No Need to War-Drive Unsupervised Indoor Localization

No Need to War-DriveUnsupervised Indoor LocalizationHe Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, Romit Roy Choudhury

-twohsien 2012.6.25OutlineIntroductionArchitecture and IntuitionDesign DetailsEvaluationDiscussion and ConclusionIntroductionIndoor localization is still not in the mainstreamAccuracyCalibration overhead

Simultaneously harness sensor-based dead-reckoning and environment sensing for localizationOutlineIntroductionArchitecture and IntuitionDesign DetailsEvaluationDiscussion and ConclusionArchitecture and IntuitionSeed Landmarks (SLMs)Certain structures in the building that force users to behave in predictable waysstairs, elevators, entrances, escalators.Dead ReckoningAccelerometer, Compass, gyroThe error gets reset whenever use crosses any of the landmarksOrganic Landmarks (OLMs)Cannot be known a priori, and will vary across different buildings

UnLoc Architecture

Dead-Reckoning Accuracy

Mean error 11.7mMean error 1.2mLandmark DensityWiFi Landmarks8 and 5 in two floor of engineering building, each of area less than 4m2Magnetic/Accelerometer Landmarks6 and 8 for each floor

Computing landmark locationsCombine all dead-reckoned estimates of a given landmarkErrors are random and independent

OutlineIntroductionArchitecture and IntuitionDesign DetailsEvaluationDiscussion and ConclusionSeed landmarksDefine sensor patterns that are global across all buildings

Acc stableAcc not stableDead reckoningDisplacement from accelerometer

Step count * Step sizeStep size: counting the number of steps for a known displacement

Dead reckoningRelative angular velocityJuxtaposes the gyroscope and compass

Organic landmarksDistinct patterns K-means clustering algorithmSimilarity threshold

Small area 4m2Organic landmarksWiFi LandmarksMAC addresses, RSSISimilarity

fi(a): RSSI of AP a overheard at liA: set of AP heard at l1 and l2

Magnetic and Inertial Sensor LandmarksBending coefficient

OutlineIntroductionArchitecture and IntuitionDesign DetailsEvaluationDiscussion and ConclusionExperiment settingsGoogle NexusS phones3 different users in 3 different university buildinsComputer science(1750m2), Engineering(3000m2), North gate shopping mall(4000m2)Every user walked arbitrarily for 1.5 hours

Questions:How many landmarks are detected in different buildings?Are they well scattered?Do real users encounter these landmarks?Localization accuracySLM Detection PerformanceTrace from 2 malls in Egypt

Detecting organic landmarksNumber of landmarks detected inside different buildings

Detecting organic landmarksNumber of landmarks and accuracy increase over time

Landmark signature matchingTradeoff between distinct signature and matching accuracy

Localization performance

OutlineIntroductionArchitecture and IntuitionDesign DetailsEvaluationDiscussion and ConclusionDiscussion and ConclusionUse the information of landmarks to recalibrate users location.Median location errors is 1.69m

Disadvantages:Device limitedEnergy