1. 1 LYU0401 Location-Based Multimedia Mobile Service Clarence Fung Tilen Ma Supervisor: Professor Michael Lyu Marker: Professor Alan Liew

2. 2 Outline Objective Objective Introduction to Localization Introduction to Localization Area-based Probability Area-based Probability Center of Mass Center of Mass Multimedia Guidance System Multimedia Guidance System Conclusion Conclusion

3. 3 Objective To study different algorithms and techniques in localization To study different algorithms and techniques in localization To find out a suitable localization method for Location-Based Service To find out a suitable localization method for Location-Based Service To develop an application for rapid Location- Based Multimedia Service System Development To develop an application for rapid Location- Based Multimedia Service System Development

4. 4 Main Steps in Localization Decide the Areas Measure Signals at Decided Areas Create a Training Set Measure Signals at Current Position Create a Testing Set Apply Suitable Algorithm Return the most likely Area

5. 5 Decide the Areas

6. 6 Collecting Training Set Data (I) In one particular area A i, we read a series of signal strengths (s ijk ) for a particular AP j with a constant time between samples In one particular area A i, we read a series of signal strengths (s ijk ) for a particular AP j with a constant time between samples We estimate s ij by averaging the series, {s ij1, s ij2 …, s ijo } We estimate s ij by averaging the series, {s ij1, s ij2 …, s ijo }

7. 7 Collecting Training Set Data (II) We read signals of all n APs, so we have the fingerprints at A i We read signals of all n APs, so we have the fingerprints at A i We read signals at all m areas We read signals at all m areas

8. 8 Collecting Testing Set Data collects a set of received signal strengths when it is at certain location collects a set of received signal strengths when it is at certain location similar to the fingerprints in the training set similar to the fingerprints in the training set a set of average signal strengths from APs a set of average signal strengths from APs

9. 9 Data Processing delete some access points that have least contribution to localization delete some access points that have least contribution to localization To shorten computation time To shorten computation time input -92 dBm for missing signal strengths input -92 dBm for missing signal strengths

10. 10 Training Set Example Position123456789101112 AP MAC addressSignal Strength (dBm) 00:02:2d:28:be:9e-70-62-58-67-73-78-83-86-84-81-78-55 00:02:2d:28:be:5d-67-59-60-71-76-79-81-86-81-83-79-52 00:60:1d:1e:43:9b-79-87-85-84-89-80-76-77-66-63-77-90 00:0f:34:f3:60:40-63-69-65-74-76-72-77-84-76-74-66-79 00:02:2d:21:39:1f-92 -82-78-82-59-78-73-83-85-82-92 00:11:93:3d:6f:c0-92 -90-85-86-89-88-92 00:0f:34:bb:df:20-92 -89-90-82-88 -92

11. 11 Testing Set Example AP MAC addressSignal Strength (dBm) 00:02:2d:28:be:9e -71 00:02:2d:28:be:5d -72 00:60:1d:1e:43:9b -89 00:0f:34:f3:60:40 -49 00:02:2d:21:39:1f -92 00:11:93:3d:6f:c0 -92 00:0f:34:bb:df:20 -92

12. 12 Area-Based Probability (ABP) Advantages: Area-based approach Area-based approach More mathematical approach More mathematical approach Higher accuracy Higher accuracyApproach: compute the likelihood of the testing set (S t ) that matches the fingerprint for each area (S i ) compute the likelihood of the testing set (S t ) that matches the fingerprint for each area (S i )

13. 13 Assumptions in ABP Signal received from different APs are independent Signal received from different APs are independent For each AP, the sequence of RSS s ijk, is modeled as a Gaussian distribution For each AP, the sequence of RSS s ijk, is modeled as a Gaussian distribution

14. 14 Applying Bayes ’ rule We compute the probability of being at different areas A i, on given the testing set S t We compute the probability of being at different areas A i, on given the testing set S t P(A i |S t ) = P(S t |A i )* P(A i )/ P(S t ) (1) P(A i |S t ) = P(S t |A i )* P(A i )/ P(S t ) (1) P(S t ) is a constant P(S t ) is a constant Assume the object is equally likely to be at any location. P(A i ) is a constant Assume the object is equally likely to be at any location. P(A i ) is a constant P(A i |S t ) = c*P(S t |A i ) (2) P(A i |S t ) = c*P(S t |A i ) (2)

