Copyright© 2002 Avaya Inc. All rights reserved Avaya – Proprietary Use pursuant to Company instructions Copyright© 2003 Avaya Inc. All rights reserved Avaya – Proprietary Use pursuant to Company instructions Signal Strength-Based Localization in Indoor Wireless Networks A.S. Krishnakumar Avaya Labs 5 April 2006

2 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

3 ask/16-Dec-15 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?

4 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

5 ask/16-Dec-15 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

6 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

7 ask/16-Dec-15 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)

8 ask/16-Dec-15 Angle of ArrivalTime of Arrival TDOA Received Signal Strength

9 ask/16-Dec-15 Is this new? It has all been done before! Radar LORAN GPS etc. So what is new ? A lot!

10 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

11 ask/16-Dec-15 Location in 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

12 ask/16-Dec-15 Location in Networks Attention has been focused on RSSI-based techniques since they: Can be implemented with currently available hardware Are reasonably accurate

13 ask/16-Dec-15 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

14 ask/16-Dec-15 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

15 ask/16-Dec-15 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.

16 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

17 ask/16-Dec-15 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

18 ask/16-Dec-15 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-line

19 ask/16-Dec-15 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:

20 ask/16-Dec-15 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:

21 ask/16-Dec-15 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)

22 ask/16-Dec-15 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.

23 ask/16-Dec-15 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

24 ask/16-Dec-15 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

25 ask/16-Dec-15 Bayesian Networks Non-hierarchical Hierarchical

26 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

27 ask/16-Dec-15 Median Error in Estimation MethodMedian 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)

28 ask/16-Dec-15 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.

29 ask/16-Dec-15 Theoretical Analysis of Accuracy x Y S1S1 S2S2 S3S3 Physical Space Signal Space Location Uncertainty (x 0,y 0 ) S0S0 T -1 T -1 (S 0 ) = (x 0,y 0 ) Probability mass 

30 ask/16-Dec-15 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

31 ask/16-Dec-15 Outline Introduction Applications of Location Information Location determination in Radio Networks Problem definition for Networks Current research and examples Limits of location determination Conclusion

32 ask/16-Dec-15 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

33 ask/16-Dec-15 Open issues and research topics Security considerations Zero-profiling techniques Deployment for coverage vs. location estimation Techniques for model adaptation

34 ask/16-Dec-15 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 [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, [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 [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.

35 ask/16-Dec-15 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– ACM Press. [Rappaport] T. S. Rapport, “Wireless Communications – Principles and Practice,” IEEE Press, P. Bahl, V.N. Padmanabhan, and A. Balachandran, “Enhancements to the RADAR user location and tracking system,” Technical report, Microsoft Research Technical Report, February 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), 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, M. Youssef and A.K. Agrawala, “Handling samples correlation in the HORUS system,” In IEEE Infocom, 2004.

36 ask/16-Dec-15 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 T. Roos, P. Myllymaki, and H. Tirri, “A statistical modeling approach to location estimation,” IEEE Transactions on Mobile Computing, 1:59–69, S. Saha, K. Chaudhuri, D. Sanghi, and P. Bhagwat, “Location determination of a mobile device using IEEE access point signals,” In IEEE Wireless Communications and Networking Conference (WCNC), 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.