GPS-less Low-Cost Outdoor Localizationfor Very Small Devices

Nirupama Bulusu, John Heidemann, and Deborah Estrin

Design Goals

RF-based Receiver-based Ad hoc Responsive Low Energy Adaptive Fidelity

In this paper …

Related Work Algorithm for Coarse-grained

Localization Implementation Results

Related Work

Fine-Grained Localization

Coarse-Grained Localization

Fine-Grained Localization

Range Finding Timing Signal Strength Signal Pattern Matching

Directionality Based Electrical Phasing Small aperture Direction Finding

Timing

Time of flight of communication signal Signal Pattern

Global Positioning System Local Positioning System Pinpoint’s 3D-iD

Different modalities of communication Active Bat

Signal Strength

Attenuation of radio signal increases with increasing distance

RADAR Wall Attenuation Factor based Signal

Propagation Model RF mapping

Signal Pattern Matching

Multi-path phenomenon Signature unique to given location Data from single point sufficient Robust Substantial effort needed for

generating signature database

Fine-Grained Localization

Range Finding Timing Signal Strength Signal Pattern Matching

Directionality Based Electrical Phasing Small aperture Direction Finding

Small Aperture Direction Finding

Used in cellular networks Requires complex antenna array Disadvantages

Costly Not a receiver based approach

Coarse-Grained Localization

Infrared Active Badge – fixed sensors Fixed transmitters Disadvantages

Scales poorly Incurs significant installation,

configuration and maintenance costs

Localization Algorithm Multiple nodes serve as Reference points

Reference points transmit periodic beacon signals containing their positions

Receiver node finds reference points in its range and localizes to the intersection of connectivity regions of these points

An Idealized Radio Model

Perfect spherical radio propagation

Identical transmission range for all radios

Terms

d : Distance b/w adjacent ref. points

R : Transmission range of reference point

T : Time interval between two successive

beacons

t : Receiver sampling time

Nsent(i,t) : No. of beacons sent by Ri in time t

Nrecv(i,t) : No. of beacons sent by Ri received in t

contd…

CMi : Connectivity metric for Ri

S : Sample size for

connectivity metric

CMthresh : Threshold for CM

(Xest, Yest) : Estimated location of

receiver

(Xa, Ya) : Actual location of receiver

contd… CMi = (Nrecv(i,t) / Nsent(i,t)) * 100

t = (S + 1 + ε) * T , 0 < ε « 1

k = No. of reference points within connectivity range

(Xest, Yest) = (avg(Xi1+…+Xik), avg(Yi1+…+Yik))

LE = Sqrt( (Xest – Xa)2 + (Yest – Ya)2)

Model

Validation of Model

78 points measured

68 correct matches

Mismatches were all at the edge

Error <= 2m

CMthresh = 90

R = 8.94m

Results

T = 2s

S = 20

t = 41.9s

d = 10m

contd…

Average error 1.83m

Standard deviation 1.07m

Max. error 4.12m

contd…

contd…

Simulation to check the effect of increasing the overlap of ref. points

Calculated for 10,201 points

NO MONOTONIC INCREASE

Discussion and Future Work

Collision Avoidance Tuning for Energy Conservation Non-uniform reference point

placement Reference Point Configuration Robustness Adaptation to Noisy Environment

Questions

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