Efficient Localization based on Imprecise Anchors in RFID System

Transcription

1 State Key Laboratory for Novel Software Technology Nanjing University, China Presenter: Dr. Lei Xie, Associate Professor

2 Outline Background & Motivation Experimental Study Solutions Performance Evaluation

3 Scenario Context-aware, location-based Service Super Markets Libraries

4 Scenario Location is the most important factor Super Markets Libraries

5 Scenario Conventional localization mainly leverage precise anchor nodes to help compute the user s location. Anchor nodes are intentionally deployed for localization. Challenges: Difficult to find or deploy enough anchor nodes for accurate localization. LANDMARC Wireless Networks 2004, Ni et al VIRE ICPP 2007, Zhao et al

6 Scenario RFID Tag New Opportunities: The same type of items are usually placed in a specified range. The location of these items can be approximately known according to the layout of the merchandize.

7 Scenario RFID Tag 10 tags 8 tags Locate the mobile RFID reader carried by user.

8 Motivation The widely deployed RFID tags are natural anchor nodes with imprecise locations. It is possible to locate the user according We propose solutions to locate the mobile to the categories of the scanned tags and user based on imprecise anchor nodes in the layout of the merchandize. RFID systems. When multiple categories of tags are scanned, the reader s location can be computed more accurately, by sufficiently leveraging the layout information of these categories.

9 Motivation Challenges The anchor positions are imprecise. Items of the same category can be placed in a range rather than on a precise point on the shelf. The reader may miss some of the nearby RFID tags due to imperfect radio environments. Time efficiency is required. RFID readers takes more time to scan nearby RFID tags when the density of RFID is increased. Return useful information in a timely manner even if the reader is surrounded by thousands of RFID tags.

10 Problem Formulation shelves aisle product with a RFID tag antenna target (customer) Color ranges Product categories

11 Experimental Study How does the issues like path loss, energy absorption and mutual Detection Regions radius interference affect the actual performance in realistic settings? Relationships among Tag size, Power and Tag density Scanning Time RSSI distribution(28.7dbm)

12 Observation Tag density effects Detection Regions radius A higher tag density more tags to be identified detection regions radius decreases

13 Observation Scanning time Relationships among Tag size, Power and Tag density Increase the scanning power The number of tags increases The scanning time increases Control the power to limit the number of tags in the detection region

14 Observation RSSI distribution Major detection region Minor detection region Tags in major detection region have higher RSSI values

15 System framework Server and database Antenna RFID Reader Tag Tag Tag Tags Tablet User Wifi AP

16 Baseline Solution Category Cardinality based Protocol (CCP) Our Solutions n1 n2 n3 n4 n5 n6 n7 n8 n9 n10 n1 n2 n3 n4 n5 n6 n7 n8 n9 n10

17 RSSI based Protocol (RP) Our Solutions s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s1 s2 s3 s4 s5 s6 s7 s8 s9 s10

18 Our Solutions Compute the optimal value of power Reasonably adjust the power of the reader, while make sure the delay to satisfy the time limitation. Use a pre-scan process to get the identified tag size. Estimate the tag density based on training data. Compute the target tag size limit with respect to the remaining time. Compute the optimal power for the mobile reader.

19 Category Matching based Protocol (CMP) Our Solutions For each scan, we can get the number of tags in each identified category, we can denote it as a vector. For every potential position during the scanning process, we can calculate a series of such potential vectors. By matching the collected vector in the target position with these potential vectors, we can get k- nearest vectors which have the highest similarities.

20 Our Solutions Category Matching based Protocol (CMP) Core problem: How to compute the vector for each potential position in the specified range? Solution: use the major detection region based model as well as the training data set to derive the potential scanning range and the potential vector.

21 Distance Voting based Protocol (DVP) Our Solutions Based on the training data, for various powers and tag densities we can estimate the distance between each tag and the reader according to RSSI. Each category can vote for the estimated distance according to the collected RSSI from their tags. The category with the minimal estimated distance can decide the candidate position. The other categories can further calibrate the localization result according to their estimated distances.

22 Our Solutions Distance Voting based Protocol (DVP) Key issue: verify both the direction and the distance to the reference categories.

23 Performance Evaluation Experiments Setup

24 Performance Evaluation Comparison of execution time Category Cardinality based Protocol (CCP) RSSI based Protocol (RP) Category Matching based Protocol (CMP) Distance Voting based Protocol (DVP) The execution time of CMP is only 62% of that of CCP The execution time of DVP is only of 57% of that of RP State Key Laboratory for Novel Software 24 Technology, Nanjing University, China

25 Accuracy of Localization Performance Evaluation Category Cardinality based Protocol (CCP) RSSI based Protocol (RP) Category Matching based Protocol (CMP) Distance Voting based Protocol (DVP) The localization error of CMP is only 75% of that of CCP The localization error of DVP is only 73% of that of RP

26 Performance Evaluation Varying the tag density The execution time of CMP is only 58% of that of CCP The execution time of DVP is only 60% of that RP As the tag density increases, the localization error decreases

27 Performance Evaluation Varying the distribution of categories The category s width has little effect on the execution time As the category s width increases, the localization error increases

28 Conclusion We present a time-efficient localization scheme to continuously tracking the mobile user. Experimental results indicate that our solutions can accurately locate the mobile users by a real-time approach. Our solution is based on the large number of tags scanned by the RFID reader, it does not require any additional precise anchor nodes, thus effectively avoids the significant deployment cost. To the best of our knowledge, this is the first localization work based on imprecise anchor nodes in RFID systems. In order to continuously track the mobile user, we propose two time-efficient algorithms to accurately locate the moving user.

29 Thank you! Q&A