Signal Strength-Based Localization in Indoor Wireless Networks

Copyright© 2002 Avaya Inc. All rights reserved Avaya – Proprietary Use pursuant to Company instructionsCopyright© 2003 Avaya Inc. All rights reserved Avaya – Proprietary Use pursuant to Company instructions

Signal Strength-Based Localization in Indoor Wireless Networks

A.S. KrishnakumarAvaya Labs

[email protected]

5 April 2006

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Outline

• Introduction

• Applications of Location Information

• Location determination in Radio Networks

• Problem definition for 802.11 Networks

• Current research and examples

• Limits of location determination

• Conclusion

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Introduction

A wireless terminal is untethered and may be mobile. To deliver a variety of services, it may be desirable to know the location of a wireless terminal with some degree of precision.

• Can we estimate the location – with enhancements to the end device?

– Without enhancements?

• What techniques are available?

• How accurately can we make this determination?

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Outline

• Introduction






• Conclusion

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Applications Wireless location estimation is an important enabling

technology to provide value-added location-aware services:

In the enterprise:

– Using closest resource in the enterprise

– Privileges based on security regions

– Enhanced e911 services, etc.

In Public spaces:– Emergency services

– Map/route information

– Recreation/Entertainment information

– Many others

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Outline

• Introduction






• Conclusion

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Location Determination in Radio Networks

Location estimation in indoor environments can be based on different characteristics of radio signals

• Signal strength (RSSI) (e.g., RADAR)

• Angle of arrival - AOA

• Time of arrival - TOA

• Time difference of arrival – TDOA (e.g., Cricket)

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M

B1 B2

B3 Angle of Arrival

1

M

B1 B2

B3

2

3

Time of Arrival

1-2

M

B1 B2

B3

2

3

1

2-3

3-1

TDOA

M (S1, S2, S3)

B1 B2

B3 Received Signal Strength

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Is this new?

It has all been done before!

• Radar

• LORAN

• GPS

• etc.

So what is new ?

A lot!

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Outline

• Introduction






• Conclusion

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Location in 802.11 Networks

Desirable Characteristics:– Use existing hardware without enhancements

– Ideally no client assistance

– Simple to deploy and use

– Adequate accuracy

Complicating Factors:– Multi-path indoor propagation environment

– Heterogeneous terminals

– Site Engineering

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Location in 802.11 Networks

Attention has been focused on RSSI-based techniques since they:

• Can be implemented with currently available hardware

• Are reasonably accurate

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Issues in Wireless Location Estimation

• Location accuracy

• Deployment and cost of ownership

• Management

• Security considerations

• Zero-profiling techniques

• Deployment for coverage vs. location estimation

• Techniques for model adaptation

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RSSI-based Techniques

Client-based approach:

– Client measures the signal strength from “visible” Access Points and this information is used to locate the client

Infrastructure-based approach:

– Deploy wireless sniffers that monitor client activity and measure signal strength information

– No client changes required

– Sniffers can also be used for other monitoring and security applications

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Grouping of RSSI-based Techniques

Bayesian Nets [24]

Nibble [9]

Youssef et al. [40]

HORUS [41]

Bayesian Nets [24]

Abnizova et al. [1]

Probabilistic

LEASE [22]

RADAR (Bahl et al.) [4]

Prasithsangaree et al. [31]

Pandey et al. [30]

Deterministic

Infrastructure-basedClient-based

†

† The reference numbers here correspond to the bibliography in A.S. Krishnakumar et al., CollaborateCom 2005.

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Outline

• Introduction






• Conclusion

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RSSI-based Techniques• Profiling-Based (Collected data is the model)

• Needs a lot of data collection to build the model– Take signal strength measures at many points in the site and do a

closest match to these points in signal strength vector space. [e.g., RADAR; INFOCOM 2000]

– Build a prior probability distribution at many chosen points and use posterior distributions to determine best estimate of location [e.g., Robotics; IROS 2003]

• Use physical characteristics of signal strength propagation and build a model augmented with a wall attenuation factor

• Needs detailed (wall) map of the building; model portability needs to be determined

– [e.g., RADAR; INFOCOM 2000] based on [Rappaport 1992]

• Adaptation– Environmental and other changes require model rebuilding

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Steps in Profiling-based Techniques

