1. Indoor Location Estimation Using Multiple Wireless Technologies
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Indoor Location Estimation Using Multiple Wireless Technologies
Dhruv Pandya, Imperial College
Ravi Jain, Telcordia Technologies
Emil Lupu, Imperial College

2. Location-aware Computing
Proliferation of mobile devices + development of standard low-cost wireless networks e.g. Bluetooth
Richer application set
User customization
Example applications: E911: locating callers in emergencies, Call forwarding, Find nearest restaurant
Single-technology Approaches
Infrared transmissions: Active Badges, Want 92
Time of Flight of Ultrasound: Cricket, Priyantha 2000
Time of Flight of RF, Triangulation: GPS
RF, Scene Analysis: RADAR
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Dhruv Pandya

3. 17/01/2019
Current Limitations
Technology limitations e.g. GPS does not work indoors
Application restricted to sensor technology and its accuracy
System-dependent location representation
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Dhruv Pandya

4. Multiple Technologies
Devices with multiple wireless technologies
Application flexibility
Improved location accuracy
Location accuracy
Jain ICDE 2001
Pandya June 2002, Masters’ Thesis
Similar approaches:
Geometric modelling: Leonhardt 1998
Conflict resolution of multiple sources: Myllymaki 2002
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Dhruv Pandya

5. Static Scene Analysis – Data Collection
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25.7m
Offline Samples
49 physically distinct locations
50 signal samples from each base station, in each of four directions
Run-time Samples
6 locations per three, two and one base station coverage scenario
50 samples from each base station while facing the North direction
Corridor
32.2m
As used in RADAR. The disadvantage of this approach is that certain areas may receive more offline locations. Hence those areas may be favoured more. Make a note about how bluetooth points are used as access points. Runtime samples: we account for temporal and spatial variability of wireless links.
Signal Characteristics
802.11: signal strength in dBm
Bluetooth: ‘link quality’
Offline location
Base Stations
Run-time location
Bluetooth Base Stations
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Dhruv Pandya

6. Final location estimate
Smallest Polygon
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Runtime A
Comparison
Function
Corridor
Offline
Final location estimate C B
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Dhruv Pandya

7. Triangulation Runtime A Comparison Function Corridor Offline C B
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Dhruv Pandya

8. Final location estimate
Nearest Neighbor
Runtime A
Comparison
Function
Corridor
Offline
Final location estimate C B
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Dhruv Pandya

9. Basic Fusion Comparison Resolve estimates ‘Fuse’ 17/01/2019
Dhruv Pandya

10. Performance Evaluation
Distance Error
Average errors for locations with similar coverage scenario
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Dhruv Pandya

11. Comparison of Algorithms
Reduction in error with increased base stations
SP has best accuracy under three (1.8-2m) and one (4-5m) base station coverage
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Dhruv Pandya

12. Vs Bluetooth
Bluetooth performs better than for all algorithms, except SP
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Dhruv Pandya

13. Basic Fusion
Basic Fusion performs better than TN ( m for three, m for two) and NN (0.5-2m for three, m for two)
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Dhruv Pandya

14. Fusion Vs Single-technologies
Bluetooth signal characteristic: Link Quality
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Dhruv Pandya

15. Summary
Location sensor technology restricts applications: Use multiple technologies
Experimental investigation into improving location accuracy by data fusion using Bluetooth and wireless LAN
Differences to RADAR
Introduce spatial and temporal variability in runtime samples
Attempt to represent a more realistic situation
Single Technology
Smallest Polygon works best with wireless LAN
Bluetooth works well with Triangulation and Nearest Neighbor
Multiple Technologies:
Increased base stations: accuracy increases m
Fusion improves accuracy over (by m), but looses out over Bluetooth
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Dhruv Pandya

16. Further Work Sophisticated single-technology and fusion algorithms
Improved location accuracy metrics
User A is 50% likely to be 2m radius of an estimate Confidence Precision
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Dhruv Pandya

17. Acknowledgement
Dhruv Pandya acknowledges travel funding support from EPSRC project GR/R95715/01 AEDUS: Adaptable Environments for Distributed Ubiquitous Systems
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Dhruv Pandya

18. Questions
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Dhruv Pandya

19. Single-technology Approaches
Infrared transmissions: Active Badges
Time of Flight analysis of Ultrasound: Cricket
Triangulation, RF: GPS
Scene Analysis, RF: RADAR
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Dhruv Pandya

20. Presentation Outline Motivation Related work Data collection
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Presentation Outline
Motivation
Related work
Data collection
Location Estimation and Fusion algorithms
Performance Evaluation
Conclusion
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Dhruv Pandya

21. Static Scene Analysis – Bracketing
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Static Scene Analysis – Bracketing
Location
Base Station ID x y SS
where r = Average of 50 Run-time SS samples, b = bracket value determined by the standard deviation of SS in our environment
Select candidate(s) that match the first SS in {r-b,…,r,…r+b}. The process may return more than one location because it is possible that more than one location may match the first SS.
the location(s) that match the first possible SS in Q
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Dhruv Pandya

22. Smallest Polygon 17/01/2019 Dhruv Pandya 17/01/2019
SP works under the following theory: if there was no interference then for a given location, candidates from all BS would be the same. SmallestPolygon is a function that takes as input one or more sets of location estimates. Thus we want to find the candidates that are closest to each other. Base case: if Ea is the only non-empty set, then selects one candidate randomly.
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Dhruv Pandya

23. Triangulation
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Dhruv Pandya

24. Nearest Neighbour 17/01/2019 Dhruv Pandya 17/01/2019
Note that RADAR consider offline locations. We consider runtime locations, temporal and spatial variability. Radio interference at one point in time will not be the same as another – that’s an assumption.
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Dhruv Pandya

25. Standard Deviation of Algorithms
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Dhruv Pandya