Evaluating Wi-Fi Location Estimation Technique for Indoor Navigation Roshmi Bhaumik Today’s topic is indoor location estimation in a Wi-Fi network

Outline Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work

Motivation Wireless communications Indoor location aware applications Chaska city wide Wi-Fi available from July 2004 Minneapolis city wide Wi-Fi proposal , 2006 Indoor location aware applications Indoor Navigation aid for visually impaired Navigation through Exhibition Halls and Museums Challenges of indoor Wi-Fi positioning Noisy characteristics of wireless channels Multi-path fading Navigating through public spaces For visually impaired or people unfamiliar to the surrounding Healthcare Tracking patients carrying emergency alarm systems or tracking expensive hospital telemetric devices. Retail Track shopper behavior, provide store/ product location for shoppers and offer promotions based on shopper’s location. Security Access control of wireless devices and detecting device positions Entertainment Location based gaming

Motivation(2) Evaluating Wi-Fi based Indoor Location Estimation techniques for indoor navigation Location aware solution (client) Location based Server Noisy characteristics of wireless channels Bluetooth devices, cordless phones, microwaves etc can cause interference as they all operate in the same band as Wi-Fi devices, namely 2.4 GHz Water and human bodies absorb RF signals at that frequency Multi-path fading Due to multiple reflections, refractions and attenuation paths for signals , a transmitted signal can reach the receiver through different paths and thus have different amplitude and phase. Changes in the environment, such as temperature and humidity can affect the received signal strength Core Common Technology Indoor Positioning Technology System Architecture of Location Based Service

Outline Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work

Problem Definition Given Evaluate Objective Constraints Wi-Fi Localization scheme Evaluate Given localization scheme for indoor navigation system Objective Fulfilling accuracy requirements of navigation application Constraints Using existing Wi-Fi Access Points (APs) Experiment carried out in typical building

Outline Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work

Related Work Requirements of positioning for indoor navigation: Accuracy Integrity- issue alarm in case of large estimation errors Availability (Coverage) Continuity of service (Location Estimation response time) (Swiss Federal Institute of Technology 2003)

Related Work (2) Evaluation criteria: Location labeling System performance Architecture Cost Hightower et al, 2001 [14] Algorithm vs. accuracy Performance vs. accuracy tradeoff Fault-tolerance P. Prasithsangaree et al, 2002 [11]

Outline Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work

Our Contribution Limitations of related work: Did not consider effect of different motion patterns on accuracy Our Contribution: Evaluate the positioning accuracy with different types of movement Other lessons learnt Case Study: Indoor navigation aid for the visually impaired

Outline Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work

Experiment Design Floor Map Stationary Stop and Go Smooth about a point Smooth uniform motion Change in direction Ekahau Software Track controlled movement Calibration Constant parameters: Device Scan Interval (500msec) Accurate Mode Variable parameters: Location Update Interval (500 - 6000msec) Location Estimates Number of visible Access Points (APs) – Significant benefit when going from 1 to 2 or from 2 to 3 APs but little incremental benefit beyond 3 APs. Placement of APs- Placement should be such that it avoids symmetric coverage Calibration density – Higher density provides better accuracy only if sample points show variation in signal intensities Irregular distribution should be avoided Device speed – For devices move very fast, density of calibration should be low for optimal latency/ accuracy ratio Speed above 1m/s should not be continuously used for average error of 2m Device scan interval – Interval of reading RSSI values from the client device For average error of 2m need scan interval smaller than 2500 ms Environmental – Open areas vs corridors Metal bodies around cause radio interference Accuracy Calculations Error measured= Euclidean distance between gold standard and estimated location

Movements for a point 1) Stationary: Gold standard= A 10 Readings taken after 2min 2)Stop and Go Gold standard= B 10 Readings taken within 30 sec 3)Smooth about a point Gold Standard= B Start taking readings at A and end at C A A B Change in wireless infrastructure Addition, removal or displacement of location of APs Change in direction of antenna Change in transmission power of APs Proximity to APs Stability of calibration is better near transmitters Change in radio environment New walls, cubicle changes, large containers, furniture Busy hour with lots of people and non busy hours with empty spaces eg. Malls, shipping/ receiving areas etc. < =3ft <= 3ft A B C Slow motion Quick motion Static

Smooth Motion on Tracking Rail 4) Smooth uniform motion Gold standard= points on the tracking rail Readings taken using the Ekahau Accuracy Tool Error calculation is done by the tool Slow motion Tracking rail

Direction Change 5) Changes in direction a) Turning back 180 degrees on the path Gold Standard = Point B Readings taken at B after turning b) Intersections with ambiguous signals Readings taken at B for all types of movement for a point A B A B Slow motion Tracking rail Turn back

