1. Object Localization Using RFID Kirti Chawla Department of Computer Science University of Virginia

2. The Problem of Locating Objects Research Milestones Background Motivation Proposed Approach Experimental Evaluation Conclusion 1/23 Outline

3. 2/23 Locate Environments Goal: Find positions of objects in an environment Hypothesis: Standard RFID is sufficient and effective Key-factors: Performance, applicability and shortcomings Locating ObjectsProblem Objects

4. Research DeliverablesMilestones Journal Publication: Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework, International Journal of Radio Frequency Identification Technology and Applications, Inderscience Publishers, 2011, Vol. 3, Nos. 1/2, pp. 2-30 Conference Publications: Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Efficient RFID-Based Mobile Object Localization, Proceedings of IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, 2010, Canada, pp. 683-690 Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Object Localization using RFID, Proceedings of IEEE International Symposium on Wireless Pervasive Computing, 2010, Italy, pp. 301-306 Patent: Kirti Chawla, and Gabriel Robins, Object Localization with RFID Infrastructure, US Patent Application Number: PCT/US2011/053067, filed with WIPO/USPTO September 2011 Copyright: Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework, US Copyright Case Number: 1-633487801, 2011 Startup Venture: Co-founded Diorama Technologies LLC, in partnership with private investors Raised venture capital funding and negotiated licensing terms Other investors have shown strong interest in commercializing our ideas

5. WIPO/USPTOPatent Object Localization with RFID Infrastructure

6. 3/23 Technologies Mismatched Solutions Limiting Constraints Techniques Current State of the ArtBackground

7. 4/23 RFID Reader RFID Tag Near-field Propagation Far-field Propagation Readers: Variety of form-factors and frequencies Tags: Flexible power source, frequency, and form factors RFID PrimerBackground

8. 5/23 Motivation Dark Environment No Line of Sight Why locate objects using RFID ? Cost Effective Solid Obstacles Natural Fit Adaptive

9. 6/23 Power-Distance RelationshipApproach Distance Reader Power Tag Power Problem: Radio variability renders Friis equation practically useless Insight: Utilize empirical power-distance relationship Comparison

10. 7/23 Antenna Insight: Similarly behaving tags are close to each other Radio Wave Shared Region Empirical Power-Distance Relationship Approach

11. 8/23 Tag Sensitivity CharacterizationApproach 13% 25%54%8% High SensitiveAverage SensitiveLow Sensitive Pile of Tags Problem: Tags have variable sensitivities / performance Insight: Bin tags based on their sensitivity Results Key Challenges

12. RFID Tag VerticalHorizontal RFID Reader 9/23 Reliability through Multi-TagsApproach Problem: Optimal tag reads occur at certain orientations Insight: Multi-tags provide orientation redundancy Results Platform Side View ParallelOrthogonal RFID Tag Platform Top View

13. Setup Phase 10/23 Tag Localization ApproachApproach Localization Phase Signal Strength Metric: MTDP

14. 11/23 Tag Localization AlgorithmsApproach Linear SearchBinary SearchParallel Search O(#Tags  Log#Power-Levels) O(#Tags  #Power-Levels) O(#Power-Levels) Reader Output Power Range 0MAXMID

15. 12/23 Reader Localization ApproachApproach Setup Phase Localization Phase

16. 13/23 Localization ErrorApproach Problem: Assumption that target and reference tag locations coincide leads to localization error Insight: Consider other nearby reference tags in order to minimize the localization error Heuristics Reference Tags Target Tag

17. 14/23 Experimental SetupEvaluation Robot Design Track Design

18. 15/23 Empirical Power-Distance Relationship Evaluation Insight: Only empirical power-distance relationship can provide high localization performance Theoretical (N = 2) Theoretical (N = 3) Theoretical (N = 6)Empirical Back

19. 16/23 Localization AccuracyEvaluation Insight: Performance can be improved by denser reference tag deployment Actual Position Inferred Position

20. 17/23 Localization TimeEvaluation Insight: Faster algorithms provide lower tag detectability Linear Search (HL) Parallel Search Linear Search (LH) Binary Search

