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No Need to War-Drive Unsupervised Indoor Localization. He Wang, Souvik Sen , Ahmed Elgohary , Moustafa Farid , Moustafa Youssef, Romit Roy Choudhury - twohsien 2012.6.25. Outline. Introduction Architecture and Intuition Design Details Evaluation Discussion and Conclusion.
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No Need to War-DriveUnsupervised Indoor Localization He Wang, SouvikSen, Ahmed Elgohary, MoustafaFarid, Moustafa Youssef, Romit Roy Choudhury -twohsien 2012.6.25
Outline • Introduction • Architecture and Intuition • Design Details • Evaluation • Discussion and Conclusion
Introduction • Indoor localization is still not in the mainstream • Accuracy • Calibration overhead • Simultaneously harness sensor-based dead-reckoning and environment sensing for localization
Outline • Introduction • Architecture and Intuition • Design Details • Evaluation • Discussion and Conclusion
Architecture and Intuition • Seed Landmarks (SLMs) • Certain structures in the building that force users to behave in predictable waysstairs, elevators, entrances, escalators. • Dead Reckoning • Accelerometer, Compass, gyro • The error gets reset whenever use crosses any of the landmarks • Organic Landmarks (OLMs) • Cannot be known a priori, and will vary across different buildings
Dead-Reckoning Accuracy Mean error 11.7m Mean error 1.2m
Landmark Density • WiFi Landmarks • 8 and 5 in two floor of engineering building, each of area less than 4m2 • Magnetic/Accelerometer Landmarks • 6 and 8 for each floor
Computing landmark locations • Combine all dead-reckoned estimates of a given landmark • Errors are random and independent
Outline • Introduction • Architecture and Intuition • Design Details • Evaluation • Discussion and Conclusion
Seed landmarks • Define sensor patterns that are global across all buildings Acc not stable Acc stable
Dead reckoning • Displacement from accelerometer • Step count * Step size • Step size: counting the number of steps for a known displacement
Dead reckoning • Relative angular velocity • Juxtaposes the gyroscope and compass
Organic landmarks • Distinct patterns • K-means clustering algorithm • Similarity threshold • Small area – 4m2
Organic landmarks • WiFi Landmarks • MAC addresses, RSSI • Similarityfi(a): RSSI of AP a overheard at liA: set of AP heard at l1 and l2 • Magnetic and Inertial Sensor Landmarks • Bending coefficient
Outline • Introduction • Architecture and Intuition • Design Details • Evaluation • Discussion and Conclusion
Experiment settings • Google NexusS phones • 3 different users in 3 different university buildins • Computer 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 accuracy
SLM Detection Performance • Trace from 2 malls in Egypt
Detecting organic landmarks • Number of landmarks detected inside different buildings
Detecting organic landmarks • Number of landmarks and accuracy increase over time
Landmark signature matching • Tradeoff between distinct signature and matching accuracy
Outline • Introduction • Architecture and Intuition • Design Details • Evaluation • Discussion and Conclusion
Discussion and Conclusion • Use the information of landmarks to recalibrate user’s location. • Median location errors is 1.69m Disadvantages: • Device limited • Energy