Elliot Hall First Floor

Elliot Hall Third Floor

Ekahau Positioning Software Ekahau Client (on every mobile device) Retrieves signals from visible Access Points through the network cards Ekahau Manager For site calibration, adding logical areas, tracking and accuracy analysis Ekahau Positioning Engine (EPE) Server stores calibration data Calculate location estimates Algorithm: Maximum A Posteriori Estimate using Bayes Rule Applications retrieve positioning data through YAX protocol /Java SDK

Results

Comparing Types of Motion: Floor 1 Actual X,Y Stationary - Error (ft) Smooth slow about point -Error (ft) Averaged X,Y – Error (ft) Stop and go -Error (ft) 703,79 0.78 7.0 6.97 9.79 703,848 6.33 11.82 6.94 11.06 703,969 11.58 18.43 15.55 11.73 703,1268 3.17 10.92 9.36 10.68 702,1452 5.53 5.64 4.50 5.98 457,1419 2.25 3.5 3.22 4.37 780,1485 6.89 6.41 5.34 26.50 Average 5.22 9.10 7.41 11.44

Comparing Types of Motion: Floor 1 Actual Stop go Smooth Avg Stationary

Comparing Types of Motion: Floor 3 Average Accuracy variation with type of motion: (1, 2, 3 &5a) Actual Smooth -5.8 Stationary -5.5 Stop n Go- 10.2 Turn around – 6.8 9.7,9.1,10,9.2 7.2,7.2,17.5,7.7 8.5,6.5,21.2 2.3,2.3,5.6 0.7,3.3,0.7,4.1 6.5,4.8,6.2,6.2

Smooth uniform motion: Floor 1 Use Ekahau Accuracy Tool Smooth uniform motion in a straight line along the tracking rail Error vector shown in red color Discuss the effect of inaccuracy at the intersection with ambiguous signal pattern with added animation Floor Min Max Average 90% Med Elliot-1st units in ft 0.4 26.2 8.8 16.8 7.6

Smooth uniform motion: Floor 3 Readings taken using Ekahau Accuracy Tool Discuss the effect of turning back 180 here by adding animation Floor Min Max Average 90% Med Elliot -3rd units in ft 0.8 16.8 7.2 12.4 8.1

Ambiguous Intersection All types of movement for a point are considered Average Error worse than 12 ft for this specific location Discuss the effect of inaccuracy at the intersection with ambiguous signal pattern with added animation Actual X,Y Stationary - Error (ft) Smooth slow about point -Error (ft) Averaged X,Y – Error (ft) Stop and go -Error (ft) 703,969 11.58 18.43 15.55 11.73

Analysis Accuracy requirements are met for the listed types of movements except : Stop and go Direction change at ambiguous intersection System does not respond to quick changes and error increases is due to overestimation Ambiguous signal patterns cause large errors

Other lessons learned Calibration Stability: Stable over 4 months Transferability: Calibration done with one client, used to track any other supported clients with same accuracy Tested with Cisco, Orinoco and Dell’s built-in WLAN cards and Toshiba PDA Calibration is transferable as EPE performs normalization of RSSI* values from different network cards and devices *Radio Signal Strength Indicator

Other lessons learned Tracking on multiple floors Adjacent floor maps linked at certain positions (e.g. start of staircase) Latency (5-15 sec) depends on device speed. Connection points between floors are not specified Correct floor detected with greater latency (2 min) Floors are significant barriers to signal propagation Points in adjacent floors have different signal patterns

Other Lessons learned Accurate mode meets accuracy requirement and latency is 5 sec Variation of LUI* does not completely control the effect of history At low RSSI, changes in signal strength with distance is very small. This causes signal aliasing and reduces accuracy *Location Update Interval

Outline Motivation Problem Definition Related work Our Contribution Experiment Case Study Conclusion and Future work

Case Study: Indoor Navigation Aid for the Visually Impaired We used EPE to build an Indoor Guidance System (IGS) meant to help the visually impaired to find their way inside buildings BUILDING DATABASE Spatial data: X,Y Attribute data: Physical & Logical spaces CLIENT Location Aware Application Audio/ Visual Output Wi-Fi Sensors USER LOCATION SERVER (EPE) Positioning Model: floor maps and calibration data Information Flow Diagram

Existing Work Building Database Data Entry interface: building and floor data Audio/Visual output : a list of surrounding features, orientation and distance from current user position User input needed to get current user position

My Additions Infer current user location in building database coordinates Multiple floor tracking and transferable calibration Improve accuracy and latency of location estimate Background thread to collect location & error estimates Average location estimates over specified time intervals Add location buoys spaced 15 ft Reduce computation Describe the surrounding with finer granularity

IGS screen shot

Application Design Guidelines During calibration, sample points should be rejected if RSSI is below certain threshold Averaging location estimates over optimum time interval is better than getting a single instantaneous estimate Accurate mode gives better location estimates for normal walking speeds Accuracy will vary depending on the type of movement The signal strength variability at a fixed position causes a location error that cannot be eliminated if a single measure is executed.

