1. A Robust High-Performance RFID-Based Location System Kirti Chawla Department of Computer Science University of Virginia

2. 1/30  Goal: Locate objects in an environment  Attributes: -Reliable -Accurate and Fast Location, Location, LocationIntroduction Locate Objects Environments

3. 2/30 RFID Tag Near-field Communication Far-field Communication  Tags and Readers: - Form Factors - Operating Frequency - Power Source RFID PrimerBackground RFID Reader

4. IntellectualContributions  Resilient to environmental conditions / noise  Accommodates numerous scenarios  Tag orientation and vendor hardware –agnostic Adaptability  Signal strength as a reliable metric  Tag sensitivity influences performance  Tag selection & sorting ensures uniformity  Heuristics enhance accuracy Reliability  Tag selection optimizes range & cost  Improved performance by matching tags to readers  Reference tags are unnecessary Scalability 3/30

5. 4/30 Technologies Mismatched Solutions Limiting Constraints Techniques Current State of the ArtBackground

6. 5/30 Pros/ConsMotivation Dark Environment No Line of Sight Cost Effective Solid Obstacles Adaptive Susceptible Invasive Entry Barrier Targeted Unintended Use Pros Cons

7. RetailMotivation Save Time Minimize Misuse Stimulate Spending Improve Turnaround 6/30 Warehouse-Store Frontend Backend

8. Backend: Save TimeMotivation Warehouse-Store 30 Min./Day Avg. Search Time 100, 000 Ft 2 4000 Stores Floor space and Nos. 100 People $ 12/Hour 275 Days/Year Workforce Cost Potential New Savings = $ 600 Million / Year 7/30

9. Frontend: Stimulate SpendingMotivation Warehouse-Store $ 72 /Day/Person $ +1 /Day/Person $ 323B /Year Improve Spending 100, 000 Ft 2 4000 Stores Floor space and Nos. $ 319B /Year $ 79M /Store/Year $ 218K /Store/Day Revenue Generation Potential New Revenue = $ 4.3 Billion / Year 8/30

10. Other Use-CasesMotivation HospitalsAirports  Locate: - Guests / Travelers - Freight - Baggage  Locate: - Medical Supplies - Surgical Instruments - Caregivers - Patients 9/30

11. Localization FrameworkResearch Tag Selection Tag Binning Empirical Power-Distance Relationship Performance-Enhancing Heuristics Collection of Tags Improved Location Estimates Candidate Tags Uniformly Sensitive Tags Tags’ Location Estimates 10/30

12. Tag SelectionResearch  Problem: Tags have variable performance  Solution: Select tags based on their performance Read Range RSS Read Count Tag Selection Results 11/30 Tag CollectionCandidate Tags

13. 12/30 Tag BinningResearch  Problem: Tags have variable sensitivities  Solution: Bin tags based on their sensitivity RSS Read Count Tag Binning Results Same Type Tags Collection Uniformly Sensitive Tags

14. Power-Distance RelationshipResearch  Problem: RF signal variability renders Friis Eq. useless  Solution: Utilize empirical power-distance relationship RFID Tag Transmitted Power: P T Received Power: P R RFID Reader Tag-Reader Distance: D Friis Transmission Equation Comparison 13/30

15. 14/30 Power-Distance RelationshipResearch  Problem: Locate objects using empirical power-distance relationship  Solution: Utilize TX and RX empirical power-distance relationship Read Count Empirical Power-Distance Relationship TX-Side Algorithms RX-Side Models Tags’ Location Estimates Uniformly Sensitive Tags

16. 15/30 TX-Side AlgorithmsResearch  Insight: Similarly behaving tags are neighbors Radio Wave Shared Region Locate Tags: Power-Modulating Algorithms Antenna

17. 16/30 TX-Side AlgorithmsResearch Locate Tags: Power-Modulating Algorithms  Problem: Locate tags using TX RF signal power  Solution: Algorithmically modulate TX RF signal power Algorithms Results

18. 17/30 RX-Side ModelsResearch  Insight: Match tags to readers for higher performance RFID Tag - A RFID Reader - A RFID Reader - B RFID Tag - B

19. Axial Orientation 18/30 RX-Side ModelsResearch Locate Tags: RSS Decay Models  Problem: Locate tags using RX RF signal power  Solution: Adapt theoretical physics model to reality Radial Orientation RSS Decay Model Results Friis Physics Model

20. 19/30 HeuristicsResearch  Problem: Assumption that target and reference tag location coincide leads to localization error  Solution: Consider neighbor reference tags that minimize localization error Localization Error Reference Tag Target Tag Heuristics

21. 20/30 Experimental SetupEvaluation Reference Tag Antenna Mobile Robot with onboard reader and multi-tag RFID Reader Backend Host Tablet Internet

22. 21/30 Tag SelectionEvaluation Back  Insight: Select tags on multi-objective criteria

23. 22/30 Tag BinningEvaluation Back  Insight: Sort tags on their RF performance

24. 23/30 Power-Distance RelationshipEvaluation  Insight: Empirical power-distance relationship enables higher performance Ideal Friis (N = 2) Ideal Friis (N = 3) Ideal Friis (N = 6) Empirical Back

25. 24/30 RSS Decay ModelsEvaluation Back  Insight: Orientation-based decay models lead to orientation-agnostic localization Radial

26. 25/30 TX-Side Localization AccuracyEvaluation Back  Insight: Performance can be improved by denser reference tag deployment Time

27. 26/30 Density Vs PerformanceEvaluation  Insight: Localization performance varies with reference tag density

