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Wireless Location Technologies

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1 Wireless Location Technologies
Nobuo Kawaguchi Graduate School of Eng. Nagoya University

2 About me Nobuo Kawaguchi Research Topics Associate Professor
Dept. Engineering, Nagoya University Research Topics Wireless Location Systems ( ) Scalable Adaptive Multicast (SAM), XCAST6 UbiCompEnvironment and Communication Middleware named “cogma” Mobile agent based system

3 Today’s Topic Wireless Location Technologies WiFi Location Database
What is WiFi based Positioning History Characteristics WiFi Location Database Wireless Location Information Systems PlaceLab Skyhook Wireless Loki PlaceEngine Apple iPhone Location Enhanced Services

4 Wireless Location Technologies
Tech. for both Indoor / Outdoor is required Outdoors GPS Cell Phone PHS Wireless LAN Environment RFID Ultra Sonic UWB Indoors 1m 10m 100m Estimation Accuracy

5 WiFi Everywhere Wireless LAN has become popular infrastructure
Restaurant Airport Wide spread of wireless LAN Home Company Station University WiFi location RSSI This slide shows a background of our research. In recent years, wireless LAN has become popular infrastructure. In the old time, we could receive the signal of wireless LAN only in the university and the company. [Click] However, now, we can receive the signal of wireless LAN in a lot of places. As a result, a large number of position estimation system using wireless LAN have been developed. [Click] This figure shows, this positioning system needs one or more WiFi locations to estimation. [Click] WiFi location is set of BSSID, position of access point and RSSI. BSSID, Latitude, Longitude Wireless LAN has become popular infrastructure A large number of position estimation system using wireless LAN have been developed

6 WiFi Location Technology
Every WiFi Access Point(AP) has followings ESSID (String) BSSID 6 byte vendor assigned unique address (MAC) Construct a database with BSSID and Position One can estimate the position just receive a WiFi BSSID. A lot of WiFi APs are already exist in the Wild. One can easily add new APs by oneself. Can increase accuracy by adding APs. Each AP do not requires network access for positioning. Important Points

7 History of WLAN positioning
Start around 2000 (MS RADER) now Technology 2000:RADAR (Microsoft) Products 2003:AirLocation (Hitachi) 2000:Ekahau Service/Activities 2006/3:Loki (Skyhook Wireless) 2003:PlaceLab (Intel) 2005/ (Nagoya Univ) 2008/1:iPhone (Apple) 2006/7:PlaceEngine (Sony CSL) 2007/9:Digial Camera (Sony)

8 Positioning methods using WLAN
Existing methods are classified into following three types. Triangulation Lateration (RSSI or TDOA) RADAR (MS Research), WiPS (Kyusyu Univ. Japan) AirLocation (TDOA)(Hitachi) Angulation (AOA) Proximity GUIDE Project (Keith et al) Scene Analysis RADAR(MS Research), Ekahau (Ekahau corp.) Place Lab (Intel Research)

9 Positioning Access Point
Triangulation (Angulation) (Lateration) Using more than 2 reference points direction Using more than 3 reference point’s distance A B E α β A B C a b c E Reference Point Estimated Point Reference Direction

10 Positioning Access Point
Proximity Consider the position of most powerful AP as a current position. Transmission range of Wireless LAN is about 100 meters in the open air Not good accuracy but simple. Scene Analysis Consider Difficult to construct learning data as target area is spread Inappropriate for our objective

11 Proximity This method consider communication area of AP as user’s position. Terminal’s location APn(xn,yn) AP1(x1,y1) AP2(x2,y2) Reference Point Communication Area of Reference Point

12 Scene Analysis This method use pre-observation wireless information called radio map. Most system use Monte Carlo family method (ex. Bayesian filter, particle filter) and radio map. Lecture Room A Lecture Room B Lecture Room C Lecture Room D Lecture Room E

