No Need to War-Drive: Unsupervised Indoor Localization Presented by Fei Dou & Xia Xiao Authors: He Wang, Souvik Sen, Ahmed Elgohary, ect. Published in: MobiSys 2012

Background and Challenges Challenges Achieve high location accuracy Simplify the calibration process Zero sum game between accuracy and calibration overhead UnLoc break away from the tradeoff, and achieve meter level accuracy with zero calibration Propose an unsupervised indoor localization scheme. It employs unsupervised learning to detect unique environmental signatures (called landmarks). Landmarks are used to correct the dead-reckoning error, which in turn improves the location accuracy of these landmarks.

Architecture and Intuition — Overview a.Seed Landmarks (SLMs) Certain structures in the building that force users to behave in predictable ways. Stairs, elevators, entrances, escalators b.Dead Reckoning Accelerometer - count the number of steps → derive the displacement Compass - track the direction of the steps Motion vector: c.Organic Landmarks (OLMs) Some signatures in magnetic domain or WiFi-based Have to be learnt dynamically

Architecture and Intuition — Overview Success of UnLoc relies on 3 expectations: a.Dead reckoning can attain desired levels of accuracy, if periodically recalibrated by landmarks. b.Indoor environments indeed offer the requisite number of landmarks. c.The locations of the landmarks can be computed from rough estimates of multiple devices

Architecture and Intuition — Dead-Reckoning Accuracy Accumulated error from dead-reckoning reduces with gyroscope, and further with landmarks.

Architecture and Intuition — Landmark Density a.WiFi Landmarks Some WiFi areas are very small (tail of distribution), and hence, an ideal landmark

Architecture and Intuition — Landmark Density b. Magnetic/Accelerometer Landmarks Some WiFi areas are very small (tail of distribution), and hence, an ideal landmark

Architecture and Intuition — Computing Landmark Locations Combining all the (dead-reckoned) estimates of a given landmark Dead-reckoning errors are random and independent, due to the noise in hardware sensors and human step sizes

Design details — Seed Landmarks Elevators, Staircases, and Escalators. elevator motion pattern variance of acceleration variance of the magnetic field correlation between the Z and Y acceleration components

Design details — Dead Reckoning b. Step size Height & Weight Counting number of steps for known distance. a.Step count Find the minimums. Find the maximums. If Max-Min > Threshold, count++ 1.Accelerometer: calculate distance

Design details — Dead Reckoning 2.Compass and gyroscope: calculate direction a. Compass will be derailed by local magnet interference. b. Gyroscope can only figure out the angular velocity, without direction. c. Mainly use the compass. Correlate the compass error by indicator of gyroscope.

Design details — Organic Landmarks b. K-means Algorithm Designate K. Select k element as cluster head. Loop: Put new element into most similar cluster. Recalculate the cluster head. End loop Steps: a.Gather sensors value For magnetic: mean, max, variance For WiFi: MAC ID, RSSI c. Figure out the cluster with low similarity. 1. Recognize distinct patterns:

Design details — Organic Landmarks 2. Recognize geographic cluster: Steps: a.Within same WiFi coverage b. Within same dead reckon area.

Design details — Organic Landmarks  WiFi Landmark features: ----Similarity calculation Same overhear Same signal strength If S < threshold for all other clusters, the cluster is distinct. A—overhears of 2 devices f i (a)--RSSI of device i

Design details — Organic Landmarks  Gyroscope features: ----Bending coefficient  Magnetic features: ----unique signature

Evaluation Results

Future works a.Investigate source of difference caused by the hardware variance and try to compensate the difference. b.Explore low power method, and let the users to decide the trade off between power and accuracy. c.Discuss the impact of environment change.

Thanks!