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1 Dynamic Location Discovery in Ad-Hoc Networks Andreas Savvides, Athanassios Boulis and Mani B. Srivastava.

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Presentation on theme: "1 Dynamic Location Discovery in Ad-Hoc Networks Andreas Savvides, Athanassios Boulis and Mani B. Srivastava."— Presentation transcript:

1 1 Dynamic Location Discovery in Ad-Hoc Networks Andreas Savvides, Athanassios Boulis and Mani B. Srivastava Networked and Embedded Systems Lab(NESL) Electrical Engineering Department Session 7

2 2 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? Known Location Unknown Location

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

4 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 5 Basic Concepts II Hyperbolic Trilateration Triangulation Multi-lateration –Considers all available beacons A B C a b c Sines Rule Cosines Rule

6 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 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 8 Long Range Beaconing Advantages: –Multi-hop Coverage –Works well even in low densities Disadvantages: –Low fault tolerance –Requires Dedicated Beacons –Some infrastructure is required B B B

9 9 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!! Beacon

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

11 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= )

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

13 13 Problem Setup Wireless Channel Model Error Distance Representation

14 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 15 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 Channel Estimator Location Estimator Convergence Criteria? Reset Locations NOYES

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

17 17 Without Beacon Error

18 18 With Beacon Error = 10 %

19 19 Effect of Beacon Error

20 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 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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