LYU0401 Location-Based Multimedia Mobile Service Clarence Fung Tilen Ma Supervisor: Professor...
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Transcript of LYU0401 Location-Based Multimedia Mobile Service Clarence Fung Tilen Ma Supervisor: Professor...
LYU0401 Location-Based LYU0401 Location-Based Multimedia Mobile Multimedia Mobile
Service Service Clarence FungClarence Fung
Tilen MaTilen MaSupervisor: Professor Michael LyuSupervisor: Professor Michael Lyu
Marker: Professor Alan LiewMarker: Professor Alan Liew
OutlineOutline
IntroductionIntroduction ObjectiveObjective Location-Based ServiceLocation-Based Service Current Localization MethodsCurrent Localization Methods Experimental StudyExperimental Study Wi-Fi Location SystemWi-Fi Location System Future WorkFuture Work Conclusion Conclusion
IntroductionIntroduction
In this semester, we mainly focus on tIn this semester, we mainly focus on the problem of localizationhe problem of localization
We have chosen the 1st floor of the Ho We have chosen the 1st floor of the Ho Sin-Hang Engineering Building to studSin-Hang Engineering Building to study the problem of localizationy the problem of localization
Our goal is to locate a person when he/Our goal is to locate a person when he/she is walking around on the floor she is walking around on the floor
ObjectiveObjective
To meet the need for Location-Based To meet the need for Location-Based ServiceService
To find out if Wireless LAN provide To find out if Wireless LAN provide enough information for localization in enough information for localization in 2D space2D space
Study on different localization Study on different localization algorithmsalgorithms
Develop an application in a mobile Develop an application in a mobile devicedevice
Location-Based ServiceLocation-Based Service Localization is necessary for many higher level sensLocalization is necessary for many higher level sens
or network functions such as tracking, monitoring aor network functions such as tracking, monitoring and geometric-based routing nd geometric-based routing
Three categories: Three categories:
Global location systemsGlobal location systems Wide-area location systems Wide-area location systems Indoor location systemsIndoor location systems
Systems in indoor environmentSystems in indoor environment Infrared (IR)Infrared (IR) UltrasoundUltrasound Radio signal Radio signal
Wireless LAN (WLAN)-Wireless LAN (WLAN)-Based Positioning SystemBased Positioning System
Advantages over all other systemsAdvantages over all other systems EconomicalEconomical
WLAN network usually exists already as part of tWLAN network usually exists already as part of the communications infrastructurehe communications infrastructure
Covers a large area Covers a large area Work in a large building or even across many buiWork in a large building or even across many bui
ldings. ldings. Stable systemStable system
Video- or IR-based location systems are subject tVideo- or IR-based location systems are subject to restrictions, such as line-of-sight limitationso restrictions, such as line-of-sight limitations
Current Localization Current Localization MethodsMethods
Point-based approachPoint-based approach goal is to return a single point for the mobile goal is to return a single point for the mobile
objectobject E.g. Simple Distance MatchingE.g. Simple Distance Matching
Area-based approachArea-based approach goal is to return the possible locations of the goal is to return the possible locations of the
mobile object as an area rather than a single mobile object as an area rather than a single pointpoint
E.g. Simple-Point Matching, Area-Based E.g. Simple-Point Matching, Area-Based ProbabilityProbability
Area-Based Probability Area-Based Probability (ABP)(ABP)
Advantages:Advantages: Presents the user an understanding Presents the user an understanding
of the system in a more natural and of the system in a more natural and intuitive mannerintuitive manner
High accuracyHigh accuracy More mathematical approachMore mathematical approach