13 GPS vs WiFi Location From , Delivering Real-World Ubiquitous Location Systems, C. ACM 2006.

14 WiFi Location: Characteristics
WiFi characteristics Difference of WLAN Adapter/Antennas Difference on Orientation WiFi Signal Strength Distribtion WiFi Positioning Acitivities Project WiFi AP positioning

15 Difference on WLAN Adapter

16 Distribution Difference

17 Difference on Orientation
Wireless LAN Card A Wireless LAN Card B Wireless LAN Card C 315º 45º 270º 90º 225º 135º 180º

18 Distribution Pattern of WiFi RSSI
Received Signal Strength (dBm) Probability Density

19 WiFi Location Database
Required for wide area location system. Acquisition Method Accuracy Efficiency How to construct a large Database

20 Acquisition Methods Variety of acquisition methods with different transportation Car 30km/h Walking 5km/h Bicycle 15km/h This slide shows the variety of acquisition methods with different transportation. In this research, we focused transportation for acquisition. We considered 4 acquisition methods by walking, bicycle, motorcycle, and car. Left side is low speed. It's not good to collect wide areas but movement is free. Right side is high speed. But it is influenced by traffic regulations. Near to the buildings and houses Influenced by traffic jams and regulations

21 Cumulative estimation accuracy of evaluative data by walking
Cumulative rate Accuracy(m) This slide shows the cumulative estimation accuracy of evaluative data by walking. Horizontal axis shows the accuracy of estimation system. Vertical axis shows cumulative rate. This figure shows, the position estimation using the learning data acquired by foot can make estimates at less than 30 m in 68% of the area. The estimation data by car is worse by about 5% in all ranges. Comparing the data by bicycle and by walking, there is about a 5% difference in the range of 10 m, but beyond 40 m, the bicycle data can estimate almost as well as that obtained by walking. Cumulative estimation accuracy of evaluative data by walking The position estimation using the learning data acquired by walking can make estimates at less than 30m in 63% of the area The estimation data by car is worse by about 5% in all ranges Beyond 40 m, the bicycle data can estimate almost as well as that obtained by walking

22 Accuracy of WiFi location
Bicycle Motor Bike Car

23 Result: Bicycle is more suitable than Others
Table: Position Estimation of Experimental data (Walking, Proximity) Walking (5km/h) Bicycle (15km/h) Car (30km/h) All data No. of APs 247 269 183 Accuracy (m) 24.3 26.7 29.6 Coverage (%) 86.8 87.8 83.3 First-round data 214 209 122 31.3 23.2 26.0 72.2 74.6 68.2 Strong signal APs (1st round) (RSSI > -90) 155 178 49 30.1 23.5 32.9 68.1 73.2 53.8 Now we can select a pre-acquisition method. This table shows the result of this experiment. “All data” is all collected WiFi locations. “First-round data” is collected WiFi locations in the first round of experiment. “Strong signal APs” is that data’s RSSI is stronger than -90 of the “First-round data”. If we simply require accuracy, “walking” may be the answer. However, we need also coverage and efficiency in the acquisition. If we only think about speed, “car” may be the answer. But the accuracy and coverage of the car data are always the worst. Additionally, the cost of the transportation and lack of freedom are not acceptable for our project. As a result, we selected pre-acquisition method using the bicycle. Bicycle has better efficiency than others

24 WarDriving using bycicle
GPS Note PC WiFi Antenna / Card Battery Stumbler

25 Project

26 Project WiFi Location Portal for Japan Begins July 2005
Data Collection of WiFi AP(BSSID) + Location Place Information Begins July 2005 Currently, we have collected 369,045 APs currently