15. 15 Gaussian Distribution We compute P(S t |A i ) for every area A i,i=1 … m, using the Gaussian assumption Integral of Normal Function taking the interval as 1

16. 16 Error function erf(x) Express Integral of Normal Function in terms of erf Express Integral of Normal Function in terms of erf Approximate value of erf by a series Approximate value of erf by a series

17. 17 Use of probability Discrete Space Estimation Discrete Space Estimation Only fixed number of locations in the training set can be returned Only fixed number of locations in the training set can be returned Eg. Return the area A i with top probability Eg. Return the area A i with top probability Continuous Space Estimation Continuous Space Estimation Continuous range of locations can be returned Continuous range of locations can be returned Return locations may or may not be in the training set Return locations may or may not be in the training set Eg. Finding Center of Mass Eg. Finding Center of Mass

18. 18 Accuracy of Discrete Space Estimation Default sample size of testing set = 4 Default sample size of testing set = 4 80 testing sets for each of the 12 locations 80 testing sets for each of the 12 locations

19. 19 Continuous Space Estimation (CSE) Advantage: Advantage: Return locations may or may not be in the training set Return locations may or may not be in the training set Higher accuracy Higher accuracy Suitable for mobile application Suitable for mobile application Two techniques: Two techniques: Center of Mass Center of Mass Time-Averaging Time-Averaging

20. 20 Center of Mass Assume n locations Assume n locations Treat each location in the training set as an object Treat each location in the training set as an object Each object has a weight equals to its probability density Each object has a weight equals to its probability density Obtain Center of Mass of n objects using their weighted positions Obtain Center of Mass of n objects using their weighted positions

21. 21 Center of Mass Let p(i) be the probability of a location x i, i=1,2 … n Let p(i) be the probability of a location x i, i=1,2 … n Let Y be the set of locations in 2D space and Y(i) is the corresponding position of x i Let Y be the set of locations in 2D space and Y(i) is the corresponding position of x i The Center of Mass is given by: The Center of Mass is given by:

22. 22 Example

23. 23 Experiment on Center of Mass 16 positions for the training set 16 positions for the training set In every location, we use our system to perform positioning for 100 times In every location, we use our system to perform positioning for 100 times Totally we get 1600 records Totally we get 1600 records

24. 24 Experiment Result

25. 25

26. 26 Summary of statistics at all the positions

27. 27

28. 28 Analysis of Experiment Result If the tolerance of error is 2 meters and 3 meters, the accuracy of our system is 85% and 94% respectively If the tolerance of error is 2 meters and 3 meters, the accuracy of our system is 85% and 94% respectively A few estimated points with a large distance error (3m to 5m) A few estimated points with a large distance error (3m to 5m) Inaccuracy in testing set Inaccuracy in testing set Frustration in signal strength Frustration in signal strength

29. 29 Application System In our project, we have implemented two development tools In our project, we have implemented two development tools Wi-Fi Location System (WLS) Wi-Fi Location System (WLS) To develop Location-Based System To develop Location-Based System Multimedia Guidance System (MGS) Multimedia Guidance System (MGS) To develop Location-Based System with multimedia services To develop Location-Based System with multimedia services Developer can develop any Location-Based Multimedia System using our tools Developer can develop any Location-Based Multimedia System using our tools

30. 30 Development Environment Platform: Platform: Window CE Window CE Window XP, 2000 Window XP, 2000 Technology: Technology: IEEE 802.11b IEEE 802.11b Tools Tools Embedded Visual C++ 4.0 Embedded Visual C++ 4.0 Visual Studio.NET 2003 Visual Studio.NET 2003

31. 31 Wireless LAN Terminology Media Access Control address (MAC Address) Media Access Control address (MAC Address) 48 bits long 48 bits long unique hardware address unique hardware address e.g. 00:50:FC:2A:A9:C9 e.g. 00:50:FC:2A:A9:C9 Service set identifier (SSID) Service set identifier (SSID) 32 character 32 character Wireless LAN identifier Wireless LAN identifier Receive Signal Strength Indicator (RSSI) Receive Signal Strength Indicator (RSSI) signal strength signal strength unit is in dBm unit is in dBm