• Data Collection– Collect signal strength measurements from all the

APs at many points in the area of interest

• Model Generation– Generate a model; could be the parameters of a

propagation model or a signal-strength vector map or something else

• Location Determination– Given a signal strength measurement, estimate

the location based on:• Euclidean distance in signal space• Maximum likelihood estimate• Some other measure

Off-

line

On

-lin

e

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A Deterministic Technique - RADAR

• Data Collection – Collect many measurements at each location on the grid

• Model building:– The same as the collected data

• On-line Estimation– Select the location that is the nearest neighbor in signal-

strength space to the measured signal strength vector

• Reported median error ~2.9m

Further details may be found in Bahl et al., Infocom 2000

Based on Profiling:

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A Deterministic Technique - RADAR

• Data Collection – Collect many measurements at different distances with and without line of sight

• Model building:– Estimate propagation model parameters and wall

attenuation– Use the model to generate a signal strength map

• On-line Estimation– Select the location that is the nearest neighbor in signal-

strength space to the measured signal strength vector

• Reported median error ~4.3m

Further details may be found in Bahl et al., Infocom 2000

Based on Propagation Model:

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LEASE – Location Estimation Assisted by Stationary Emitters

• Automatic adaptation to changes

• “Profiling” handled automatically by using SEs

• There is a mapping between client- and infrastructure-based deployments (and LEASE)

– Interpretation for Client-based deployment

– Sniffers co-located with APs

– Points where you profile signal strength from APs = points where you place SEs

• Signal Strength model for a sniffer needs to be built using measured signal strengths from SEs

– Model using minimal number of SEs (“profiled” points)

• Our approach to build signal strength model:

– Treat the problem as a data modeling problem

• Median error ~5m (Further details in Krishnan et al. Infocom 2004)

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Components of the LEASE system• Uses sniffers, stationary

emitters (SEs) and a location estimation engine (LEE)

• SEs

– Cheap, battery operated devices at known locations

– Transmit a few packets periodically

• Sniffers

– Record signal strength from the SEs and clients

– Feed this information to the LEE

AP: SE: Sniffer: LEE:

• LEE

– (Re-)models the “radio map” for a sniffer in response to signal strength readings of SEs at sniffers

– Uses models to locate clients.

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A Profiling-based Probabilistic Technique

• Data Collection – Collect many measurements at each location on the grid

• Model building:– Histogram of signal strengths at each location (i.e. joint

probability distributions)

• On-line Estimation– Select the location that maximizes the probability

P(location|measured signal vector)

• Reported median error ~1m

Further details of this technique may be found in Youssef et al., PerCom 2003

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A Probabilistic Technique without Profiling

• Based on hierarchical Bayesian networks• Simultaneously estimate the location of a number of

terminals• The signal strength model is a hyperparameter of the

Bayesian model• Assume reasonable prior distributions and compute the

posterior density given the measurements• Use the computed posterior density to estimate the

quantities of interest• Currently uses Markov Chain Monte Carlo techniques• Median error ~5m

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Bayesian Networks

Non-hierarchical

Hierarchical

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Outline

• Introduction






• Conclusion

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Median Error in Estimation

Method Median Error in Estimation

RADAR - Profiling ~3m

RADAR - Propagation ~4.3m

LEASE ~5m

Probabilistic - Profiling ~1m

Probabilistic – No Profiling ~5m

Elnahrawy et al. observed a localization error of 10 ft (median) and 30 ft 97 (percentile) over a range of algorithms, approaches and environments (SECON 2004)

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Estimation Accuracy

The median error values are widely variable. This raises the following questions:

• Why are they different?

• How do we compare these values?

• Is some kind of normalization possible? If so, how?

• Are there fundamental limits to location accuracy with this technique?

• What is the dependency on factors such as distance between APs?

A preliminary analytical attempt to address these questions appeared in A.S. Krishnakumar and P. Krishnan, Infocom 2005.

