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Dynamic Location Discovery in Ad-Hoc Networks

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

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

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

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!!

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 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 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 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 Convergence Criteria? Reset Locations NO YES

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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