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CIS 632 / EEC 687Mobile Computing

Indoor Localization

Chansu Yu

Next-Generation Services

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Spotlight Mobile, Founded’02, from Cornell

Uses GPS Pseudo-lites

ShopKick - A $100-device at a store sends a special-pitch sound so that the Smartphone app can pick it up to offer discounts, etc.

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• Exquisite combination and integration of methods, tools, and services Incorporated a variety of COEX services with the indoor

navigation system in myCoex app Support turn-key based proposal for any sites similar to

COEX

WiFi fingerprint

capturing tool

Indoor routing path

drawing and mapping tool

AP environment

analysis tool

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Integrated First Responder Location and Physiological Monitoring

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Indoor Localization Techniques

Active Badge (Infrared)

Active Bat, Cricket (Ultrasonic)

UbiSense (UWB)

Smart Floor (Pressure Sensor)

Spot-On (RFID)

RADAR (Wi-Fi)

Wifi-based Indoor Localization or Positioning System (WPS)

Characteristics of Wi-Fi Wi-Fi signal reaches 35m ~ 95m

Each AP has globally unique ID (BSSID)

RSS (Received Signal Strength) in dBm

SSID: XUnivBSSID:00:0e:83:39:a9:80

RSS: -40 (dBm)

SSID: XunivBSSID:00:0e:83:39:a9:80

RSS: -90 (dBm)

No Wi-Fi Signals

in indoors (32m), in outdoors (95m)

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WPS

Triangulation-Based

Fingerprint-Based

Triangulation-Based WPS

Beacon Database War-Driving/War-Walking

Distance from AP Radio Propagation Model

Location Determination Least Squares Estimation

Davidon Algorithm

AP1

AP2

AP3

d1

d2

d3

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Fingerprint-Based WPS

Building Radiomap (offline phase) Empirical

Generation

Location Determination (online phase) Nearest Neighbor

SVM

Bayesian

Neural Network

Fingerprint-Based WPS –Offline Phase

* Effective learning data creationSampling-based Signal Propagation ModelInterpolation Model

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Fingerprint-Based WPS –Online Phase

f1 = {AP1=-44, AP2=-53… APn =-90}

f2 = {AP1=-44, AP2=-58… APn =-93}

……

fk = {AP1=-89, AP2=-97… APn =-52}

Database

Estimation location is the fingerprint which has the

minimum distance among fingerprints in the radiomap

Time complexity = O(|fingerprints| x |APs|)

* Improving accuracyNearest Neighbor Method (kNN)Naïve Bayesian Method Histogram Method

Measured FP fo

kNN (k=3) : obtain the 3 nearest FPs (fx, fy, and fz) => estimated location = average ofloc(fx)+ loc(fy) + loc(fz)

Fingerprint-Based WPS –Clustering (1)

Computational Cost Reducing Methods Reducing search space

Clustering

Decision Tree

Reducing comparison cost AP Select

AP Clustering

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Fingerprint-Based WPS –Clustering (2)

Fingerprint clustering using the strongest AP(s)

Cluster C1 = set of FPs, the strongest AP of which is AP1Cluster C2 = set of FPs, the strongest AP of which is AP2…

If AP4 is the strongest AP in the measured fo, search C4.

Fingerprint-Based WPS –Clustering (3)

Fingerprint clustering using the strongest AP(s)

Cluster C1 = set of FPs, the strongest AP of which is AP1Cluster C2 = set of FPs, the strongest AP of which is AP2…

If AP5 & AP13 are the strongest APs in the measured fo, search C5 U C13.

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WiFi indoor localization

High accuracy indoor localization

WiFi enabled smartphone indoor localization

RADAR [INFOCOM’00], Horus [MobiSys’05], Chen et.al[P

ercom’08]

Cricket [Mobicom’00], WALRUS [Mobisys’05], DOLPHIN [Ubicomp’04], Gayathri et.al [SECON’09]

SurroundSense [MobiCom’09], Escort [MobiCom’10], WILL[INF

OCOM’12], Virtual Compass [Pervasive’10]

