563.11.2 Wireless Location Tracking

1. 563.11.2 Wireless Location Tracking Saman A. Zonouz University of Illinois Fall 2007

2. Outline Algorithms Technologies Products 2

3. Wireless Location Tracking Approaches Brannstrom 02. • Direct Approaches • E.g. GPS • Expensive • Impractical for indoors location tracking • Indirect Approaches • Other nodes determine location • Limited power • Local communications 3

4. Anchor/Beacon Nodes • Know their global coordinates a priori • Hard coded • GPS • Beacon placement impacts Location Estimation Precision • Convex Hull around the sensor network • Additional beacons in the center is helpful Bachrach 04

5. Received Signal Strength Indication • In Theory • Radio Signal Energy diminishes with Distance Square • In Practice • Noise • Radio propagation is Non-uniform in reality • Different Propagationon Asphalt and Grass • Physical ObstaclesReflect or Absorb Bahl 00

6. Radio Hop Count • The distance between two communicating nodes is less than ‘R’ (maximum range) • Simple connectivity, No matter what signal strength is • 0.5R Accuracy • Hop count ( ): length of the shortest path in the graph between Kleinrock 78

7. Time Difference of Arrival • Each node equipped with • Speaker and Microphone • Wireless Communication Capability • Impressively Accurate • Echo-free environments Savvides 01

8. Angle of Arrival • Radio and Microphone arrays • Analyze Phase and Time difference • Determine the direction of a transmitting node • More accurate and expensive than TDoA Priyantha 01

9. Semidefinite Programming • Geometric constraints between nodes • Linear Matrix Inequalities (LMIs) • All LMIs form a semidefinite progam • Resulting a “Bounding Region” Doherty 01

10. Diffusion • Idea: The most likely position of a node is the Centroid of its neighbors positions • S1: Initialize the position of all non-beacon nodes to (0, 0) • S2: Repeat until convergence {Set each position to the centroid of its neighbors} • Only Radio Connectivity data are required • Low accuracy • Sparse sensor networks • Nodes are outside the convex hull of the beacons Bulusu 02

11. APIT • Assumption • Nodes ni can hear a large number of beacons • Only “Simple Connectivity” data are available • Beacon Triangle (BT), formed by 3 beacons • ni decides it is inside/outside a given triangle • ni finds the intersection of BTs containing it • Centroid of the intersection is the position estimate He 03

12. Wireless Location Tracking Technologies • Infra Red (IR) has a substantial presence • Radio Frequency (RF) is usually used • Issues • Frequency Range • What type of RF? • Higher freq.  Shorter range • Tags • Size • Power consumption Weissman 04

13. RFID • Passive • Battery-less tags • Reading Range is 1-2 meter • Low cost • Active • Actively transmit their ID in response • Range tens of meters Ward 06

14. Ultra Wide Band (UWB) • Based on sending short pulses (typically <1ns) • High Accuracy • Low power consumption • Generally a radio technology • For short-range high bandwidth communications Fontana 07

15. WLAN (IEEE 802.11b) • 2.4 GHz ISM band • Popular in hospitals • 11 Mbps, 50-100 meters • Indoor location Tracking • Accuracy ~2 meters • Tags • Bulky • High power consumption • Antennas in Access Points (AP) Weissman 04

16. Bluetooth • Short-range Communication Technology • 2.4 GHz ISM Band based on IEEE 802.15.1 • Intended to replace cables • Readings in medical devices, Cell phones, Laptops • Supports Networks with up to 8 Nodes • Data transfer rate of 1 Mbps • Range 10-100 meters • Expensive hardware • High power consumption Bluetooth Wikipedia

17. Zigbee Communication technology for Monitoring and Control Based on IEEE 802.1.15 Three device types Coordinator: root of network tree Router: passing data from other devices End device nodes Simpler and Cheaper than Bluetooth Low Power Consumption Low Data Rate 200Kbps Supports Networks up to 65K nodes Zigbee Wikipedia 17

18. Products: Asset Location in Hospitals • Why is it important? • Large hospitals lose thousands of dollars each year • One-third of hospital staff time is spent searching for equipments Siemens 06 18

19. HealthTrax (InfoLogix) • RFID Real-Time Location System • Tracking assets and people in hospitals • Using hospital’s 802.11 infrastructure www.infologixsys.com

20. UbiSense • Real-Time Location System • Tags and Sensors • Based on Ultra-WideBand • Accuracy of 15 cm in a 3D env. • Location Tracking Algorithm • Each Sensor uses AoA • TDoA for a pair of sensors • Position Updating upto 20 times per sec. • 5 Years Battery life www.ubisense.net

21. Tadlys’ Indoors Location (TOPAZ) • Patient/Asset Location using Bluetooth • Tags, Cell phones, PDAs • Accuracy 2-3 meters • Tens of tags simultaneously • Reliability >95% • Positioning delay 15-30 seconds www.tadlys.com

22. Cricket • Wireless Location Tracking System • Time Difference of Arrival (TDoA) • Open Source Software based on Tiny OS • Listeners • Attached to a host using RS232 • Passive • Beacons • Active Priyantha 00 Demo: http://cricket.csail.mit.edu/#applications

23. Other Systems • AeroScout Asset/Patient Tracking • Wi-Fi based RFID tags • RSSI & TDoA • Indoors Range up to 180 feet • SYSGEN Asset Location • Hospital’s 802.11 wireless infrastructure • ID badge with bio-metric thumb print reader • Associate medical devices to ID badges www.aeroscout.com www.sysgen.com

24. Conclusions • Current Indoors Wireless Location Tracking Systems • TDoA and AoA are the two most widely-used approaches • IEEE 802.11 is usually used for asset location in hospitals • All deployed algorithms need beacon nodes • Intelligent power-saving Zigbee nodes makes Zigbee a proper technology for location tracking systems

25. References • Nissanka B. Priyantha, Hari Balakrishnan, Erik Demaine, Seth Teller, Anchor-Free Distributed Localization in Sensor Networks, LCS Tech. Report #892 • Adam Smith, Hari Balakrishnan, Michel Goraczko, Nissanka Priyantha,Tracking Moving Devices with the Cricket Location System, Proc. 2nd USENIX/ACM MOBISYS Conf., Boston, MA, June 2004. • D. Moore, J. Leonard, D. Rus, S. Teller. "Robust distributed network localization with noisy range measurements." In Proceedings of the Second ACM Conference on Embedded Networked Sensor Systems (SenSys '04). Baltimore, MD. November 3-5, 2004. pp. 50–61. • Jonathan Bachrach and Christopher Taylor, “Localization in Sensor Networks.” Book chapter, 2004. • Christopher Taylor, Ali Rahimi, Jonathan Bachrach, Howard Shrobe, Anthony Grue, “Simultaneous Localization, Calibration and Tracking in an Ad Hoc Sensor Network.” IPSN, 2006. • Chuang-wen You, Yi-Chao Chen, Ji-Rung Chiang, Energy-efficient Zigbee localization Project, National Taiwan University