32. 32 Wi-Fi Location System (WLS) Development Tool for Location-Based System Development Tool for Location-Based System Simplify development steps Simplify development steps Increase the efficiency and productivity Increase the efficiency and productivity It divides into 3 components It divides into 3 components Wi-Fi Signal Scanner (WSS) Wi-Fi Signal Scanner (WSS) Wi-Fi Data Processor (WDP) Wi-Fi Data Processor (WDP) Wi-Fi Location Detector (WLD) Wi-Fi Location Detector (WLD)

33. 33 Wi-Fi Location System Wi-Fi Signal Scanner Wi-Fi Signal Scanner To collect the signal strength received from access points To collect the signal strength received from access points Wi-Fi Data Processor Wi-Fi Data Processor To process collected data To process collected data To filter noise data To filter noise data Wi-Fi Location Detector Wi-Fi Location Detector To detect the location in target place To detect the location in target place To show the detected position name and corresponding position at Map Picture To show the detected position name and corresponding position at Map Picture

34. 34 Multimedia Guidance System (MGS) WLS Limitations WLS Limitations only provides Location-Based services only provides Location-Based services does not support any multimedia services does not support any multimedia services Discrete Space Estimations Discrete Space Estimations We create new development tools to support multimedia services We create new development tools to support multimedia services

35. 35 Multimedia Guidance System (MGS) Multimedia Guidance System is a development tool to support multimedia service Multimedia Guidance System is a development tool to support multimedia service It consists of three components It consists of three components Wi-Fi Signal Scanner (WSS) Wi-Fi Signal Scanner (WSS) Multimedia Guidance Processor (MGP) Multimedia Guidance Processor (MGP) Location-Based Multimedia Service System (LBMSS) Location-Based Multimedia Service System (LBMSS)

36. 36 Multimedia Guidance System Deploying Procedure Deploying Procedure Processing Data and generating LBSData Collecting Signal strength data Deploying System with clients and server application

37. 37 Wi-Fi Signal Scanner (WSS) To collect the signal strength received from access points To collect the signal strength received from access points The collected data received from WSS is then processed by MGP The collected data received from WSS is then processed by MGP Same as WSS in WLS Same as WSS in WLS

38. 38 Wi-Fi Signal Scanner MAC AddressSSIDWEPSignal Strength Number of received data Total Number of Signal Strength Mean of Signal Strength

39. 39 Multimedia Guidance Processor (MGP) To process and filter collected signal strength data To process and filter collected signal strength data To set client environment in LBMSS To set client environment in LBMSS Server Address Server Address Availability of Service Availability of Service Position Information Position Information Video Video Picture Picture Position in Map Picture Position in Map Picture

40. 40 Multimedia Guidance Processor Data Processing Procedure Data Processing Procedure Open the MGP Set the project name, server path and map picture path in setting Add/Delete the target Position Set the name, data, video, picture, position in the map picture and point of interest Filter the noise data Setting Lines Generate location-based data Select service for mobile client

41. 41 Multimedia Guidance Processor Position Information Access Point Information Position SettingService SettingInformation Section Environment and Options Setting

42. 42 Location-Based Multimedia Service System (LBMSS) To provide multimedia service for users To provide multimedia service for users It mainly consists of two parts It mainly consists of two parts Client Client Server Server Chat and Management Server Chat and Management Server Paint Server Paint Server Media Server Media Server

43. 43 Overall Architecture

44. 44 Overall Architecture Techniques on Client/Server model in LBMSS Techniques on Client/Server model in LBMSS Socket Programming in.NET Socket Programming in.NET Multithread Model (AsyncCallback, Non-Blocking Function) Multithread Model (AsyncCallback, Non-Blocking Function) Encode object variable into byte stream Encode object variable into byte stream Import Native Code (Visual C++ API) to Managed Code (.NET API) Import Native Code (Visual C++ API) to Managed Code (.NET API) Control other program by CreateProcess() API Control other program by CreateProcess() API

