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

2. Outline Introduction Introduction Objective Objective Location-Based Service Location-Based Service Current Localization Methods Current Localization Methods Experimental Study Experimental Study Wi-Fi Location System Wi-Fi Location System Future Work Future Work Conclusion Conclusion

3. Introduction In this semester, we mainly focus on the problem of localization In this semester, we mainly focus on the problem of localization We have chosen the 1st floor of the Ho Sin- Hang Engineering Building to study the problem of localization We have chosen the 1st floor of the Ho Sin- Hang Engineering Building to study the problem of localization Our goal is to locate a person when he/she is walking around on the floor Our goal is to locate a person when he/she is walking around on the floor

4. Objective To meet the need for Location-Based Service To meet the need for Location-Based Service To find out if Wireless LAN provide enough information for localization in 2D space To find out if Wireless LAN provide enough information for localization in 2D space Study on different localization algorithms Study on different localization algorithms Develop an application in a mobile device Develop an application in a mobile device

5. Location-Based Service Localization is necessary for many higher level sensor network functions such as tracking, monitoring and geometric-based routing Localization is necessary for many higher level sensor network functions such as tracking, monitoring and geometric-based routing Three categories: Three categories: Global location systems Global location systems Wide-area location systems Wide-area location systems Indoor location systems Indoor location systems Systems in indoor environment Systems in indoor environment Infrared (IR) Infrared (IR) Ultrasound Ultrasound Radio signal Radio signal

6. Wireless LAN (WLAN)-Based Positioning System Advantages over all other systems Economical Economical WLAN network usually exists already as part of the communications infrastructure WLAN network usually exists already as part of the communications infrastructure Covers a large area Covers a large area Work in a large building or even across many buildings. Work in a large building or even across many buildings. Stable system Stable system Video- or IR-based location systems are subject to restrictions, such as line-of-sight limitations Video- or IR-based location systems are subject to restrictions, such as line-of-sight limitations

7. Current Localization Methods Point-based approach Point-based approach goal is to return a single point for the mobile object goal is to return a single point for the mobile object E.g. Simple Distance Matching E.g. Simple Distance Matching Area-based approach Area-based approach goal is to return the possible locations of the mobile object as an area rather than a single point goal is to return the possible locations of the mobile object as an area rather than a single point E.g. Simple-Point Matching, Area-Based Probability E.g. Simple-Point Matching, Area-Based Probability

8. Area-Based Probability (ABP) Advantages: Presents the user an understanding of the system in a more natural and intuitive manner Presents the user an understanding of the system in a more natural and intuitive manner High accuracy High accuracy More mathematical approach More mathematical approach

9. Steps in using ABP Decide the Areas Measure Signals at Different Areas Create a Training Set Measure Signals at Current Position Create a Testing Set Find out the Probability of Being at Different Areas Calculate Probability Density Return the Area with Highest Probability

10. Applying Area-based Approach

11. Some Terms and Definitions n Access Points n Access Points AP 1, AP 2, …, AP n AP 1, AP 2, …, AP n Training set T 0 Training set T 0 Offline measured signal strengths at different locations an algorithm uses Offline measured signal strengths at different locations an algorithm uses Consists of a set of fingerprints (S i ) at m different areas A i Consists of a set of fingerprints (S i ) at m different areas A i T 0 = ( A i, S i ), i = 1 … m T 0 = ( A i, S i ), i = 1 … m

12. Some Terms and Definitions Fingerprints S i Fingerprints S i Set of n signal strengths at A i, one per each access point Set of n signal strengths at A i, one per each access point S i = (s i1, …, s in ), where s ij is the expected average signal strength from AP j S i = (s i1, …, s in ), where s ij is the expected average signal strength from AP j

13. Generating Training Set In one particular 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 A i, we read a series of signal strengths (s ijk ) for a particular AP j with a constant time between samples k = 1 … o ij,where o ij is the number of samples from AP j at A i k = 1 … o ij,where o ij is the number of samples from AP j at A i 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 }

14. Generating Training Set We do the same for all n APs, so we have the fingerprints at A i, We do the same for all n APs, so we have the fingerprints at A i, S i = (s i1, …, s in ) S i = (s i1, …, s in ) We do the same for all m areas, so we have the training set We do the same for all m areas, so we have the training set T 0 = ( A i, S i ), i = 1 … m T 0 = ( A i, S i ), i = 1 … m

15. Collecting Signals At each area chosen, we measure the signal strength from the access points for 1 minute At each area chosen, we measure the signal strength from the access points for 1 minute 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-82-78-82-59-78-73-83-85-82 00:11:93:3d:6f:c0 -90-85-86-89-88 00:11:20:93:65:c0 -89 -90 00:0f:34:bb:df:20 -89-90-82-88 00:0c:ce:21:1b:9d -87 00:0c:85:35:33:d2 -88 00:11:20:93:63:90 -89-88 00:0c:85:35:33:d4 -87 00:04:76:a7:ab:a3-90

16. Data Processing We have chosen 7 out of 13 access points We have chosen 7 out of 13 access points least contribution to localization least contribution to localization shorten computation time shorten computation time For missing signal strengths, we input -92 dBm as entry For missing signal strengths, we input -92 dBm as entry

