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LANDMARC. Indoor Location Sensing Using Active RFID Lionel M. Ni, HKUST Yunhao Liu, HKUST Yiu Cho Lau, IBM Abhishek P. Patil, MSU. LANDMARC. Motivation. Overview of RFID. LANDMARC Approach. Performance Evaluation. Conclusion. Why Indoors Location-Sensing ?. Location-aware Computing.
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LANDMARC Indoor Location Sensing Using Active RFID Lionel M. Ni, HKUST Yunhao Liu, HKUST Yiu Cho Lau, IBM Abhishek P. Patil, MSU
LANDMARC Motivation Overview of RFID LANDMARC Approach Performance Evaluation Conclusion
Location-aware Computing • The location is an important context that changes whenever the object moves • Location-aware services allow to offer value-added service to the user, depending on their current geographic position and will be a key feature of many future mobile applications • Sensing the location: explicit and implicit cooperation; outdoor or indoor
Location Sensing Techniques • Triangulation: use geometric properties of triangle to compute object locations • Signal strength: signal attenuation is a function of distance to the signal source • Scene analysis: use features of a scene observed from a certain reference point • Proximity: determine if an object is near a known location
Sensing Technologies • Infrared • Ultrasonic • Radio Frequency • RFID • 802.11 • Bluetooth • Others
Existing Technologies and Systems Infrared Example: Active Badge Location System • Line-of-sight • Coarse resolution • Short range • Blocked by common materials • Light, weather sensitive • Pollution can affect transmission • Low power requirements • Low circuitry costs: $2-$5 for the entire coding/decoding circuitry • Simple circuitry • Higher security • Portable • High noise immunity
IEEE 802.11Example: RADAR • It is using a standard 802.11 network adapter to measure signal strengths at multiple base stations positioned to provide overlapping coverage in a given area
Strength Easy to set up Requires few base stations Uses the same infrastructure that provides general wireless networking in the building Weakness Poor overall accuracy: scene-analysis: within 3 meters with 50 percent probability signal strength: 4.3 meters at the same probability Support Wave LAN NIC Microsoft RADAR
Active Bat (AT&T) ultrasound time-of-flight measurement can locate Bats to within 9cm of their true position for 95 percent of the measurements Ultrasonic
Ultrasonic time-of-flight and a radio frequency control signal Lateration and proximity techniques Decentralized scalability 4x4 square-foot regions Beacon Listener Cricket Location Support System (M.I.T)
Objects are located by homogenous sensor nodes without central control SpotOn tags use received radio signal strength information as a sensor measurement for estimating inter-tag distance No complete system yet RFID: SpotON
Selection criteria • Use commodity products or off-the-shelf components • Low cost • Resolution: no more than 2-3 meters • Decision: RFID technology LANDMARC Prototype
RFID is a means of storing and retrieving data through electromagnetic transmission to a RF compatible integrated circuit 3 basic components What is RFID (Radio Frequency Identification)?
Active RFID • RF Reader • Range up to 150 feet • Identify 500 tags in 7.5 seconds with the collision avoidance • Support 8 power levels (function of distance) • Active Tag system • Emit signal, which consists of a unique 7-character ID, every 7.5 seconds for identification by the readers • Button-cell battery (2-5 years life) • Operate at the frequency of 303.8 MHz
Active RFID Advantages • Non-line-of-sight nature • RF tags can be read despite the extreme environmental factors : snow, fog, ice, paint … • be read in less than 100 milliseconds • promising transmission range • cost-effectiveness
How many readers are needed? Build an array of readers: too expensive How reliable is the tag detection? Not very reliable due to signal attenuation Placement of RF readers Cannot measure distance directly Using RFID: First Attempt
the received signal power at distance is given by • free space loss is given by
LANDMARC Approach • The LANDMARC system mainly consists of two physical components, the RF readers and RF tags
Distance estimation Placement of reference tags Selection of k neighboring reference tags Weight of each selected reference tags Known Reference Tags
• • • the placement of the reference tags the value of k in this algorithm the formula of the weight Three Key Issues
Distance Estimation: Signal Strength • Signal Strength Vector of an unknown tag • Signal Strength Vector of a reference tag • Euclidian distance
Effect of the Value k Cumulative Percentile Of Error Distance When K Value Is 2, 3, 4, 5
Influence of The Environmental Factors Cumulative Percentile Of Error Distance in Daytime & Night
Influence of The Environmental Factors (cont’d) Change The Placements Of Tracking Tags
Influence of The Environmental Factors (cont’d) Cumulative Percentile Of Error Distance When Changing The Placement Of Tracking Tags
Effect of The Number of Readers Cumulative Percentile Of Error Distance With 3 or 4 Readers Data
The Effect of Placement of Reference Tags Without Partition
Effect of Placement of Reference Tags (cont’d) With Partition
Effect of Placement of Reference Tags (cont’d) With Partition
Placement of Reference Tags Replacements of the Reference Tags with a Higher Density
Effect of Higher Density Reference Tags Cumulative Percentile Of Error Distance With Higher Reference Tag Density
Lower Density of Reference Tags Replacements of the Reference Tags with a Lower Density
Effect of Lower Density Reference Tags Cumulative Percentile Of Error Distance With lower Reference Tag Density
Overall Accuracy • Using 4 RF readers in the lab, with one reference tag per square meter, accurately locate the objects within error distance such that the largest error is 2 meters and the average is about 1 meter.
Conclusions • RFID can be a good candidate for building location-sensing systems • Able to handle dynamic environments • Suffer some problems • Difference of Tags’ Behavior • RFID does not provide the signal strength of tags directly • Unable to adjust emitting interval • Standardization
5.Stop eating 6.Back to desk 1.walk Desk 2. Tracking movement Refrigerator Desk 3. Notify where you are (Location sensing) 7. Time sensing 10. Walk around 8. immobility sensing An Obesity Care Case 4. Notify your eating schedule 9. Notify to move 13. Distance& Time sensing 18. Refer Healthy food 11. Proximity sensing 14. Notify to stop 16. Notify “ok to eat” 17. Go to kitchen 12. Walk away from sofa 15. Go back to desk Refrigerator Desk Sofa 19. Eat matched food
Triangulation • Lateration • Direct • Time-of-flight • Attenuation • Angulation
Scene Analysis • use features of a scene observed from a certain reference point • Proximity • determine if an object is near a known location
Project Motivation • GPS’s inability for accurate indoor location sensing • Develop a cost-effective indoor location sensing infrastructure • Enables location-based Web services for mobile-commerce (m-commerce) environment • Plenty of other application scenarios, depending on your imagination and creativity
Passive RFID vs. Active RFID Active tag System
A Triangulation Approach A Triangulation Approach