1. © 2008 Frans Ekman Mobility Models for Mobile Ad Hoc Network Simulations Frans Ekman fekman@netlab.hut.fi Supervisor: Jörg Ott Instructor: Jouni Karvo

2. Mobility Models for Mobile Ad Hoc Network Simulations Thesis is done to the Networking Laboratory /Helsinki University of Technology Funded by  Nokia Research Centre in the SINDTN project  Academy of Finland in the DISTANCE project © 2008 Frans Ekman

3. Outline 1. Background 2. Working Day Movement model 3. Validation 4. Conclusions and future work

4. © 2008 Frans Ekman Ad hoc networks Infrastructure-less networks where each node acts as a router

5. © 2008 Frans Ekman Delay-Tolerant Networks (DTN) End-to-end connectivity does not exist Nodes need to store and forward packets

6. © 2008 Frans Ekman Delay-Tolerant Networks (2/2) Development of new protocols requires simulation Simulation requires modeling of the target environment User movement must be modeled  Contacts are transfer opportunities!

7. © 2008 Frans Ekman Modeling user movement Movement traces of real people Obtained from experiments:  Analyzing WLAN access-point data  Bluetooth devices registering contacts Limitations of traces  Environment specific  Limited to certain areas or certain sets of nodes

8. © 2008 Frans Ekman Movement models A set of rules controlling node movement Configurable and easy to work with Models  Random Walk  Random Waypoint (RWP) Currently most models are too simple  Homogeneous movement  Some real world characteristics are missing -> Need for a realistic movement model

9. © 2008 Frans Ekman Outline 1. Background 2. Working Day Movement model 3. Validation 4. Conclusions and future work

10. © 2008 Frans Ekman Working Day Movement Model Idea: Combine as many different movement characteristics into one model as possible. We developed our model as an extension to the ONE (Opportunistic Networking Environment) simulator We model movement of nodes  Staying at home  Working at an office  Doing some activity with friends in the evening  Transportation between activities (bus, car or walking) Use of real world maps

11. © 2008 Frans Ekman How it works? (1) A submodel for each activity  Home activity submodel  Office activity submodel  Evening activity submodel  Transport submodels Walking submodel Car submodel Bus submodel

12. © 2008 Frans Ekman How it works? (2) Each node is initially assigned:  A home location  An office location  A favorite meeting spot for evening activity Fewer offices and meeting spots than nodes A configurable percentage moves by car (the rest walks and/or uses the bus)

13. © 2008 Frans Ekman How it works? (3) 1. Node wakes up in the morning 2. Goes to the office and works for a time defined in settings (default 8h) 3. Goes to its favorite meeting spot and does evening activity 4. Goes back home Nodes use the transportation submodels to travel between activities

14. © 2008 Frans Ekman Home activity submodel Nodes walk a certain distance away from the road and stays there until wakeup

15. © 2008 Frans Ekman Office activity submodel An office is a square with the side defined in settings Each node has a desk and moves continuously between its desk and a randomly selected location Pareto distributed pause times 1 2 3

16. © 2008 Frans Ekman Evening activity A group walk according to the MBM model (Random Walk on the map) A group is formed of nodes  Ending their office activity at the same time  Same favorite meeting spot  Group sizes defined in settings Pause at the end Split up and go back home

17. © 2008 Frans Ekman Transport submodels Walking submodel  Uses Dijkstra’s shortest path algorithm  Speed defined in settings Car submodel  Same as walking except higher speed

18. © 2008 Frans Ekman Bus transportation submodel For nodes not owning a car When a node wants to move from one location to the other:  Compare walk distances  Take bus if it results in a shorter path to walk A node can take the bus by walking to the closest bus stop and enter the bus when it stops. The node will exit the bus at the bus stop closest to the destination

19. © 2008 Frans Ekman The map Road net For each node group  Homes  Offices  Meeting spots  Bus route Possible to limit a node group’s movement to a specific area (district)

20. © 2008 Frans Ekman Outline 1. Background 2. Working Day Movement model 3. Validation 4. Conclusions and future work

21. © 2008 Frans Ekman Characterization of movement Contact durations  Limits the amount of data that can be sent Inter-contact times  Time interval a node pair is not in contact  Corresponds to how often nodes have an opportunity to send data Both measured as distributions (CCDF)

22. © 2008 Frans Ekman Experimental setup (1/2) 4 Main districts 3 overlapping districts  e (A and B)  f (A and C)  g (A and D) One large district h covering the whole map g f e h

23. © 2008 Frans Ekman Experimental setup (2/2) Simulation time 7 · 10 5 s 1000 nodes 18 buses 10 nodes moving according to the Shortest Path Map Based Movement (SPMBM) model. (RWP on a map)  Background movement

24. © 2008 Frans Ekman Results: Default settings: Power-law up to 12h like the iMote Bluetooth trace following with an exponential decay Inter-contact times distribution

25. © 2008 Frans Ekman Results: Contact durations

26. © 2008 Frans Ekman Results: Contacts per hour

27. © 2008 Frans Ekman Results: Total- vs. unique encounters for each node plotted on a scatter diagram Working Day MovementRWP

28. © 2008 Frans Ekman Results: Impact of pause times inside office Possible to vary the power-law exponent to meet a specific environment

29. © 2008 Frans Ekman Outline 1. Background 2. Working Day Movement model 3. Validation 4. Conclusions and future work

30. © 2008 Frans Ekman Conclusions and future work Our model captures several real world properties  It produces similar Inter-contact times and contact durations as real world traces  Heterogeneity in contact patterns  Periodic nature Ideas for future work  More submodels  Configuration scripts to deal with complexity  Modeling of traffic

31. © 2008 Frans Ekman Questions?