45. 45 Paint Server To provide paint service To provide paint service It has two main functions It has two main functions Store Client ’ s Artwork Store Client ’ s Artwork Print Artwork for Client Print Artwork for Client

46. 46 Chat and Management Server To provide chat service To provide chat service It shows the current position of all clients on the map It shows the current position of all clients on the map

47. 47 Media Server To provide media service To provide media service It is made by existing server architecture (e.g. HTTP, FTP, Streaming Server) It is made by existing server architecture (e.g. HTTP, FTP, Streaming Server)

48. 48 Client To provide multimedia service for users To provide multimedia service for users Client program mainly has 4 services Client program mainly has 4 services Guide Guide Paint Paint Chat Chat Video Video

49. 49 Client Guide Service Guide Service Select Destination Select Destination Guide Line shows on Map Tab Guide Line shows on Map Tab Implement by Shortest Path Algorithm Implement by Shortest Path Algorithm

50. 50 Shortest Path in the Guide Service We define the nodes as the turning points or the ending of the corridors We define the nodes as the turning points or the ending of the corridors

51. 51 add edges connecting two nodes add edges connecting two nodes give a weight which is equal to the distance between the two nodes give a weight which is equal to the distance between the two nodes 1 5 26 3 47 8 81 116 38 76 200 154 43

52. 52 add the source node and the destination node add the source node and the destination node add new edges add new edges Use of Dijkstra ’ s algorithm Use of Dijkstra ’ s algorithm 38 47 C 60 56 D 11090 1 5 26 81 38 76 200 154 43

53. 53 Dijkstra ’ s algorithm 1. Set i=0, S 0 = {u 0 =s}, L(u 0 )=0, and L(v)=infinity for v <> u 0. If |V| = 1 then stop, otherwise go to step 2. 2. For each v in V\S i, replace L(v) by min{L(v), L(u i )+d vu }. If L(v) is replaced, put a label (L(v), u i ) on v. 3. Find a vertex v which minimizes {L(v): v in V\S i }, say u i +1. 4. Let S i +1 = Si cup {u i +1}. 5. Replace i by i+1. If i=|V|-1 then stop, otherwise go to step 2.

54. 54 Client Paint Service Paint Service 1. Get Picture from Media Server 2. Draw on Picture 3. Send Artwork to Paint Server

55. 55 Client Chat Service Chat Service Message can be sent both direction Message can be sent both direction i.e. Client to Server or Server to Client i.e. Client to Server or Server to Client

56. 56 Client Video Service Video Service Play video by clicking “ Play ” Button Play video by clicking “ Play ” Button Video is played by Windows Media Player (WMP) Video is played by Windows Media Player (WMP) WMP is controlled by CreateProcess() API in Embedded Visual C++ WMP is controlled by CreateProcess() API in Embedded Visual C++

57. 57 Comparison between Tradition Method and MGS Method Tradition Method Tradition Method Studying the Technology (1-2 week) Studying the Technology (1-2 week) Software Design (2-3 week) Software Design (2-3 week) Architecture Design (2-3 week) Architecture Design (2-3 week) Algorithm Design (1-2 week) Algorithm Design (1-2 week) MGS Method MGS Method Wi-Fi Signal Scanner (1/2 day) Wi-Fi Signal Scanner (1/2 day) Multimedia Guidance Processor (2-3 days) Multimedia Guidance Processor (2-3 days) Location-Based Multimedia Service System (4-5 days) Location-Based Multimedia Service System (4-5 days)

58. 58 Comparison between Tradition Method and MGS Method Using MGS method, we can develop Location- Based Multimedia Service System in a short time. Using MGS method, we can develop Location- Based Multimedia Service System in a short time. This work can be done by non-professionals This work can be done by non-professionals It simplifies the development steps It simplifies the development steps

59. 59 Conclusion We are successful in achieving the goal of localization We are successful in achieving the goal of localization We have done experiments on accuracy of algorithm We have done experiments on accuracy of algorithm We have implemented two location-based development tools We have implemented two location-based development tools Wi-Fi Location System Wi-Fi Location System Multimedia Guidance System Multimedia Guidance System

60. 60 Q&A

61. 61 The END