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

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

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

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

Localization Basics

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)

Received Signal Strength

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

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

Ranging Characteristics ToA using RF and Ultrasound

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

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

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

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:

Atomic Multilateration

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

Iterative Multilateration Accuracy

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

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

Collaborative Multilateration and Beacon density

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

Beacon requirements

Experimental Setup

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)

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

Energy Consumption Comparison

Traffic Comparison

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

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?