Introduction to Locating Systems in Ubiquitous Computing and Sensor Networks

Amir Haghighat

Why location?

Ubiquitous Computing (ubicomp) Context-aware computing Search and rescue

Sensor Networks Environmental monitoring Geographic routing Target tracking

Man discovers mobile computing…

Now, where’s the nearest

place I can buy shoulder pads?!

…and quickly wants location-enhanced computing.

Why not GPS?

Ubicomp GPS does not work indoors GPS works poorly in urban canyons

Sensor Networks Power, cost, and size issues

Outline

Localization techniques Taxonomy Mini-survey of location systems in

ubiquitous computing “Beep: 3D Indoor Positioning Using

Audible Sound“ Locating systems in sensor

networks

Location Sensing Techniques

Triangulation Lateration Angulation

Scene analysis Proximity

Lateration

•Time of flight

•Attenuation

Angulation

Phased antenna arrays provide angle of arrival

Scene Analysis Uses features of a scene observed from a

particular vantage point to draw conclusions about the location of the observer or of objects in the scene.

No distance/angle measurements Two types of scene analysis:

Static: observed features looked-up in predefined dataset that maps them to location(i.e. MSR RADAR)

Differential: Differences in the scene correspond to movements of observer

Proximity Detecting physical contact (i.e. human skin) Monitoring wireless cellular access points

Observing automatic ID systems (i.e. RFID tracking of livestock)

Location System Properties Physical Position vs. Symbolic Location Absolute vs. Relative Localized location computation (privacy and

power issues) Accuracy and Precision

i.e. 1 meter accuracy, 90% of time Scale Recognition Cost

Time and money Limitations

Mini-survey of Location Systems in Ubiquitous Computing

Media: infrared, (ultra)sound, radio frequency (RF), vision

Active Badge

Users carry badges that emit diffuse infrared signals

One base-station per room interference from fluorescent light

and sunlight

Olivetti Active Badge (right) and a base station (left)

Active Bat RF and ultrasound Lateration performed by central

server 9cm 95% of time, 1 base-station per

10m2

Cricket RF and ultrasound Privacy and

decentralization in mind

Symbolic or physical location

4*4 ft regions, ~100% of time, 1 beacon per 16 ft2

RADAR 802.11 signal

strengths from 3 APs construct a “signature” for every location

“Offline phase” and “Online phase”

3 meter accuracy, 50% of time, having 3 APs

E911

FCC initiative 100m, 67% and 300m, 95% Possible solutions: GPS, proximity,

angle of arrival, time difference of arrival

Impacts: Network impact, handset impact, legacy handsets

Place Lab Uses 802.11 and GSM beacons, whose positions are

known 802.11 AP locations from war drivers

Over 2 million known AP positions GSM tower locations from FCC’s database

20-30m median accuracy, 100% coverage in Seattle GPS works less accurately in urban areas (i.e. downtown)

Bayes Filter

Easy Living

Real-time 3D cameras provide stereo-vision positioning for home environment

Move from person tracking to capturing broader context

CSEM (www.csem.ch)

The camera emits an RF modulated optical radiation field (typically 20 MHz or higher) in the infra-red spectrum. This signal is diffusely backscattered by the scene and detected by the camera. Every pixel is able to demodulate the signal and detect its phase, which is proportional to the distance of the reflecting object.

Beep: 3D Indoor Positioning Using Audible Sound

Atri Mandal, Cristina V. Lopes, Tony Givargis,Amir Haghighat, Raja Jurdak, Pierre Baldi

School of Information and Computer Sciences

University of California, Irvine

Presented by:Amir Haghighat

                                    

         

Overview

Motivation Architecture Results Conclusion Future Work

Introduction and Motivation

+ =

Virtual World Physical World

or

Required Characteristics

Fairly accurate (~1 meter) No additional h/w requirement on

the part of the user Fairly cheap to deploy

Beep Architecture

Triangulation

2 2 2 2( ) ( ) ( ) 1,2,i i i ix X y Y z Z r i n

where [Xi, Yi, Zi] is the position of the ith sensor.

S3

S2

S1

r3

r2

r1

Delay Elimination

2 2 2 2( ) ( ) ( ) 1, 2,i i ix X y Y z R d i n

Results

Error Estimation

Results

Accuracy and Precision:

• 2D: 2 ft (97%)

• 3D: 3 ft (95%)

Beep Performance in Noisy Environment

Quiet Noisy

Beep in noisy environment:2 feet 90% of time, given the location's distance was not greater than ~18 feet from any 3 sensors (1 sensor per ~160 ft2 =15 m2)

BeepBeep Architecture

BeepBeep Performance in Noisy Environment

Quiet Noisy

BeepBeep in noisy environment:2 feet 80% of time, given the location's distance was not greater than ~15 feet from any 3 sensors (1 sensor per ~110 ft2 =10 m2)

Related Work

UCLA Pros: Accurate, mainly targeting

wireless sensor networks Cons: CPU clocks have to be synched,

data is processed offline, no absolute locations

Conclusion

Fairly accurate (2 ft, 97% of time) No additional h/w requirement on

the part of the user (virtually all roaming devices have speakers, WLAN compatibility?)

Fairly cheap to deploy (10,000 sq. ft => ~ $5000 at $100 per sensor module)

Future Work Eliminate the need for 802.11 on

the part of the user Test in an authentic environment

(UCI bookstore?) HCI issues Accuracy in presence of authentic

noise Less annoying sound than a

monotone 4000 Hz

GPS-Less Low-Cost Outdoor Localization for Small Devices, UCLA, 2000

Node localizes itself as the centroid of the reference points, from which it can receive beacon signals (proximity-based)

Beacon signals are assumed to overlap in space, not in time

Location of a node is estimated, using the locations of k reference points whose beacon signals are received

Xest = (Xi1 + Xi2 + … + Xik) / k Yest = (Yi1 + Yi2 + … + Yik) / k

APS (Ad-Hoc Positioning System), Rutgers, 2001

Each beacon broadcasts a packet with its location and a hop count, initialized to one.

The hop-count is incremented by each node as the packet is forwarded.

Each node maintains a table of minimum hop-count distances to each beacon

APS (Ad-Hoc Positioning System), Rutgers, 2001

A beacon can use the absolute location of another beacon along with the minimum hop count to that beacon to calculate the average distance per hop.

The beacon broadcasts the average distance per hop, which is forwarded to all nodes.

Individual nodes use the average distance per hop, along with the hop count to known beacons, to calculate their local position using lateration

Positioning node within 1/3 radio range in dense networks

Project Overview

Chris Karlof and David Wagner, "Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures", Elsevier's AdHoc Networks Journal, Special Issue on Sensor Network Applications and Protocols, September 2003.

Sybil attack

HELLO flood attack

Karlok and Wagner explore potential attacks on sensor networks and their countermeasures

I plan to work on adversary node localization Absolute or relative

position Proximity or RF signal

attenuation characteristics Kalman filter for tracking