Steps in using ABPSteps 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
Applying Area-based Applying Area-based ApproachApproach
Some Terms and Some Terms and DefinitionsDefinitions
n Access Pointsn Access Points APAP11, AP, AP22, …, AP, …, APnn
Training set TTraining set T00 Offline measured signal strengths at differeOffline measured signal strengths at differe
nt locations an algorithm usesnt locations an algorithm uses Consists of a set of fingerprints (SConsists of a set of fingerprints (Sii) at m diff) at m diff
erent areas Aerent areas Aii
TT0 0 = ( A= ( Aii, S, Sii ), i = 1 … m ), i = 1 … m
Some Terms and Some Terms and DefinitionsDefinitions
Fingerprints SFingerprints Sii
Set of n signal strengths at ASet of n signal strengths at Aii, one per eac, one per each access pointh access point
SSi i = (s= (si1i1, …, s, …, sinin), where s), where sijij is the expected av is the expected average signal strength from APerage signal strength from APjj
Generating Training SetGenerating Training Set
In one particular AIn one particular Aii, we read a series of , we read a series of signal strengths (ssignal strengths (sijkijk ) for a particular A ) for a particular APPj j with a constant time between samplwith a constant time between sampleses k = 1… ok = 1… oij ij ,where o,where oijij is the number of samp is the number of samp
les from APles from APj j at Aat Aii We estimate sWe estimate sij ij by averaging the series, by averaging the series,
{s{sij1ij1, s, sij2ij2…, s…, sijoijo } }
Generating Training SetGenerating Training Set
We do the same for all n APs, so we haWe do the same for all n APs, so we have the fingerprints at Ave the fingerprints at Aii, , SSi i = (s= (si1i1, …, s, …, sinin))
We do the same for all m areas, so we We do the same for all m areas, so we have the training set have the training set TT0 0 = ( A= ( Aii, S, Sii ), i = 1… m ), i = 1… m
Collecting SignalsCollecting Signals At each area chosen, we measure the signal At each area chosen, we measure the signal
strength from the access points for 1 minute strength from the access points for 1 minute Position 1 2 3 4 5 6 7 8 9 10 11 12
AP MAC address Signal 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 -89 -90
00:0f:34:bb:df:20 -89 -90 -82 -88 -88
00:0c:ce:21:1b:9d -87
00:0c:85:35:33:d2 -88 -88
00:11:20:93:63:90 -89 -88
00:0c:85:35:33:d4 -87
00:04:76:a7:ab:a3 -90
Data ProcessingData Processing
We have chosen 7 out of 13 access We have chosen 7 out of 13 access pointspoints least contribution to localization least contribution to localization shorten computation time shorten computation time
For missing signal strengths, we For missing signal strengths, we input -92 dBm as entryinput -92 dBm as entry
Training SetTraining SetPosition 1 2 3 4 5 6 7 8 9 10 11 12
AP MAC address Signal 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 -92 -82 -78 -82 -59 -78 -73 -83 -85 -82 -92
00:11:93:3d:6f:c0 -92 -92 -92 -90 -85 -86 -89 -88 -92 -92 -92 -92
00:0f:34:bb:df:20 -92 -92 -92 -89 -90 -82 -88 -88 -92 -92 -92 -92
Getting Testing SetGetting Testing Set
The object to be localized collects a set The object to be localized collects a set of received signal strengths (RSS) wheof received signal strengths (RSS) when it is at certain locationn it is at certain location
A testing set (SA testing set (Stt) is created similar to th) is created similar to the fingerprints in the training sete fingerprints in the training set
It is a set of average signal strengths frIt is a set of average signal strengths from APs, Som APs, St t = (s= (st1t1, …, s, …, stntn))
RSSRSS
AP MAC address Signal 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
Testing SetTesting Set
AP MAC address Signal 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
Applying ABPApplying ABP
Goal: return the area with a highest prGoal: return the area with a highest probability obability
Approach: compute the likelihood of tApproach: compute the likelihood of the testing set (She testing set (Stt) that matches the fing) that matches the fingerprint for each area (Serprint for each area (Sii) )
Applying ABPApplying ABP