Conclusions and Future Work EPE location estimation scheme performs well for an indoor navigation application but some areas can be improved: Calibration stability and transferability is good Tracking over multiple floors works well Accuracy is good for smooth movement in a straight line and stationary state Accuracy is poor when signal strengths are low Differences from LOCADIO: LOCADIO – transitional probabilities are functions of 1. Shortest distance path 2. Human walking speed distribution 3. Still/ Moving inference. The last two are combined to obtain probability of a given speed given moving or still. The probability of a given speed is combined with the probability of transitioning to a node based on shortest distance path. My approach- Pre-calculate the transitional probability matrix based on moving and still ( ie 2 matrices). Based on moving or still inference Switch between the two matrices. The “moving” matrix may be independently updated based on speed inference -> using another HMM. Example in tracking people, one HMM could be assigned to (x,y) and another to speed. The results of the speed inference can be used to update transitional probabilities for (x,y).

Conclusions and Future work(2) Accuracy is poor if the path of movement has a number of intersections, specially with ambiguous signal pattern Using directional APs for asymmetric coverage Accuracy is poor for stop and go motion Using some method to detect still and moving state Use above to choose the transition probabilities Related work –LOCADIO[7] Further tuning of the application can achieve better accuracy for indoor navigation Future work: Implement own location detection scheme. Incorporate suggested improvements

Keywords Wi-Fi : Wireless fidelity, IEEE802.11b APs: Access Points, Within the range (~50ft) of an AP, the wireless end-user has a full network connection with the benefit of mobility. RSSI : Received Signal Strength Indicator Bayesian networks :A probabilistic graphical model . The nodes represent variables and directed arcs represent conditional dependencies between variables. Stochastic model :A mathematical model which contains random (stochastic) components or inputs; consequently, for any specified input scenario, the corresponding model output variables are known only in terms of probability distributions in contrast to a deterministic model Machine learning :A method for creating computer programs by the analysis of data sets. IGS: Indoor Guidance system, a navigation aid developed for the Low Vision Lab, Psychology department, UMN

References Papers: 1. P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” Proceedings of IEEE Infocom 2000, March 2000, pp. 775–784 2. P. Myllymaki, T. Roos, H. Tirri, P. Misikangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” Proceedings of the 3rd IEEE Workshop on Wireless LANs, September 2001, pp. 59–69. 3. R. Battiti, A. Villani, and T. Le Nhat, “Neural network models for intelligent networks: deriving the location from signal patterns,” in Proceedings of AINS2002, (UCLA), May 2002. 4. Castro, P., et al. A Probabilistic Room Location Service for Wireless Networked Environments. in Ubicomp 2001. 5. Ladd, A.M., et al. Robotics-Based Location Sensing using Wireless Ethernet. in Eighth International Conference on Mobile Computing and Networking. 2002. 6. John Krumm, “Probabilistic Inferencing for Location”, Proceedings of the 2003 Workshop on Location-Aware Computing, October 2003. 7. John Krumm and Eric Horvitz, "LOCADIO: Inferring Motion and Location from Wi-Fi Signal Strengths", First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (Mobiquitous 2004), August 2004 8. D. Fox, J. Hightower, H. Kautz, L. Liao, and D. Patterson. "Bayesian techniques for location estimation" in Proceedings of The 2003 Workshop on Location-Aware Computing, October2003. 9. L. R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. of the IEEE, Vol.77, No.2 pp.257--286, 1989. 10. J.A.Tauber.Indoor Location Systems for PervasiveComputing http://theory.lcs.mit.edu/~josh/papers/location.pdf 11. P. Prasithsangaree, P. Krishnamurthy, and P. K. Chrysanthis, "On Indoor Position Location With Wireless LANs ," The 13th IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC 2002), Lisbon, Portugal, September 2002. 12. http://www.dinf.ne.jp/doc/english/Us_Eu/conf/csun_98/csun98_008.htm 13. http://topo.epfl.ch/publications/paper_IAIN03_epfl.pdf 14. Jeffrey Hightower and Gaetano Borriello.Location systems for ubiquitous computing.IEEE Computer,August2001. Slides: P1.”Graphical Models on Manhattan: A probabilistic approach to mobile device positioning” presented by Petri Myllymäki and Henry Tirri P2. faculty.cs.tamu.edu/dzsong/teaching/ fall2004/netbot/Yutu_Liu_Robot.ppt

Commercial Solutions Ekahau Aeroscout WhereNet Newbury 3.5 ft 3.1 ft Avg. Accuracy claims 3.5 ft 3.1 ft 10ft Tracking Range WLAN covered site Latency claims Low(?) - < 5 sec Software API Open API : SDK (Java) or TCP protocol Open API for 3rd party applications Available to partners for building applications Vendors need to integrate it in their applications Client software yes none Proprietary Hardware none( any Wi-Fi enabled client) Summary Software based solution using Wi-Fi technology for location estimation Hardware and software based solution using Wi-Fi and Active RFID technology Hardware & software based solution based on Wi-Fi technology for WLAN security , access control etc. There are a lot of such new solutions. These are the more popular solutions in the market. These are all applicable to indoor positioning using Wi-Fi technologies. The comparison parameters are based on requirements for the navigation application.