28. 27/30 RX-Side Localization AccuracyEvaluation Back  Insight: Performance can be improved by minimizing RF dead-zones

29. Approach Localization Time Test Region (m 2 /m 3 ) Localization Accuracy (m) Reference Tags Ni et al., 2003Not Reported2D, 202Active Bekkali et al., 2007Not Reported2D, 90.5 – 1.0Passive Zhao et al., 2007Not Reported2D, 200.14 – 0.29Passive Choi and Lee, 2009Not Reported2D, 140.21Passive Choi et al., 2009Not Reported2D, 30.2 – 0.3Passive Zhang et al., 2010Not Reported2D, 360.45Active Brchan et al., 2012A few seconds2D, 221Active TX-Side: Combined Algorithms 1.67 minutes2D, 80.18Passive RX-Side: Combined Models ~4 seconds 2D, 8 3D, 16 0.30- 0.60Optional, Passive Comparative EvaluationEvaluation

30. 29/30 Summary and Future WorkConclusion RFID-Based Location System: - Pure RFID reliably locates objects - Match tags to readers - Tag selection & binning improves tag performance - TX/RX empirical power-distance relationship - Algorithms, models, and heuristics for object localization - Identify / mitigate key localization challenges Future Research Directions: - Scalability - Combination of approaches - Visualization tools - Field testing and commercialization

31. Co-directed 10 undergraduate theses and Capstone projects Won the 2011 SEAS Entrepreneurial Concept Competition Placed 2 nd at the 2012 Darden Business Competition Journal Publications: Kirti Chawla, Christopher McFarland, Gabriel Robins, and Wil Thomason, A Robust Real-Time RFID-Based Location System, 2013, In preparation 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, Christopher McFarland, Gabriel Robins, and Connor Shope, Real-Time RFID Localization using RSS, IEEE International Conference on Localization and Global Navigation Satellite System, 2013, Italy, pp. 1-6, Best Presentation Award Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Efficient RFID-Based Mobile Object Localization, 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, IEEE International Symposium on Wireless Pervasive Computing, 2010, Italy, pp. 301-306 Patents: Kirti Chawla and Gabriel Robins, System and Method For Real-Time RFID Localization, 2013 Kirti Chawla and Gabriel Robins, Real-Time RFID Localization Using Received Signal Strength (RSS) System and Related Method, US Patent: 61/839,617, 2013 Kirti Chawla & Gabriel Robins, Object Localization with RFID Infrastructure, WIPO Patent: 2012047559 A3, 2012; US Patent: 20130181869 A1, 2013 DeliverablesContributions 30/30

32. Backup Slides

33. Backend: Minimize MisuseMotivation Warehouse-Store 100, 000 Ft 2 4000 Stores Floor space and Nos. Potential New Savings = $ 200 Million / Year 1 Million Items 5 % Misuse Rate $ 1 / Item Reported Misuse Back

34. Frontend: Improve Turnaround Motivation Warehouse-Store $ 72 /Day/Person 3K /Store/Day +5 /Store/Day Maximize Utility 100, 000 Ft 2 4000 Stores Floor space and Nos. Potential New Revenue = $ 500 Million / Year $ 319B Rev/Year $ 79M /Store/Year $ 218K /Store/Day Revenue Generation Back

35. How Our Research Can Affect Your Bottom Line Motivation $ 600 Million / Year $ 200 Million / Year $ 500 Million / Year Stimulate Spending $ 4.3 Billion / Year Save Time Improve Turnaround Minimize Misuse

36. Localization ChallengesApproach Radio Interference OcclusionsTag SensitivityTag SpatialityTag Orientation Reader Locality

37. RFID Reader RFID Tag VerticalHorizontal Reliability through Multi-TagsApproach Platform Side View ParallelOrthogonal RFID Tag Platform Top View  Problem: Optimal tag reads occur at certain orientations  Solution: Multi-Tags provide orientation redundancy

38. Power-Modulating AlgorithmsApproach Linear SearchBinary SearchParallel Search O(#Tags  Log#Power-Levels) O(#Tags  #Power-Levels) O(#Power-Levels) Reader Output Power Range 0MAXMID Back

39. Heuristics FrameworkApproach Root Sum Square Minimum Power Selection Absolute Difference Localization Error Meta Heuristic Back  Problem: There can be multiple neighbor reference tags  Solution: Select neighbor reference tags using different selection criteria

40. RSS Decay ModelsEvaluation Back  Insight: Orientation-based decay models lead to orientation-agnostic localization

41. TX-Side Localization TimeEvaluation Back  Insight: Faster algorithms provide lower tag detectability

42. Technology Cost Breakup (Post R&D) Product Warehouse-Store Variable (Software) $ 50K (Backend) Software and Misc. Cost 100, 000 Ft 2 4000 Stores Floor space and Nos. $ 20K (300 Ant.) $ 20K (80 Readers) $ 10K (1M Tags) RFID Hardware Cost Total Cost 1 st Year = $ 100K + SLC * + AMC + / Store Total Cost N th Year = SLC + AMC / Store; N  ≥ 2 * Software License Cost, + Annual Maintenance Cost | All costs are current estimates Old Revenue = 79M / Store / Year New Revenue = 81M / Store / Year

43. TX-Side AlgorithmsResearch Locate Readers: Proximity-Sensing Algorithm  Problem: Locate readers using TX RF signal power  Solution: Sense proximity of neighbor tags

44. Object Localization with RFID Infrastructure USPTO and WIPOPatents 4/30