Assumptions:Assumptions: Signal received from different APs are Signal received from different APs are
independent independent
For each APFor each APjj, j = 1…n, the sequence of , j = 1…n, the sequence of RSS sRSS sijkijk, k = 1… o, k = 1… oijij, at each A, at each Ai i in Tin Too is mo is modeled as a Gaussian distributiondeled as a Gaussian distribution
Applying Bayes’ ruleApplying Bayes’ rule We compute the probability of being aWe compute the probability of being a
t different areas At different areas Aii, on given the testin, on given the testing set Sg set St t
P(AP(Ai i |S|Stt) = P(S) = P(St t |A|Aii)* P(A)* P(Aii)/ P(S)/ P(Stt) (1)) (1) P(SP(Stt) is a constant) is a constant Assume the object is equally likely to be at any Assume the object is equally likely to be at any
location. P(Alocation. P(Aii) is a constant) is a constant
P(AP(Ai i |S|Stt) = c*P(S) = c*P(St t |A|Aii)) (2) (2)
Area Based ProbabilityArea Based Probability We compute P(SWe compute P(St t |A|Aii) for every area A) for every area Aii ,i=1…m, using ,i=1…m, using
the Gaussian assumptionthe Gaussian assumption
Finding Probability DensityFinding 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(AMax{P(Ai i |S|Stt) } = Max{c*P(S) } = Max{c*P(St t |A|Aii) } ) } = Max{P(S= Max{P(St t |A|Aii) }) }
Return the area AReturn the area Aii with top probability with top probability
Gaussian Distribution Gaussian Distribution In our application, In our application,
we can take μ as the we can take μ as the expected average siexpected average signal strengths for thgnal strengths for the access point to be e access point to be calculated calculated
we take σ as 8.5we take σ as 8.5
Integral of Normal Integral of Normal FunctionFunction
Find probability Find probability by integrationby integration
Take interval as Take interval as 1 1
Error function erf(x)Error function erf(x) Express Integral of Express Integral of
Normal Function in Normal Function in terms of erfterms of erf
Approximate value Approximate value of erf by a seriesof erf by a series
Choose iteration of Choose iteration of 5050
Experimental StudyExperimental Study Area 5 is near Area 5 is near
the North-West the North-West stairway on the stairway on the 1st floor1st floor
deep purple line deep purple line is on the top of is on the top of other linesother lines
Localization Localization system returns system returns the correct the correct resultresult
ABP Localization Graph at Area 5
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 5 9 13 17 21 25 29 33 37 41Sample No
Prob
ability
Probability at A1
Probability at A2
Probability at A3
Probability at A4
Probability at A5
Probability at A6
Probability at A7
Probability at A8
Probability at A9
Probability at A10
Probability at A11
Probability at A12
Accuracy of Localization Accuracy of Localization SystemSystem
Default Default sample size sample size of testing of testing set = 4set = 4
80 testing 80 testing sets for each sets for each of the 12 of the 12 locations locations
8580
75 74
8575 71
87.5
52.5
70
87.5
6975.94
0
10
20
30
40
50
60
70
80
90
Percentage
1 2 3 4 5 6 7 8 9 10 11 12 Overall
Area
Accuracy at Different Locations
Accuracy of Localization Accuracy of Localization SystemSystem
1 5 9 13 17 2125
76 87.6 93 96 98 99 99.5
0102030405060708090100
Percentage
Sample No
System Overall Accuracy
Other Factors affecting Other Factors affecting Accuracy Accuracy
Property of signalsProperty of signals The strength of signals fluctuatesThe strength of signals fluctuates
Hardware failureHardware failure access points fails to give out signals or access points fails to give out signals or
give out signals at unusual strengthgive 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 doorsopening the doors
Orientation in collecting signal Orientation in collecting signal
Wi-Fi Location System Wi-Fi Location System (WLS) (WLS)
Development Tool for Location-Based SyDevelopment Tool for Location-Based Systemstem
Simplify development stepsSimplify development steps Increase the efficiency and productivity Increase the efficiency and productivity It divides into 3 componentsIt 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)
