MOBILITY MODELING FOR AD HOC AND OPPORTUNISTIC NETWORKS

1. MOBILITY MODELING FOR AD HOC AND OPPORTUNISTIC NETWORKS Gunnar KarlssonLinnæus Center ACCESSKTH School of Electrical Engineering100 44 Stockholm, Sweden Work with Ljubica Pajević, Sylvia Kouyoumdjieva, Ólafur Ragnar Helgason and Vladimir Vukadinović

2. Wireless and mobile What determines communication performance? Propagation of radio signals Sharing of spectrum Traffic demand What about mobility? Makes propagation and sharing dynamic Shifts traffic demands Personal wireless communication Vehicular and pedestrian mobility Mostly indoors In built environments Social and physical interaction among people

3. Capturing mobility Vital for performance evaluation and network dimensioning Examples Cellular handoffs, coverage and load Ad hoc routing convergence and churn Opportunistic networking feasibility Interworking of modes: data offloading Present synthetic and analytics models Fixed number of nodes Limited or no structure of the space No physical interaction among mobile nodes Social behavior absent Experimentally obtained mobility data Allow for realism but are difficult to generalize Fixed number of nodes, specific surroundings CRAWDAD database for traces

4. Levels of mobility models Microscopic Variation in radio channel quality Within a single cell Single peer-to-peer contact Mesoscopic Microscopic properties as averages Arrival and departures of mobile nodes Churn and handoff Most dynamics in resource sharing Macroscopic Full network and geographic scope Infrastructure planning and dimensioning Loads in cells, handover patterns

5. Model places where people congregate No internal mobility and full connectivity Poisson arrival process with rate λ Exponentially distributed sojourn times, mean value 1/μ Opportunistic content distribution (broadcasting) Nodes bring content with probability p All nodes in the place receive the contents Data transfer time infinitesimally small Models coffee shops, bus stops, train cars, … Analytic approach: Zero dimensions content states

6. Analytic approach: One dimension Nodes enter at one end, depart at the other No change of direction IID inter-arrival times Speed randomly drawn IID interval [vmin, vmax] (vmin > 0) Opportunistic content distribution (broadcasting) Nodes bring content with probability p All nodes would like to receive the contents Models streets, corridors, and other linear movements Street segment 0 L s lt l’t

7. Analytic approach: A grid

8. Multi-agent simulation Addresses mesoscopic and macroscopic mobility Radio link properties averaged out Advanced node behavior Target speed distribution Stamina (pause times) Route choices Nodal interactions Collision avoidance Target speed vs social comfort distance Structured space Restrictions to mobility Affects route choices

10. Gunnar Karlsson, Linnæus Center ACCESS and School of Electrical Engineering

11. Goals of our work How does mobility affect connectivity? Sensitivity of connectivity among nodes to Speed, arrival process, space, cultural aspects Validation of analytic models Create mobility traces for system simulator

12. Conclusion Mobility affects propagation, sharing and routing Smaller cells, ad hoc and opportunistic modes Handoff, data offloading, dimensioning and planning Mobility modeling Must include constraints on mobility Node interaction can be ignored at low density Multi-agent simulation as a novel means Captures detailed human mobility behavior Allow scientific experimentation with setup Validation of analytic models

13. Future work Library of 0-D and 1-D models Compose complex models of simple building blocks Compare measurement trace and model Add performance metrics For cellular systems Multi-agent simulations Macroscopic modeling Cellular systems modeling