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Published byDominic Richards Modified over 9 years ago
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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
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OUTLINE Introduction Architecture and Intuition Design Details Evaluation Discussion and Conclusion
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INTRODUCTION Indoor localization is still not in the mainstream Accuracy Calibration overhead Simultaneously harness sensor-based dead-reckoning and environment sensing for localization
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OUTLINE Introduction Architecture and Intuition Design Details Evaluation Discussion and Conclusion
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ARCHITECTURE AND INTUITION Seed Landmarks (SLMs) Certain structures in the building that force users to behave in predictable ways stairs, 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
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UNLOC ARCHITECTURE
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DEAD-RECKONING ACCURACY Mean error 11.7m Mean error 1.2m
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LANDMARK DENSITY WiFi Landmarks 8 and 5 in two floor of engineering building, each of area less than 4m 2 Magnetic/Accelerometer Landmarks 6 and 8 for each floor
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COMPUTING LANDMARK LOCATIONS Combine all dead-reckoned estimates of a given landmark Errors are random and independent
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OUTLINE Introduction Architecture and Intuition Design Details Evaluation Discussion and Conclusion
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SEED LANDMARKS Define sensor patterns that are global across all buildings Acc stableAcc not stable
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DEAD RECKONING Displacement from accelerometer Step count * Step size Step size: counting the number of steps for a known displacement
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DEAD RECKONING Relative angular velocity Juxtaposes the gyroscope and compass
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ORGANIC LANDMARKS Distinct patterns K-means clustering algorithm Similarity threshold Small area – 4m 2
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ORGANIC LANDMARKS WiFi Landmarks MAC addresses, RSSI Similarity f i (a): RSSI of AP a overheard at l i A: set of AP heard at l 1 and l 2 Magnetic and Inertial Sensor Landmarks Bending coefficient
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OUTLINE Introduction Architecture and Intuition Design Details Evaluation Discussion and Conclusion
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EXPERIMENT SETTINGS Google NexusS phones 3 different users in 3 different university buildins Computer science(1750m 2 ), Engineering(3000m 2 ), North gate shopping mall(4000m 2 ) 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
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SLM DETECTION PERFORMANCE Trace from 2 malls in Egypt
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DETECTING ORGANIC LANDMARKS Number of landmarks detected inside different buildings
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DETECTING ORGANIC LANDMARKS Number of landmarks and accuracy increase over time
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LANDMARK SIGNATURE MATCHING Tradeoff between distinct signature and matching accuracy
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LOCALIZATION PERFORMANCE
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OUTLINE Introduction Architecture and Intuition Design Details Evaluation Discussion and Conclusion
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DISCUSSION AND CONCLUSION Use the information of landmarks to recalibrate user’s location. Median location errors is 1.69m Disadvantages: Device limited Energy
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