Localization for Mobile Sensor Networks ACM MobiCom 2004 Philadelphia, PA 28 September 2004 University of Virginia Computer Science Lingxuan Hu and David Evans You are here

2 Location Matters Sensor Net Applications –Mapping –Environment monitoring –Event tracking Geographic routing protocols

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 –Other nodes determine location based on messages received

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

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 Our Goal (Reasonably) Accurate Localization in Mobile Networks Low Density, Arbitrarily Placed Seeds Range-free: no special hardware Low communication (limited addition to normal neighbor discovery)

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

8 Our Approach: Monte Carlo Localization Adapts an approach from robotics localization Take advantage of mobility: –Moving makes things harder…but provides more information –Properties of time and space limit possible locations; cooperation from neighbors Frank Dellaert, Dieter Fox, Wolfram Burgard and Sebastian Thrun. Monte Carlo Localization for Mobile Robots. ICRA 1999.

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

10 MCL Step: Predict Node’s actual position Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, v max

11 Prediction p(l t | l t-1 ) =c if d(l t, l t-1 ) < v max 0 if d(l t, l t-1 ) ≥ v max 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.

12 MCL Step: Predict Node’s actual position Predict: Node guesses new possible locations based on previous possible locations and maximum velocity, v max Filter Filter: Remove samples that are inconsistent with observations Seed node: knows and transmits location r

13 Filtering 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 S S

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

15 Recap: Algorithm Initialization: Node has no knowledge of its location. L 0 = { set of N random locations in the deployment area } Iteration Step: Compute new possible location set L t based on L t-1, the possible location set from the previous time step, and the new observations. L t = { } while (size ( L t ) < N ) do R = { l | l is selected from the prediction distribution } R filtered = { l | l where l  R and filtering condition is met } L t = choose ( L t  R filtered, N )

16 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

17 Convergence Node density n d = 10, seed density s d = 1 The localization error converges in first steps Estimate Error ( r ) Time (steps) v max =.2r, s max =0 v max =r,s =0 v max =r,s =r

18 Speed Helps and Hurts Increasing speed increases location uncertainty ̶ but provides more observations Estimate Error ( r ) v max ( r distances per time unit) s d =1,s min =0,s max =v s d =1,s max =s min =r s d =2,s max =v s d =2,s max =s min =r Node density n d = 10

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 Amorphous: Nagpal, Shrobe and Bachrach. IPSN 2003.

20 Cost Tradeoff: Samples Maintained Estimate Error ( r ) Sample Size ( N ) s d =1,v max =s =.2r s d =1,v max =s =r s d =2,v max =s =.2r s d =2,v max =s =r 1.1 n d = 10 Good accuracy is achieved with only 20 samples (~100 bytes)

21 Cost Tradeoff: Impact of Indirect Seeds Estimate Error (r) Seed Density Direct seeds only Direct and Indirect seeds Indirect seeds help, and cost is low if neighbor discovery is required. n d = 10, v max = s max =.2r

22 Radio Irregularity n d = 10, s d = 1, v max = s max =.2 r Insensitive to irregular radio pattern Estimate Error ( r ) Degree of Irregularity ( r varies ± dr ) MCL Centroid Amorphous

23 Motion n d =10, v max = s max = r Adversely affected by consistent group motion Estimate Error ( r ) Maximum Group Motion Speed ( r units per time step) s d =.3 s d =1 s d = Estimate Error ( r ) Time Random, v max = s max =.2 r Area Scan Random, v max =0, s max =.2 r Scan Stream and Currents Random Waypoint vs. Area Scan Controlled motion of seeds improves accuracy

24 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

25 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

26 Thanks! People: Tarek Abdelzaher, Tian He, Anita Jones, Brad Karp, Kenneth Lodding, Nathaneal Paul, Yinlin Yang, Joel Winstead, Chalermpong Worawannotai Funding: NSF ITR, NSF CAREER, DARPA SRS