21. 18/23 Localization Performance Vs #Tags Evaluation Insight: Localization performance varies with tag density Diminishing returns

22. Approach Average Time (minutes) Test Area (m 2 ) Localization Error (m) Notes Setup Phase Localization Phase Ni et al., 2003 --- 2 Pure RFID Alippi et al., 2006 -- 200.68 Bekkali et al., 2007 -- 90.5 – 1.0 Senta et al., 2007 -- 20.2 Wang et al., 2007 --- 0.1 – 0.9 Zhang et al., 2007 --- 1Hybrid Seo and Lee, 2008 -- 50.2 – 1.6Pure RFID Choi and Lee, 2009 -- 14.40.02Hybrid Choi et al., 2009 --- 0.21 Pure RFID Joho et al., 200927 -- 0.38 Linear Search (LH) Linear Search (HL) Binary Search Parallel Search Measure and Report Combined Approach 161.23 29.78 47.24 1.67 0 161.23 5.28 1.42 1.95 1.67 0 10.32 8 0.27 0.29 0.31 0.35 0.25 0.18 Pure RFID 19/23 Comparative EvaluationEvaluation Preliminary new experiments show: 1) Reference tags optional 2) Instantaneous localization 3) High accuracy

23. 20/23 Applications Warehouses Hospitals Supply Chains Monitor life-critical events Smart Carts Provide ground truth Tier-II Applications Assisted Living Location-Aware Services Energy Saving in Buildings Locating Objects Tier-I Applications

24. 21/23 Object Location VisualizationInterface

25. 22/23 Future DirectionsConclusion RFID-only Object Localization Framework: - Showed that pure RFID can be used for object localization - Introduced a power-distance relationship metric - Proposed tag binning to mitigate tag sensitivity variability - Devised effective localization algorithms and heuristics - Identified / mitigated key localization challenges Future Research Directions: - Scalability - Technology Evolution - Localization performance - Visualization tools - Field testing and Commercialization

26. Research DeliverablesMilestones Journal Publication: Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework, International Journal of Radio Frequency Identification Technology and Applications, Inderscience Publishers, 2011, Vol. 3, Nos. 1/2, pp. 2-30. Conference Publications: Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Efficient RFID-Based Mobile Object Localization, Proceedings of IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, 2010, Canada, pp. 683-690. Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Object Localization using RFID, Proceedings of IEEE International Symposium on Wireless Pervasive Computing, 2010, Italy, pp. 301-306. Patent: Kirti Chawla, and Gabriel Robins, Object Localization with RFID Infrastructure, US Patent Application Number: PCT/US2011/053067, filed with WIPO/USPTO September 2011. Copyright: Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework, US Copyright Case Number: 1-633487801, 2011. Startup Venture: Co-founded Diorama Technologies LLC, in partnership with private investors Raised venture capital funding and negotiated licensing terms Other investors have shown strong interest in commercializing our ideas 23/23

27. WIPO/USPTOPatent Object Localization with RFID Infrastructure

28. Backup Slides

29. Localization Solutions Localization Type Localization Technique Signal StrengthSignal Phase Arrival Time Environmental Self Organizing Localization SpaceBackground

30. Localization ChallengesApproach Radio Interference OcclusionsTag SensitivityTag SpatialityTag Orientation Reader Locality Back

31. Tag Sensitivity – Single TagEvaluation Constant Distance/Variable Power Variable Distance/Constant Power Back

32. Tag Sensitivity – Multi-Tag (Proximity) Evaluation Constant Distance/Variable Power Variable Distance/Constant Power Back

33. Tag Sensitivity – Multi-Tag (Rotation-1) Evaluation Constant Distance/Variable Power Back

34. Tag Sensitivity – Multi-Tag (Rotation-2) Evaluation Variable Distance/Constant Power Back

35. Error-Reducing HeuristicsApproach Root Sum Square Minimum Power Selection Absolute Difference Localization Error Meta Heuristic Problem: There can be multiple nearby reference tags Insight: Select nearby reference tags using different schemes Back