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Theoretical Analysis of Accuracy

x

Y

S1

S2

S3Physical Space

Signal Space

Location Uncertainty

(x0,y0)

S0T -

1

T -1 (S 0

) =

(x 0,y 0

)

Probability mass

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Estimation Accuracy

The analysis shows that the minimum value of location uncertainty depends upon:

• Desired probability α

• Signal variance • Propagation constant

• Number of APs

• Distance between APs

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Outline

• Introduction






• Conclusion

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Conclusion

• Indoor location determination presents challenges due multipath propagation and other factors

• We can still estimate location accurately enough for many applications

• Site engineering affects location accuracy

• The same technique has been applied to Bluetooth networks with comparable results

• About the only factor affecting location uncertainty that is in control of the algorithm designer appears to be the signal variance

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Open issues and research topics

• Security considerations

• Zero-profiling techniques

• Deployment for coverage vs. location estimation

• Techniques for model adaptation

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Bibliography - 1• [Bahl Infocom 2000] P. Bahl, V.N.Padmanabhan, “RADAR: An In-Building RF-

based User Location and Tracking System,” Proceedings of IEEE Infocom 2000, Tel Aviv, Israel, March 2000.

• [Youssef PerCom 2003] Moustafa Youssef, Ashok Agrawala, A. Udaya Shankar, “WLAN Location Determination via Clustering and Probability Distributions,” IEEE International Conference on Pervasive Computing and Communications (PerCom) 2003, Fort Worth, Texas, March 23-26, 2003.

• [Krishnan Infocom 2004] P. Krishnan, A. S. Krishnakumar, Wen-Hua Ju, Colin Mallows, Sachin Ganu, “A System for LEASE: Location Estimation Assisted by Stationary Emitters for Indoor RF Wireless Networks,” Proceedings of IEEE Infocom 2004, Hong Kong.

• [Krishnakumar Infocom 2005] A.S. Krishnakumar and P. Krishnan, “On the Accuracy of Signal Strength-based Location Estimation Techniques,” to appear in the Proceeding of IEEE Infocom 2005, Miami, Florida, March 2005.

• [Madigan Infocom 2005] David Madigan, Eiman Elnahrawy, Richard P. Martin, Wen-Hua Ju, P.Krishnan, and A.S. Krishnakumar, “Bayesian Indoor Positioning Systems,” to appear in the Proceedings of IEEE Infocom 2005, Miami, Florida, March 2005.

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Bibliography - 2• [Robotics] Andrew M. Ladd, Kostas E. Bekris, Algis Rudys, Lydia E. Kavraki,

Dan S. Wallach, and Guillaume Marceau, “Robotics-based location sensing using wireless ethernet,” In Proceedings of the eighth Annual International Conference on Mobile Computing and Networking (MOBICOM-02), pages 227–238, New York, September 23–28 2002. ACM Press.

• [Rappaport] T. S. Rapport, “Wireless Communications – Principles and Practice,” IEEE Press, 1996.

• P. Bahl, V.N. Padmanabhan, and A. Balachandran, “Enhancements to the RADAR user location and tracking system,” Technical report, Microsoft Research Technical Report, February 2000.

• Prasithsangaree, P. Krishnamurthy, and P.K. Chrysanthis, “On indoor position location with wireless LANs,” In The 13th IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC 2002), 2002.

• N.B. Priyantha, A. Chakraborty, and H. Balakrishnan, “The cricket location support system,” In Proceedings of the Sixth Annual ACM International Conference on Mobile Computing and Networking, 2000, pages 51–56, 2003.

• M. Youssef and A.K. Agrawala, “Handling samples correlation in the HORUS system,” In IEEE Infocom, 2004.

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Bibliography - 3

• [Krishnakumar CollaborateCom 2005] A.S. Krishnakumar and P. Krishnan, “The Theory and Practice of Signal Strength-Based Location Estimation,” The first international conference on collaborative computing, San Jose, California, December 2005.

• T. Roos, P. Myllymaki, and H. Tirri, “A statistical modeling approach to location estimation,” IEEE Transactions on Mobile Computing, 1:59–69, 2002.

• S. Saha, K. Chaudhuri, D. Sanghi, and P. Bhagwat, “Location determination of a mobile device using IEEE 802.11 access point signals,” In IEEE Wireless Communications and Networking Conference (WCNC), 2003.

• A. Smailagic, D.P. Siewiorek, J. Anhalt, D. Kogan, and Y. Wang, “Location sensing and privacy in a context aware computing environment,” Pervasive Computing 2001, 2001.

Documents

Signal Strength-Based Localization in Indoor Wireless Networks