Wireless LAN Wireless LAN TerminologyTerminology
Media Access Control address (MAC Media Access Control address (MAC Address)Address) 48 bits long48 bits long unique hardware address unique hardware address e.g. 00:50:FC:2A:A9:C9e.g. 00:50:FC:2A:A9:C9
Service set identifier (SSID)Service set identifier (SSID) 32 character32 character Wireless LAN identifierWireless LAN identifier
Receive Signal Strength Indicator (RSSI) Receive Signal Strength Indicator (RSSI) signal strength signal strength unit is in dBm unit is in dBm
OverviewOverview
Platform:Platform: Window CEWindow CE Window XP, 2000Window XP, 2000
Technology:Technology: IEEE 802.11bIEEE 802.11b
ToolsTools Embedded Visual C++ 4.0Embedded Visual C++ 4.0 Visual Studio .NET 2003Visual Studio .NET 2003
Tradition Development Tradition Development Procedure (TDP)Procedure (TDP)
The followings in the Tradition The followings in the Tradition Development ProcedureDevelopment Procedure
Studying the technology
Software Design
Algorithm design
Final System
1-2 week
1-2 week
2-3 week
Wi-Fi Location System Wi-Fi Location System Development Procedure Development Procedure
(WLP) (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
Comparison between TDP Comparison between TDP and WLPand WLP
Using WLP, we can develop Using WLP, we can develop Location-Based System in a short Location-Based System in a short time.time.
This work can be done by non-This work can be done by non-professionalsprofessionals
It simplifies Development StepsIt simplifies Development Steps
Wi-Fi Signal Scanner Wi-Fi Signal Scanner
To collect the To collect the signal strength signal strength received from received from access pointsaccess points
Collected DataCollected Data
Strength Signal
Number of Received
Signal
Total of Received
Signal
Mean of Received
Signal
Wi-Fi Data Processor Wi-Fi Data Processor
To process collected dataTo process collected data
Position Region
Access Point
Region
Setting and Information
Region
Wi-Fi Data ProcessorWi-Fi Data Processor
Two main steps in WDPTwo main steps in WDP Filter out useless dataFilter out useless data Set parameters at each positionSet parameters at each position
DataData NameName Point at Map PicturePoint at Map Picture
Wi-Fi Location Detector Wi-Fi Location Detector
Three functions in WLDThree 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 To show the detected position name and
corresponding position at the Map Picture corresponding position at the Map Picture To show calculated probabilityTo show calculated probability
Three modes in WLD Three modes in WLD Data ModeData Mode Map ModeMap Mode Probability Mode Probability Mode
Data ModeData Mode
To show the sample dataTo show the sample data
Map ModeMap Mode
Position
Name
Probability ModeProbability Mode
To show calculated probability at each To show calculated probability at each positionposition
ConclusionConclusion
We are success in applying Area-Based We are success in applying Area-Based ProbabilityProbability
We have done experiments on accuracy We have done experiments on accuracy of algorithmof algorithm
We have implemented Location-Based We have implemented Location-Based Development Tool—Wi-Fi Location Development Tool—Wi-Fi Location SystemSystem
Based on our knowledge and developed Based on our knowledge and developed tools in localization, we are able to tools in localization, we are able to further develop a location-based service further develop a location-based service
Future WorkFuture Work
Ho Sin-Hang Engineering Building Ho Sin-Hang Engineering Building Tour Guide ServiceTour Guide Service
Multimedia Application with video Multimedia Application with video streaming streaming
Improvement in Localization Improvement in Localization Algorithm Algorithm
Increase the Accuracy in Localization Increase the Accuracy in Localization Research on 3D localization Research on 3D localization
algorithm in an building algorithm in an building
Q&AQ&A
DEMODEMO
THE ENDTHE END