Academic Research

RADAR [INFOCOM’00]: P. Bahl and V. N. Padmanabhan. RADAR: An In-building RF-based User Location and Tracking System. INFOCOM’00. Cricket [Mobicom’00]: N. Priyantha, A. Chakraborty, and H. Balakrishnan. The Cricket Location-support System. MobiCom’00. DOLPHIN [Ubicomp’04]: M. Minami, Y. Fukuju, K. Hirasawa, and S. Yokoyama. DOLPHIN: A Practical Approach for Implementing A Tully Distributed Indoor Ultrasonic Positioning System. Ubicomp’04.WALRUS [Mobisys’05]: G. Borriello, A. Liu, T. Offer, C. Palistrant, and R. Sharp. WALRUS: Wireless Acoustic Location with Room-level Resolution Using Utrasound. MobiSys’05. Horus [MobiSys’05]: M. Youssef and A. Agrawala. The Horus WLAN Location Determination System. MobiSys’05.Beepbeep [Sensys’07]: C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan. Beepbeep: A High Accuracy Acoustic Ranging System Using Cots Mobile Devices. Sensys’07.Chen et.al [Percom’08]: S. Chen, Y. Chen and W. Trappe. Exploiting Environmental Properties for Wireless Localization and Location Aware Applications. PerCom’08.Gayathri et.al [SECON’09]: G. Chandrasekaran, M. A. Ergin, J. Yang, S. Liu, Y. Chen, Marco Gruteser and Rich Martin. Empirical Evaluation of the Limits on Localization Using Signal Strength. SECON’09.SurroundSense [MobiCom’09]: M. Azizyan, I. Constandache, and R. R. Choudhury. Surroundsense: Mobile Phone Localization via Ambience Fingerprinting. MobiCom’09.Escort [MobiCom’10]: I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury. Did You See Bob? Using Mobile Phones to Locate People. MobiCom’10.Virtual Compass [Pervasive’10]: N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A. Wolman, and M. Corner. Virtual compass: relative positioning to sense mobile social interactions. Pervasive’10.WILL [INFOCOM’12]: C. Wu, Z. Yang, Y. Liu, and W. Xi. WILL: Wireless Indoor Localization Without Site Survey. INFOCOM’12.

Conferences

IEEE/ACM Mobicom

ACM MobiSys

IEEE Infocom

IEEE Percom

People Tracking conference

The Location Business Summit USA, 2012, San Jose

3rd International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2013, Portugal

The Third International Conference on Mobile Services, Resources, and Users (MOBILITY 2013)

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Large-scale Venue

High localization errors

Generate huge amount of unnecessary traffic

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Environment- COEX, Seoul- 505 x 237m

APs- 1,734 APs

Data set- Learning Set: 2,028 FPs- Samsung Galaxy S

Test Set- 594 FPs

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1,734 APs

2,028Locations(FPs)

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AP cardinality (The number of APs observed at a location is an order of magnitude larger at Coex compared to HKUST. Clustering may not work well due to the large feature set per FP. A trend line is added for the distribution of Coex.)

Histogram of RSS (Weaker signals are received better at HKUST partly due to less interference and less movements at an academic building of HKUST. The mode is about 10 dB different.)

Problems in Large-Scale Venues

To obtain FP, a mobile station sends a probe request message (broadcast). It waits for probe response message from APs at Channel 1

for MinChannelTime (1msec).

If none, switch to the next channel.

If yes, wait for MaxChannelTime (10msec).

With many APs in the vicinity, Some APs continue to send probe response message even

though the mobile station switches to the next channel (Probe message explosion)

It leads to incomplete FP, leading to localization accuracy

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Slide 25

Problems in Large-Scale Venues (2)AP 1-1 AP 1-2 AP 1-3 AP 1-… AP 1-nSTA

Probe Request on channel X

Probe Response

AP 1-4 AP 1-5 AP 1-6

Probe Response

Probe Response

Probe Response

Probe Response

Probe Response

Probe Response

Probe Response

Probe Response

Probe Response

Probe ResponseProbe Response

Probe Response

Retransmit Probe Response

MaxChannelTime

STA cannot receive

Probe Response

after MaxChannelTime

Unnecessary

Probe Response

Unnecessary

Retransmission

Probe Response

flooding

Retransmit Probe Response

Retransmit Probe Response

Retransmit Probe Response

Retransmit Probe Response

Retransmit Probe Response

Problems in Large-Scale Venues (3)

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(Un)detected APs (It shows the number of (un)detected APs. Some APs are not detected via fingerprinting in both Coex and HKUST. While it is mostly due to temporal collisions and random radio propagation in HKUST, it is due to the limited channel time allowed to get responses in comparison to the number of APs in the proximity in Coex.)

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Remember!It All Depends on RF Propagation!

In free space, receiving power proportional to 1/d² (d = distance between transmitter and receiver)

Suppose transmitted signal is Pt, received signal Pr = h Pt, where h is proportional to 1/d²

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dGG

P

Ptr

t

r

Pr: received power

Pt: transmitted power

Gr, Gt: receiver and transmitter antenna gain

(=c/f): wave length