1. Localization for Mobile Sensor Networks ACM MobiCom 2004 Lingxuan HuDavid Evans Department of Computer Science University of Virginia

2. Localization Location Awareness Importance –Environment monitoring –VehicleTracking –Location based routing – save significant energy –Improve caching behavior –Security enhanced (wormhole attacks)

3. Determining Location Direct approaches –GPS Expensive (cost, size, energy) Only works outdoors, on Earth –Configured manually Expensive Not possible for ad hoc, mobile networks Indirect approaches –Small number of seed nodes Seeds are configured or have GPS –Dependence on special hardware –Requirement for particular network topologies

4. Hop-Count Techniques DV-HOP [Niculescu & Nath, 2003] Amorphous [Nagpal et. al, 2003] Works well with a few, well-located seeds and regular, static node distribution. Works poorly if nodes move or are unevenly distributed. r 1 1 2 2 3 3 3 3 4 4 4 4 4 5 5 6 7 8

5. Local Techniques Centroid [Bulusu, Heidemann, Estrin, 2000]: Calculate center of all heard seed locations APIT [He, et. al, Mobicom 2003]: Use triangular regions Depend on a high density of seeds (with long transmission ranges)

6. Environment considered Conditions –No special hardware for ranging is available –The prior deployment of seed (beacons) nodes is unknown –The seed density is low –The node distribution is irregular –Nodes and seeds can move uncontrollably.

7. Scenarios NASA Mars Tumbleweed Image by Jeff Antol Nodes moving, seeds stationary Nodes and seeds moving Nodes stationary, seeds moving

8. MCL: Initialization Initialization: Node has no knowledge of its location. L 0 = { set of N random locations in the deployment area } Nodes actual position

9. MCL Step: Predict Nodes actual position Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, v max

10. Prediction Assumes node is equally likely to move in any direction with any speed between 0 and v max. Can adjust probability distribution if more is known.

11. MCL Step: Filter Nodes actual position Filter: Remove samples that are inconsistent with observations Seed node: knows and transmits location r

12. Filtering Indirect Seed If node doesnt hear a seed, but one of your neighbors hears it, node must be within distance (r, 2r] of that seeds location. Direct Seed If node hears a seed, the node must (likely) be with distance r of the seeds location S S

13. Resampling Use prediction distribution to create enough sample points that are consistent with the observations.

14. Results Summary Effect of network parameters: –Speed of nodes and seeds –Density of nodes and seeds Cost Tradeoffs: –Memory v. Accuracy: Number of samples –Communication v. Accuracy: Indirect seeds Radio Irregularity: fairly resilient Movement: control helps; group motion hurts

15. Convergence Node density n d = 10, seed density s d = 1 The localization error converges in first 10-20 steps 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 05101520253035404550 Estimate Error ( r ) Time (steps) v max =.2r, s max =0 v max =r,s =0 v max =r,s =r

16. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 0.10.511.522.533.54 Estimate Error ( r ) Seed Density MCL Centroid Amorphous Seed Density n d = 10, v max = s max =.2r Better accuracy than other localization algorithms over range of seed densities Centroid: Bulusu, Heidemann and Estrin. IEEE Personal Communications Magazine. Oct 2000. Amorphous: Nagpal, Shrobe and Bachrach. IPSN 2003.

17. Radio Irregularity n d = 10, s d = 1, v max = s max =.2 r Insensitive to irregular radio pattern 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 0.10.20.30.40.5 Estimate Error ( r ) Degree of Irregularity ( r varies ± dr ) MCL Centroid Amorphous

18. Future Work: Security Attacks on localization: –Bogus seed announcements Require authentication between seeds and nodes –Bogus indirect announcements Retransmit tokens received from seeds –Replay, wormhole attacks Filtering has advantages as long as you get one legitimate announcement Proving node location to others

19. Summary Mobility can improve localization: –Increases uncertainty, but more observations Monte Carlo Localization –Maintain set of samples representing possible locations –Filter out impossible locations based on observations from direct and indirect seeds –Achieves accurate localization cheaply with low seed density

20. THANK YOU