17. Training Set 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

18. Getting Testing Set The object to be localized collects a set of received signal strengths (RSS) when it is at certain location The object to be localized collects a set of received signal strengths (RSS) when it is at certain location A testing set (S t ) is created similar to the fingerprints in the training set A testing set (S t ) is created similar to the fingerprints in the training set It is a set of average signal strengths from APs, S t = (s t1, …, s tn ) It is a set of average signal strengths from APs, S t = (s t1, …, s tn )

19. RSS 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

20. Testing Set 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

21. Applying ABP Goal: return the area with a highest probability Goal: return the area with a highest probability Approach: compute the likelihood of the testing set (S t ) that matches the fingerprint for each area (S i ) Approach: compute the likelihood of the testing set (S t ) that matches the fingerprint for each area (S i )

22. Applying ABP Assumptions: Signal received from different APs are independent Signal received from different APs are independent For each AP j, j = 1 … n, the sequence of RSS s ijk, k = 1 … o ij, at each A i in T o is modeled as a Gaussian distribution For each AP j, j = 1 … n, the sequence of RSS s ijk, k = 1 … o ij, at each A i in T o is modeled as a Gaussian distribution

23. 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)

24. Area Based Probability We compute P(S t |A i ) for every area A i,i=1 … m, using the Gaussian assumption We compute P(S t |A i ) for every area A i,i=1 … m, using the Gaussian assumption Finding Probability Density Finding Probability Density the object must be at one of the 12 areas the object must be at one of the 12 areas ΣP(Ai | St) =1 for all i ΣP(Ai | St) =1 for all i Max{P(A i |S t ) } = Max{c*P(S t |A i ) } Max{P(A i |S t ) } = Max{c*P(S t |A i ) } = Max{P(S t |A i ) } = Max{P(S t |A i ) } Return the area A i with top probability Return the area A i with top probability

25. Gaussian Distribution In our application, we can take μ as the expected average signal strengths for the access point to be calculated we take σ as 8.5

26. Integral of Normal Function Find probability by integration Find probability by integration Take interval as 1 Take interval as 1

27. 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 Choose iteration of 50 Choose iteration of 50

28. Experimental Study Area 5 is near the North-West stairway on the 1st floor Area 5 is near the North-West stairway on the 1st floor deep purple line is on the top of other lines deep purple line is on the top of other lines Localization system returns the correct result Localization system returns the correct result

29. Accuracy of Localization System 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

30. Accuracy of Localization System

31. Other Factors affecting Accuracy Property of signals Property of signals The strength of signals fluctuates The strength of signals fluctuates Hardware failure Hardware failure access points fails to give out signals or give out signals at unusual strength access points fails to give out signals or give out signals at unusual strength Change in environment Change in environment addition access points on the floor addition access points on the floor opening the doors opening the doors Orientation in collecting signal Orientation in collecting signal

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. 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

34. Overview 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

35. Tradition Development Procedure (TDP) The followings in the Tradition Development Procedure The followings in the Tradition Development Procedure Studying the technology Software Design Algorithm design Final System 1-2 week 2-3 week

36. Wi-Fi Location System Development Procedure (WLP) Collecting Data Processing Data Deploying and Test System Several hours 1 day Final System Several days Using Wi-Fi Signal Scanner Using Wi-Fi Data Processor Using Wi-Fi Location Detector

37. Comparison between TDP and WLP Using WLP, we can develop Location-Based System in a short time. Using WLP, we can develop Location-Based System in a short time. This work can be done by non-professionals This work can be done by non-professionals It simplifies Development Steps It simplifies Development Steps

38. Wi-Fi Signal Scanner To collect the signal strength received from access points To collect the signal strength received from access points

39. Collected Data Strength Signal Number of Received Signal Total of Received Signal Mean of Received Signal

40. Wi-Fi Data Processor To process collected data To process collected data Position Region Access Point Region Setting and Information Region

41. Wi-Fi Data Processor Two main steps in WDP Two main steps in WDP Filter out useless data Filter out useless data Set parameters at each position Set parameters at each position Data Data Name Name Point at Map Picture Point at Map Picture

42. Wi-Fi Location Detector Three functions in WLD Three functions in WLD To detect the location in the target place To detect the location in the target place To show the detected position name and corresponding position at the Map Picture To show the detected position name and corresponding position at the Map Picture To show calculated probability To show calculated probability Three modes in WLD Three modes in WLD Data Mode Data Mode Map Mode Map Mode Probability Mode Probability Mode

43. Data Mode To show the sample data To show the sample data

44. Map Mode Position Name

45. Probability Mode To show calculated probability at each position To show calculated probability at each position

46. Conclusion We are success in applying Area-Based Probability We are success in applying Area-Based Probability We have done experiments on accuracy of algorithm We have done experiments on accuracy of algorithm We have implemented Location-Based Development Tool — Wi-Fi Location System We have implemented Location-Based Development Tool — Wi-Fi Location System Based on our knowledge and developed tools in localization, we are able to further develop a location-based service Based on our knowledge and developed tools in localization, we are able to further develop a location-based service

47. Future Work Ho Sin-Hang Engineering Building Tour Guide Service Ho Sin-Hang Engineering Building Tour Guide Service Multimedia Application with video streaming Multimedia Application with video streaming Improvement in Localization Algorithm Improvement in Localization Algorithm Increase the Accuracy in Localization Increase the Accuracy in Localization Research on 3D localization algorithm in an building Research on 3D localization algorithm in an building

48. Q&A

49. DEMO

50. THE END