009, IEEE Communications Magazine.

[17]  V. Colizza,et al.  Metapopulation epidemic models with heterogeneous mixing and travel behaviour , 2014, Theoretical Biology and Medical Modelling.

[18]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[19]  Bu-Sung Lee,et al.  Achieving Small-World Properties using Bio-Inspired Techniques in Wireless Networks , 2011, Comput. J..

[20]  Christian Bettstetter,et al.  How does randomized beamforming improve the connectivity of ad hoc networks? , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[21]  Stefan Parkvall,et al.  Design aspects of network assisted device-to-device communications , 2012, IEEE Communications Magazine.

[22]  Khaled A. Harras,et al.  CAF: Community aware framework for large scale mobile opportunistic networks , 2013, Comput. Commun..

[23]  Niloy Ganguly,et al.  Modeling broadcasting using omnidirectional and directional antenna in delay tolerant networks as an epidemic dynamics , 2010, IEEE Journal on Selected Areas in Communications.

[24]  V. Latora,et al.  Impact of network structure on a model of diffusion and competitive interaction , 2011 .

[25]  Hossam Afifi,et al.  Hybrid Model for LTE Network-Assisted D2D Communications , 2014, ADHOC-NOW.

[26]  Kazuyuki Aihara,et al.  Safety-Information-Driven Human Mobility Patterns with Metapopulation Epidemic Dynamics , 2012, Scientific Reports.

[27]  Alexandros G. Dimakis,et al.  Base-station assisted device-to-device communications for high-throughput wireless video networks , 2012, ICC.

[28]  Marcelo Dias de Amorim,et al.  Part-whole dissemination of large multimedia contents in opportunistic networks , 2012, Comput. Commun..

[29]  Vittoria Colizza,et al.  Heterogeneous length of stay of hosts’ movements and spatial epidemic spread , 2012, Scientific Reports.

[30]  Xuemin Shen,et al.  Operator controlled device-to-device communications in LTE-advanced networks , 2012, IEEE Wireless Communications.

[31]  Eitan Altman,et al.  Performance of ad hoc networks with two-hop relay routing and limited packet lifetime (extended version) , 2008, Perform. Evaluation.

[32]  Gang Zhang,et al.  Quantitative assessment on the cloning efficiencies of lentiviral transfer vectors with a unique clone site , 2012, Scientific Reports.

[33]  Pan Hui,et al.  BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks , 2011 .

[34]  Sheng Chen,et al.  Collaborative Vehicular Content Dissemination with Directional Antennas , 2012, IEEE Transactions on Wireless Communications.

[35]  K. Dietz,et al.  A structured epidemic model incorporating geographic mobility among regions. , 1995, Mathematical biosciences.

[36]  Constantine A. Balanis,et al.  Antenna Theory: Analysis and Design , 1982 .

[37]  Esteban Moro Egido,et al.  The dynamical strength of social ties in information spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Alessandro Vespignani,et al.  Phase transitions in contagion processes mediated by recurrent mobility patterns , 2011, Nature physics.

[39]  M. Keeling,et al.  Networks and epidemic models , 2005, Journal of The Royal Society Interface.

[40]  Andrea Baronchelli,et al.  Contagion dynamics in time-varying metapopulation networks , 2012, ArXiv.

[41]  Bu-Sung Lee,et al.  Self-organization of nodes using bio-inspired techniques for achieving small world properties , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[42]  A. Barabasi,et al.  Impact of non-Poissonian activity patterns on spreading processes. , 2006, Physical review letters.

[43]  Tom Britton,et al.  Stochastic epidemic models: a survey. , 2009, Mathematical biosciences.

[44]  Hanghang Tong,et al.  Information spreading in context , 2011, WWW.

[45]  Jari Saramäki,et al.  Multiscale analysis of spreading in a large communication network , 2011, ArXiv.

[46]  K. Psounis,et al.  Efficient Routing in Intermittently Connected Mobile Networks: The Single-Copy Case , 2008, IEEE/ACM Transactions on Networking.

[47]  Vito Latora,et al.  Selfishness, Altruism and Message Spreading in Mobile Social Networks , 2009, IEEE INFOCOM Workshops 2009.

[48]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[49]  Ingve Simonsen,et al.  Effects of City-Size Heterogeneity on Epidemic Spreading in a Metapopulation: A Reaction-Diffusion Approach , 2013 .

[50]  Etienne Huens,et al.  Data for Development: the D4D Challenge on Mobile Phone Data , 2012, ArXiv.