introduction to locating systems in ubiquitous computing and sensor networks
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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
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