Localization for Mobile Sensor Networks

1. Localization for Mobile Sensor Networks ACM MobiCom 2004 Lingxuan Hu David 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 r 4 DV-HOP [Niculescu & Nath, 2003] Amorphous [Nagpal et. al, 2003] 1 2 7 3 1 4 3 5 2 4 8 3 3 6 4 4 5 Works well with a few, well-located seeds and regular, static node distribution. Works poorly if nodes move or are unevenly distributed.

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 Nodes stationary, seeds moving NASA Mars Tumbleweed Image by Jeff Antol Nodes moving, seedsstationary Nodes and seedsmoving

8. MCL: Initialization Node’s actual position Initialization: Node has no knowledge of its location. L0 = { set of N random locations in the deployment area }

9. MCL Step: Predict Node’s actual position Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, vmax

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

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

12. Filtering S S Indirect Seed If node doesn’t hear a seed, but one of your neighbors hears it, node must be within distance (r, 2r] of that seed’s location. Direct Seed If node hears a seed, the node must (likely) be with distance r of the seed’s location

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 2 Node density nd = 10, seed density sd = 1 1.8 1.6 1.4 1.2 v =.2 r s =0 max max 1 , Estimate Error (r) 0.8 v = r , s =0 max max 0.6 0.4 v = r , s = r max max 0.2 0 0 5 10 15 20 25 30 35 40 45 50 Time (steps) The localization error converges in first 10-20 steps

16. Seed Density 3 nd = 10, vmax = smax=.2r 2.8 2.6 Centroid: Bulusu, Heidemann and Estrin. IEEEPersonal Communications Magazine. Oct2000. Amorphous: Nagpal, Shrobe and Bachrach. IPSN 2003. 2.4 Centroid 2.2 2 1.8 1.6 Estimate Error (r) 1.4 1.2 Amorphous 1 0.8 0.6 0.4 MCL 0.2 0 0.1 0.5 1 1.5 2 2.5 3 3.5 4 Seed Density Better accuracy than other localization algorithms over range of seed densities

17. Radio Irregularity 2 nd= 10, sd = 1, vmax = smax=.2r 1.8 1.6 Centroid 1.4 1.2 1 Amorphous Estimate Error (r) 0.8 0.6 MCL 0.4 0.2 0 0 0.1 0.2 0.3 0.4 0.5 Degree of Irregularity (r varies ±dr) Insensitive to irregular radio pattern

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