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 = , k=

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?