27 Environment full of wireless LAN Environment full of wireless LAN Users Effect on … Service area Estimation accuracy Information Service WiFi Location Database WiFi Location WiFi Location WiFi Location WiFi Location Goals of Wide-area WiFi Location acquisition by collaboration with user Creation of position estimation system using WiFi Location database Producing Low-Cost positioning system for everywhere everybody Activity Research on positioning system Public relations of positioning system Creation of acquisition assistance tools is our location project. We are working on the to collect WiFi Location with user. Our goal is divided into three steps. First, we collect WiFi Locations in wide-area by collaboration with user. Next, we create positioning system using WiFi Location database. Finally, we produce low-cost positioning system for everywhere everybody. Figure shows collaboration between and user. [Click] User can get and use information services and applications from with no cost. [Click] If user discovered new access point, [Click] user can register in database. [Click] Registered WiFi location comes to be used to estimate next time. [Click] If users register a lot of WiFi locations, [Click] database grows more and more, [Click] and quality of service improves, too. In this way, grows up with the user. Our main activity is research and public relations of positioning system and create acquisition assistance tools.

28 Collecting AP’s by Collaborators
300 over members collecting AP Mainly for Tokyo/Osaka/Nagoya - Ranking

29 Trend of No. of AP in
Over 530,000 WiFi locations in Japan No. of collaborator No. of AP This slide shows the current status of Horizontal axis shows the time to the month. The left side vertical axis shows the number of collected WiFi locations. The right side vertical axis shows the number of collaborator. The blue line shows number of WiFi locations. The red line shows number of collaborator. collected about 200,000 WiFi locations in September 2006. Now, over WiFi locations are collected in Japan. Month

30 Tokyo area ( over AP)

31 Tokyo APs plotted on GoogleEarth

32 View from Tokyo-Castle

33  Nagoya Area , Over 40000APs

34 Access Points in Nagoya City Area
1km Square 878 APs Center of Nagoya JAPAN 1km Access Point Total 878 8.8 unit / 100m×100m

35 APs in Residential Area
Access Point Total 278 3.5 unit /100m×100m Residential area Height is restricted 278 APs in 1km square area 1km Residential Area

36 upload page

37 Tools for Locky Stumbler KML converter
Log→ KML (Google Earth)

38 Wireless Location Lib for WLDB
Locky Toolkit By using Locky Toolkit, one can easily develop a WiFi Location Application Just a few line of Java code. // Creation of LockyToolkit object LockyToolkit lockyToolkit = new LockyToolkit(); // Load a WiFi Location DB lockyToolkit.openDB(); // Get Locky Code from current wireless measurement LockyCode lockyCode = lockyToolkit.getLockyCode(); // Get latitide, Longitude double latitude = lockyCode.getLatitude(); double longitude = lockyCode.getLongitude(); Locky Toolkit example program (Java)

39 PlaceLab (2003~ Intel Research)
Bootstrapping Location-enhanced Computing Enabling privacy-observant, wide scale, indoor & outdoor device positioning with low barriers to participation Research Agenda Previous research: small communities, high cost systems Our goal: enable large communities by reducing barriers to adoption Provide low-cost, highly convenient position-sensing technology Make users comfortable with respect to their location privacy Develop services and toolkits to make it easy to build location-aware applications Usage Model Client devices cache snapshots of WiFi Beacon Databases Applications use location to provide customized, dynamic content and services Devices that hear WiFi beacons estimate location locally & privately. New beacons get added to user-contributed database Urban areas have dense WiFi coverage PlaceLab Approach Rely on increasing WiFi densities to provide low-cost device positioning Build a public user-contributed data store to map RF beacons to geographic locations Improve quality of positioning data via Bayesian filter and sensor fusion techniques Understand privacy management tradeoffs by studying use of location-aware applications Provide a “PlaceLab” for educators with toolkits and curriculum for web and ubicomp courses Offer developers a mechanism to use “place” abstractions in addition to low-level coordinates (Reference )

40 Skyhook wireless Loki 200 employee 8million APs
Loki Toolbar By using the Loki toolbar, one can locate the place of the terminal in major cities in U.S.