Wi-Fi Positioning Theory Static Location Estimation with Wi-Fi Nearest Neighbor [1] Minimum Euclidean distance in signal space between real time and offline entries (x,y,z,ssi (i=1..N)) weighted average of k nearest neighbors Average accuracy ~ 10 ft Neural Networks [3] Dependencies between signal and location are not easily modeled by an Multi-Layer Perception neural network Bayesian Approach – Maximum A Posteriori (MAP) Estimate [2,4,5] The probabilistic model assigns a probability for each possible location l, given the observation o. Nearest Neighbor (RADAR[1]) database with entries (x,y,z,ssi (i=1..N)) recorded in an offline-phase weighted average of k nearest neighbors in signal space to estimate location N= number of visible APs Neural Network The fact that a basic method like the k -nearest-neighbors achieves similar results is an indication that the complex dependencies between signals and positions are not easily modeled by an MLP neural network. Kalman filter: all the conditional distributions of the HMM model are continuous linear-Gaussian HMM: In discrete case, distributions are represented as sparse matrices Particle Filter: Approximate inference is obtained using particle filtering technique Focuses computation on areas where most probability mass lies.

Wi-Fi Positioning Theory (2) Tracking motion (recursive estimate) Kalman Filter [6,8] linear relation between measurement and state vector Gaussian noise Extended Kalman Filter [6,8] nonlinearity dealt by updating linearization not suitable for large disturbances Hidden Markov Model (HMM) [6,9] all states must be explicitly represented separate HMM for each subset of state variable apply Viterbi Algorithm to find most probable path Particle Filter [6,8] current state => N weighted state samples samples updated using SIR after each new measurement focus on state space regions with high probability Markov assumption- current state depends only on immediate previous state

Ekahau: Theory Location estimation is posed here as a machine learning problem Calibration data is used to build a model to predict location given real-time signal measurements using probabilistic methods Tracking uses a Hidden Markov model (HMM) where the locations lt are the hidden unobserved states. We compute maximum probability path l1…ln , given a sequence (history) of observations o1, …on using the Viterbi algorithm During calibration signal strength measurements are collected at sample points at known locations Radio signal strength measurements are performed by the mobile client device and sent to the a server The server estimates the client location based on real-time measurements from the client at that location and the calibration data, using probabilistic methods Tracking with history improves accuracy as signal aliasing is eliminated by using position history

Theory (2) p(o|l): likelihood function/conditional probability of obtaining observations o at location l. (from calibration) p(l): prior probability of location l (can add user profiles, tracking rails etc. to improve accuracy) p(o): normalizing constant Using a loss function and posterior distribution, p(l|o), we can get a optimal estimator t for the location variable. Average accuracy obtained varies between 3ft to 10ft depending on actual implementation likelihood function / conditional probability of obtaining observations o at location l. This is determined at calibration phase by taking empirical observations at known location

Hidden Markov Model HMM: λ = <N,M,{Pi},{aij},{bi(k)}> N the number of states (S1 S2 .. SN) M the number of possible observations O = O1 O2 .. OT is the sequence of observations Q = q1 q2 .. qT is the notation for a path of states {P1, P2, .. PN} The initial probabilities P(q0 = Si) = Pi The state transition probabilities P(qt+1=Sj | qt=Si)=aij The observation / emission probabilities P(Ot=k | qt=Si)=bi(k)

Viterbi Algorithm Find most probable path given a sequence of observations O1, …On i.e. argmax P(Q| O1 O2… OT) ? By induction: The most probable path to Sj has Si* as its penultimate state where i*=argmax dt(i) aij bj (Ot+1) i = The Probability of the path of Length t with the maximum chance of doing all these things: …OCCURING and …ENDING UP IN STATE Si …PRODUCING OUTPUT O1…Ot By Induction: The most prob path with last two states Si Sj is the most prob path to Si , followed by transition Si → Sj The most probable path to Sj has Si* as its penultimate state where i*=argmax δt(i) aij bj (Ot+1) i Dynamic Programming is used here Here location is the hidden state and signal strengths from APs are the observed states