Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors

1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors Savvides, C. C. Han, M. B. Srivastava Networked and Embedded Systems Lab University of California, Los Angeles {asavvide, simonhan, mbs}@ee.ucla.edu

2. Localization in Sensor Networks • Context awareness in applications • Network coverage analysis • Report origins of events • Temperature at a specific part of the room • Locate/track objects, people, robots • Assist with routing • Why not GPS? • Costly, power hungry, requires line-of-sight, large form factor, accuracy

3. Problem Statement Iterative Multilateration • Estimate node locations in an ad-hoc network of nodes • Uniformly deployed nodes on a flat plane • Ad-Hoc Localization System(AHLoS) • Every node contributes to process • Small fraction of nodes (beacons) are initially aware of their locations • Distributed • Robust to surrounding environment changes and node failures • Energy Efficient • Scalable • Inter-node ranging uses(RSSI, ultrasound) Collaborative Multilateration

4. Ranging • Localization relies on the ability of nodes to measure distances • Physical layer effects may bias ranging => empirical study • RF Received Signal Strength Indicator (RSSI) • RF + Ultrasound Time-of-Arrival(ToA) Measurement 1 Position Estimate Multilateration or other Measurement 2 Measurement n

5. Target Platforms Rockwell WINS Node (RSSI) Medusa Experimental Node (ToA) • Atmel AVR 8535 MCU • RFM Radio • 40KHz Ultrasound • 200MHz StrongARM • DECT Radio from Connexant

6. Platform Characterization Ultrasound ToA RSSI in football field Max range 3m, accuracy 2cm Max range 20m, accuracy 7m

7. Beacon Unknown Localization Algorithms • Atomic Multilateration (base case) • Solution similar to GPS • Formulated as a least squares problem • Requires 3 beacons (if more than 3 beacons are available, the ultrasound propagation speed is also estimated) • May not work if beacons are badly aligned

8. 2 1 0 4 3 Atomic Multilateration Minimize over all This can be linearized to the form where MMSE Solution:

9. Iterative Multilateration • Each node that calculates its location it becomes a beacon that can help other nodes to calculate their locations • Allows Distributed Operation • Problem: • Error accumulation • Reasonable results can be achieved for small networks since ultrasonic distance measurement is accurate • Error accumulation can be limited using weights

10. Iterative Multilateration • Each node that calculates its location it becomes a beacon that can help other nodes to calculate their locations • Allows Distributed Operation • Problem: • Error accumulation • Reasonable results can be achieved for small networks since ultrasonic distance measurement is accurate • Error accumulation can be limited using weights

11. Iterative Multilateration • Each node that calculates its location it becomes a beacon that can help other nodes to calculate their locations • Allows Distributed Operation • Problem: • Error accumulation • Reasonable results can be achieved for small networks since ultrasonic distance measurement is accurate • Error accumulation can be limited using weights

12. Iterative Multilateration Accuracy 50 Nodes, 20x20 room, range=3m, 10% beacons 20mm white gaussian ranging error

13. Collaborative Multilateration • Considers location information over multiple hops • More than one unknown node positions are estimated simultaneously • Set of nodes considered MUST have a unique solution

14. Collaborative Multilateration Results

15. Node and Beacon Placement • Nodes are assumed to have a uniform distribution • The success of the iterative multilateration process depends on node connectivity and beacon availability Node range = 10m

16. Node vs. Initial Beacon Densities % Resolved Nodes Total Nodes % Initial Beacons Uniformly distributed deployment in a field 100x100. Node range = 10 Results include only iterative multilateration

17. Experimental Setup • Initially Simulated in ns-2 on top of DSDV • Testbed Implementation • Ultrasound transmitted simultaneously with RF • Distributed Computation

18. Centralized or Distributed? • Where should the computation for location estimation take place? • At a central node? • Inside the network? • How does this decision facilitate • Scalability • Robustness • Energy efficiency

19. Centralized Cons A route to a central point Time synchronization High latencies for location updates Central node requires preplanning More traffic => higher power consumption Centralized Pros Can solve more accurately Distributed Pros More robust to node failure Less traffic => less power Better handling of local environment variations Speed of ultrasound Radio path loss Rapid updates upon topology changes No time synch. required Centralized vs. Distributed Localization

20. Energy Characterization • Ultrasound penalty is the same for both cases so we did not characterize it • Measured AVR MCU and RFM radio • Total Power - 20mW

21. Localization Energy Cost Node range 10m, 20% beacons Central node at the center of the network

22. Related Work • Centralized • RADAR [Bahl et. al] • Active BAT [Harter et. al] • Proximity • Cricket System [Priyantha et. al] • Ad-Hoc Distributed Proximity • GPS Less Localization [Bulusu et. al] • Ad-Hoc Centralized • Convex Optimization Methods [Doherty et. al]

23. Conclusions and Future Work • Initial results are encouraging (20 cm accuracy) • A distributed implementation is desirable • This is only the beginning! • Medusa II Node under development • 20 meter ultrasound range • More computation power • New 3D test bed • Collaborative Multilateration is promising and should be further explored • Many new applications are emerging!

24. AHLoS Website http://nesl.ee.ucla.edu/projects/ahlos