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Dynamic Location Discovery in Ad-Hoc Networks

Session 7. Dynamic Location Discovery in Ad-Hoc Networks. Andreas Savvides, Athanassios Boulis and Mani B. Srivastava (asavvide,boulis,mbs@ee.ucla.edu) Networked and Embedded Systems Lab(NESL) http://nesl.ee.ucla.edu Electrical Engineering Department. Known Location. Unknown Location.

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Dynamic Location Discovery in Ad-Hoc Networks

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  1. Session 7 Dynamic Location Discovery in Ad-Hoc Networks Andreas Savvides, Athanassios Boulis and Mani B. Srivastava (asavvide,boulis,mbs@ee.ucla.edu) Networked and Embedded Systems Lab(NESL) http://nesl.ee.ucla.edu Electrical Engineering Department

  2. Known Location Unknown Location What is location discovery? • Given a network of sensor nodes where a few nodes know their location how do we calculate the location of the nodes?

  3. Why? • Support Location Aware Applications • Navigation • Track Objects • Sensor Networks – report event origins • evaluate network coverage • assist with routing

  4. Basic Concepts • Distance measuring methods • Signal Strength • Uses RSSI readings and wireless propagation model • Time based methods • ToA, TDoA • Used with radio, IR, acoustic, ultrasound • Angle of Arrival (AoA) • Measured with directive antennas or arrays

  5. Sines Rule B b A a c C Cosines Rule Basic Concepts II Hyperbolic Trilateration Triangulation Multi-lateration • Considers all available beacons

  6. Existing Technologies INFRASTRUCTURE: • Automatic Vehicle Location system (AVL) • Base stations keep track of police cars ( uses time based and signal strength methods) • GPS, Loran • 911 Emergency Location System (ToA, TDoA) • BAT System(AT&T Cambridge Labs), Cricket (MIT) • RADAR – indoor, uses signal strength maps • RFID tags – IR proximity AD-HOC: • Picoradio (UC Berkeley) – indoor, based on signal strength maps • GPS-less outdoor localization (Bulusu et. al) – proximity based

  7. Location Discovery in Ad-Hoc Networks • No infrastructure support • GPS may not always work • Costly, Power Hungry, does not work everywhere • Our Approach • Use RSSI for measuring node separation • But how should the beacons be placed? • Multiple tradeoffs still an open problem

  8. B B B Long Range Beaconing • Long Range Beaconing Advantages: • Multi-hop Coverage • Works well even in low densities • Disadvantages: • Low fault tolerance • Requires Dedicated Beacons • Some infrastructure is required

  9. Beacon Our Approach • Single hop beaconing • Iterative multilateration • Dynamic estimate the wireless channel parameters • Can be done in conjunction with routing Advantages: • Data packets are also act as beacon signals • Distributed – relies on neighborhood information • Fault tolerant • Location discovery is almost free!!

  10. Initial Beacon Step 1: becomes beacon Step 2: becomes beacon Step 3: becomes beacon Iterative Multilateration • Start with a small number of beacons • Number of beacons increases as more nodes estimate their positions

  11. Challenges • Multi-path and shadowing effects • Difficult to work in indoor environments • Beacon placement problem • Bad geometry can affect the quality of the solution • Variable wireless channel characteristics • signal propagation differs from place to place (n=1.5 ... 6)

  12. Solution • Setup as an over-constrained optimization problem and solve for • Wireless propagation model parameters • Node Locations

  13. Problem Setup Wireless Channel Model Error Distance Representation

  14. Optimization Problem • This is a non-linear optimization problem • Hard to compute in one step • We solve the problem in 2 phases over multiple iterations • Keep in mind beacon errors!

  15. Channel Estimator Reset Locations Location Estimator Convergence Criteria? NO YES Two-Phase Approach • Obtain a propagation model estimate based on initial set of beacons • Certainly of node estimates used as weights for the channel estimate • Follow a rip-up and retry method until a predefined set of constraints is met

  16. Simulations 100 Nodes 100 x 100 grid Range = 10 Beacons = 10

  17. Without Beacon Error

  18. With Beacon Error = 10 %

  19. Effect of Beacon Error

  20. Implementation & Measurements • Implemented Location Discovery Algorithm as part of DSDV routing protocol in SensorSim • Obtained RSSI measurements using RSC nodes in outdoor environments • Analyzing the results

  21. Conclusions and Future Work • Radio signal strength methods can provide a low cost scalable location discovery • BUT does not work well indoors • experimenting with ultrasound • Exploring Collaborative Multilateration • Beacon placement problem needs to be explored

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