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

1. Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors Authors: Andreas Avvides, Chih-Chieh Han and Mani Strivastava Presenter: Ram Gudavalli 10/28/03

2. Localization • Localization – determining the physical position of an object w.r.t. some coordinate system • Applications to sensor networks • Location based routing • Provide location feedback for a sensed phenomena (such as fire) • Tracking

3. Obstacles in GPS-based Localization • GPS cannot work indoors • Power consumption too high • Cost prohibitive • Increases node size

4. Localization Basics • Distance (or Angle) Ranging • Received Signal Strength • Time of Arrival • Angle of Arrival • Distance (or Angle) Estimation • Hyperbolic Tri-lateration • Triangulation • Maximum Likelihood Estimation

5. Localization Basics

6. Ranging Characteristics • Received Signal Strength • RF signal attenuation as a function of distance • For signal strength measurements, used WINS nodes • Inconsistent for most settings (Indoors, between buildings, parking lot) • Presented least square fit of two separate power levels under an idealized setting (Football field with nodes at ground level)

7. Received Signal Strength

8. Received Signal Strength • Multipath, Fading, Shadowing problems • Range varies with altitude of radio antenna • 30m at ground level • 100m at height of 1.5m • Nodes must be calibrated to common scale

9. Ranging Characteristics • ToA using RF and Ultrasound • The time difference between RF and ultrasound • For ToA measurements use Medusa nodes • To estimate the speed to sound, perform a best line fit using linear regression t = sd + k s = speed of sound in timer ticks d = estimated distance between the two nodes k = constant For this model s = 0.4485, k= 21.485831

10. Ranging Characteristics ToA using RF and Ultrasound

11. RF/Ultrasound ToA • Medusa node • 3m ultrasonic range • Ranging accurate to 2cm

12. Signal Strength vs. ToA Ranging • ToA is much more reliable than received signal strength • Signal strength is greatly affected by amplitude variations • Time difference of received signals is a more robust metric • ToA less susceptible to multipath effects because shortest-path signal is used • AHLoS uses ToA ranging

13. AHLoS Localization Algorithm • Beacon nodes • Subset of nodes that have a known location • Broadcast location to their neighbors • Unknown nodes • Nodes with unknown location • Measure their separation from their neighbors • Use ranging information and beacon location information to estimate their position • Once a position is established, an unknown node becomes a beacon node

14. Atomic Multilateration • Unknown node must be within one hop of at least 3 beacon nodes • Maximum Likelihood estimate of the node's position can be obtained by taking min mean square estimate of a system of distance error equations of the form:

15. Atomic Multilateration

16. Iterative Multilateration • Atomic multilateration is used a basic primitive. • Determine position of unknown nodes with maximum number of beacons • When location is estimated, the node becomes a beacon • Disadvantage • accumulation of error when unknown nodes which become beacons are used in estimation

17. Iterative Multilateration Accuracy

18. Collaborative Multilateration • Position estimation by considering use of location information over multiple hops • Conditions for participation • A node is a participating node if it is either a beacon or if it is an unknown with at least three participating neighbors • A participating node pair is a beacon-unknown or unknown-unknown pair of connected nodes where all unknowns are participating • Can be used is assist iterative multilateration where beacon density is low and requirement for atomic multilateration not met

19. Collaborative Multilateration Most basic case for collaborative multilateration Definition given is not complete though!!

20. Collaborative Multilateration and Beacon density

21. Node and Beacon Placement • Localization success depends on network connectivity and beacon placement • Probability of a node having at least 3 beacon neighbors

22. Beacon requirements

23. Experimental Setup

24. Centralized vs. Distributed Schemes • Centralized scheme • Ranging measurements and beacon locations are collected at central base station • Computed location values are forwarded back to the nodes • Drawbacks • Route to the central node must be known • Time synchronization problem (change in network topology) • Requires pre-planning • Energy consumption much higher • Robustness of system suffers (central stations fail or nodes close to stations die)

25. Centralized vs. Distributed Scheme • AHLoS uses Distributed scheme • Distributed setup has 6 to 10 times less communication overhead than centralized setup • Network traffic increases in centralized setup as the number of beacons increase • In distributed scheme, network traffic decreases as the percentage of beacons increases • Centralized implementation gives more accurate

26. Energy Consumption Comparison

27. Traffic Comparison

28. Conclusion • ToA ranging is much more accurate than Received Signal Strength • Present a multilateration algorithm to perform dynamic ad-hoc localization • Iterative multilateration accumulates error • Distributed scheme for implementing this algorithm is preferable

29. Questions • What is the error rate for collaborative multilateration? • How well does algorithm work in nodes without 3 participating neighbors? • Is it fair to assume a uniformly dense network?