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  1. Final Year ProjectLYU0301 Location-Based Services Using GSM Cell Information over Symbian OS Mok Ming Fai CEG mfmok1@cse Lee Kwok Chau CEG leekc1@cse

  2. Agenda • Symbian OS • Location-based services (LBS) • Current GSM Positioning Methods • Using GSM cell information in 2D space and 1D path • MTR Travaller • Future Work

  3. The Symbian OS • Standard operating system for data-enabled mobile devices • 32-bit, little-endian operating system working with ARM architecture chips with v4 instruction set or higher

  4. Location-Based Services (LBS) • Services are provided based on user’s location under different wireless networks • LBS is applicable in various fields • Different issues have to be considered • Each of them requires different accuracy and latency

  5. GSM Positioning Methods • Region-based • Cell Information (CI) • Point-based • Time of Arrival (TOA) • Angle of Arrival (AOA) • Enhanced Observed Time Difference (E-OTD) • Assisted GPS (A-GPS)

  6. Point-based GSM Positioning Methods • TOA (200m - 10km) • E-OTD (50m - 100m) • AOA (>>150m) • A-GPS (10m - 50m)

  7. Motivation • Advanced positioning methods require: • extra cost to existing network / synchronization between base stations • special hardware to end users • telco-dependent • Not all LBSs need very accurate location information • GSM cell information (e.g. cell ID) is available in ordinary GSM handset • Symbian phone offers programming capability for general developers • Location estimation by GSM cell ID is adopted in our project

  8. Overview of GSM Cell ID Location Estimation • Each base station has unique location ID and cell ID • Main idea: each base station can somehow provide certain ‘information’ about a particular location • Advantages: • simple implementation, only current registered cell is required • applicable on ordinary GSM phone • without any support from telco Location: [50] Cell ID: [4] Location: [50] Cell ID: [2] Location: [50] Cell ID: [3] Location: [50] Cell ID: [1]

  9. GSM Cell Change Event • Received signal strength from current registered cell is weaker than another, so cell change occurs • Consequences: • More information provided • More reliable in detecting boundary

  10. Location-based Services in 2D Space • Initiatives • To locate the approximate location of a mobile phone using a program that run on Symbian OS • Principle • Determining GSM cells coverage and their distribution • Plot a cell ID-to-location map • Locate current position of a mobile device

  11. Data Collection Method • Collected location ID and cell ID pairs for two telcos in the CU campus. • Data Collection method: • Static Method for SmarTone • Cell Change Method for Peoples

  12. Principle of the Two Data Collection Methods • Static Method • Wait for a sufficiently long period of time at a specific point in the 2D map to see the strength and stability of a cell strength. • Determine the location ID and cell ID of that specific location after observing for a period of time

  13. Principle of the Two Data Collection Methods • Cell Change Method • Walk around the campus and find the “boundaries” of different cells • When cell change occurs we note down the change and try to find out the boundaries of the cells : location where cell change event is detected Cell boundaries Cell C B->C A->B Cell A Cell B

  14. Advantages and Disadvantages of the Two Methods

  15. Experimental Results For Peoples

  16. Experimental Results

  17. Experimental Results For SmarTone

  18. Experimental Results

  19. Conclusion of the Experiment • Potential difficulties in 2D Space • ID-to-location map drawn not accurate enough • Cannot locate the location of a mobile device to an acceptable accuracy owing to the large size of cells • Hierarchy of cells make it even harder to locate our current position

  20. Location: [50] Cell ID: [2] Location: [50] Cell ID: [4] Location: [50] Cell ID: [3] Location: [50] Cell ID: [1] Cell ID: [1->2] Cell ID: [2->3] Using GSM Cell IDs in 1D Space • A set of multiple cell change events can indicate a path

  21. Problem of Using GSM Cell IDs in 1D Space • The mapping of cell change event set and path is one-to-many • Apply this method on fixed path

  22. Cell ID changes here Station 1 Station 2 MTR Traveller for Stations in Subway • Apply on traffic route • MTR Traveller – detect station arrival • Initial observation: • Between two stations in subway, there is exactly one cell change • This event can tell user that he / she is going from one station to another station

  23. Station 2 Station 1 Transition Pairs: [S1, S2, O, B], [S1, S2, B, P], [S1, S2, P, G] Station Cells: [S1, O], [S1, B] MTR Traveller for Stations in Open Area • KCR stations in open area • Many cells are involved in between two stations • A station platform may also be covered by multiple cells • Group the cells into ‘station cells’ (pure cell ID) and ‘transition pairs’ (cell changes)

  24. Station 2 Station 1 Transition Pairs: [S1, S2, O, B], [S1, S2, B, P], [S1, S2, P, G] Station Cells: [S1, O], [S1, B] Operation of MTR Traveller • Transition pair => on the way between S1 and S2 • Station cell => in the station platform Cell ID: O [S1, O] => in Station 1 Cell ID: OB [S1, B] => in Station 1 Cell ID: BP [S1, S2, B, P] => on the way of S1S2 Cell ID: PG [S1, S2, P, G] => on the way of S1S2

  25. MTR Cell ID Data • Peoples

  26. MTR Cell ID Data • SmarTone

  27. MTR Cell ID Data • Sunday

  28. KCR Cell ID Data • Peoples

  29. KCR Cell ID Data • SmarTone

  30. Estimating the Accuracy of Proposed Method • Record the time difference at which the cell change occurs and at the moment that the train actually arrives the destination station • Convert the error range in time to distance by assuming constant velocity in that range • Result: 30m - 300m, comparable to E-OTD

  31. Demonstration • Videos in actual stations

  32. GSM Cell Change Method(Boundary / Line Based) GSM Cell Change Method in 1D Path(Point Based) Pure GSM Cell Information Location Estimation(Region Based) Detect registered cell change occurred at cell boundary Concentrate on specific cell changes (intersections between the path and the boundary) Evolution of Our Positioning Methods

  33. Automatic Cell Data Collection • Collection of cell data was done manually in the past • Automatic cell data collection tool is required for regular update • Cell Snap

  34. Contribution of Work • Enhancing pure cell ID location estimation by considering cell change events • MTR Traveller provides different application opportunities, such as: • Notification • Information providing • Cell Snap allows automatic cell data collection

  35. Future Work • Improvement on MTR Traveller • Personalization • Informative • User interface • Distributed intelligence (SMS / GPRS) • Generic middleware / library for developers • Other applications • Bus / tram route • Detection of car speed detectors

  36. Conclusion • GSM cell provides location-related information, but not accurate and reliable enough • Those information can be obtained through Symbian phone • The method was enhanced by using cell change events • Difficulties were encountered in 2D space • The proposed method was also applied to 1D path: MTR Traveller • Automatic cell data collection by Cell Snap

  37. Q&A Section • Thank you very much!