42 PlaceEngine by Sony CSL
You can upload the WiFi Info to PlaceEngine Server using Web Service

43 PlaceEngine Web service for WLAN location
200,000 access point in Tokyo area. (Reference )

44 PlaceEngine 150,000 access point in Tokyo area.
(Reference )

45 Mash up with PlaceEngine
PlaceEngine can be used as a Web parts. Your PC Web Browser Web Service PE button PlaceEngine Web DB PlaceEngine Client

46 PlaceEngine×Station Info.
PlaceEngine can be used as a Web parts.

47 PlaceEngine × Restaurant Search

48 PlaceEngine × Restaurant Search
Asked to send “location” to Web Application

49 PlaceEngine × Restaurant Search

50 Apple iPhone Apple iPhone utilize WiFi Positioning System by Skyhook Wireless and GSM Location System by Google.

51 Google Maps for Mobile "My Location" Technology
Adds a GSM Location technology onto mobile phones Do not require GPS (only 15% of mobile phone has GPS)

52 Community: War Driving Community
12,886,129 points from 796,376,798 person (2008/01/23) Mostly for use WiFi Hotspot. Not for positioning.



55 Applications of WLAN Positioning
Beacon Print (Intel Research: UbiComp2005) Detect User’s Preferred Places Do not require pre-acquisition NearMe (MS Research:UbiComp2004) Wireless Proximity Detect near person using BSSID Self organization of WLAN AP data PlaceEngine uses this technology

56 WiFi Tagged Photo Exif for JPEG
Date , Time , other photo data add WiFi BSSID.. || Location Enhanced Photo If you put photos on Flickr with BSSID, You can find the closer photos. With WiFi tag, and WiFi Location DB, You can find the photo using Place. This does not require offline WiFi location database Digital camera with WiFi is now on Market

57 Utilization of WiFi Location System
Location systems are widely spread But not for WiFi Location System GPS is primary location source A lot of GPS application are public NMEA format We need integrate WLS with GPS

58 Implementation of Virtual GPS
Virtual COM driver GPS Application WiFi Locky VirtualGPS NMEA output WLDB Get NMEA data from COM port COM X COM Y Virtual COM Driver

59 Hybrid Positioning with GPS
Virtual COM driver GPS Application WiFi GPS Selection module Locky VirtualGPS NMEA output WLDB Get NMEA data from COM port COM X COM Y Virtual COM Driver

60 Indoor positioning for public area
Outdoors data can be easily collected tool (GPS+ WiFi) How to collect data for indoor area. There is no way to locate position. We need some tool for data acquisition. Which place we should collect? Public place with WiFi

61 Subway of Nagoya

62 We have WiFi on all stations

63 We collected underground WiFi
Map of underground Use a photo of floor map. Plot a location by Hand on the photo. Any place can be collected with this tool. Other data can be offered with Photo.

64 Subway Stumbler We have developed a tool for indoors

65 How many data have we collected?
Current Subway WiFi data. 83 Nagoya Subway Stations 30 person-day 2000 Unique APs 82500 point of locations

66 How many data have we collected?
Current Subway WiFi data. 83 Nagoya Subway Stations 30 day collection 2000 Unique APs 82500 point of locations Only 30 day are required to collect subway data in Nagoya ( 2 million city )

67 It is not only a location information
Location Processing Subway knowledge Time table Arrival time can be estimate There are shops in the station. Restrooms Exchange station It is not only a location information

68 Possible Applications
Train Navigation with “Time” Exit navigation If you collect WiFi data every seconds, Your route is stored in the device. Station/Route can be estimated.  → Train fee is easily calculated.

69 Underground Map of Nagoya
ALPSLAB Underground(Since 2007/12)

70 We already collected WiFi
Total Unique Sakae Area AP = Nagoya Station AP :241

71 Experiment with iPod Touch

72 Writing  Enquate

73 FriendMap Shows the position of Friends and Chat

74 iNavi Location Dependent Database Input Query Result set Infomation

75 NextTrain Realtime TimeTable Station Map Countdown for next train

76 Realtime Train Map Shows position of trains by time-table
Shows current position Trace the train

77 Station Map Maps for underground station

78 U Underground Map

79 Summary Wireless Location Technology
With WiFi, one can easily estimate one’s position Collection of WiFi Location Database Experiment in Real In-door Environment Nagoya Subway

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