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SurroundSense : Mobile Phone Localization via Ambience Fingerprinting

SurroundSense : Mobile Phone Localization via Ambience Fingerprinting. MARTIN AZIZYAN, IONUT CONSTANDACHE, ROMIT ROY CHOUDHURY Presented by Lingfei Wu. Outline. Introduction SurroundSense Architecture System Design Implementation Evaluation Conclusion. Introduction.

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SurroundSense : Mobile Phone Localization via Ambience Fingerprinting

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  1. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting MARTIN AZIZYAN, IONUT CONSTANDACHE, ROMIT ROY CHOUDHURY Presented by Lingfei Wu

  2. Outline • Introduction • SurroundSense Architecture • System Design • Implementation • Evaluation • Conclusion

  3. Introduction Application of logical localization • Notion of location • Physical coordinates(latitude/longitude) • Logical labels(like Starbucks, Mcdonalds) • Many applications based on logical location

  4. Introduction Physical coordinate can be reversed to logical location. However, it often causes error ! Why not compute logical location directly?

  5. Relative work 1. Lack accuracy 2. Need pre-installed infrastructure • Active RF • Install special hardware • Ultrasound, Bluetooth • Passive RF • GPS, GSM or WIFI based • Behavior Sensing • Imaging matching

  6. Motivation Starbucks Bookstore Wal-Mart McDonald’s • Combine effect of ambient sound, light, color, user motion • Sound (microphone) • Starbucks VS Bookstore • Light / Color (camera) • Different thematic light, colors and floors. • Human movement (accelerometer) • Wal-Mart VS McDonald • Place may not be unique based on any one attribute • The combination can be unique enough for localization • In this paper, we propose SurroundSense for logical localization.

  7. SurroundSense Architecture Candidate list 1.Xxx 2.Yyy 3.zzz 1.Xxx 2.Yyy 1.Xxx 2.Yyy 1.Xxx 2.Yyy 1.Xxx

  8. System Design • Fingerprint generation • Fingerprinting sound • Fingerprinting motion using accelerometers • Fingerprinting color/light using cameras • Fingerprinting Wi-Fi • Fingerprint matching • Wi-Fi filter • Sound filter • Motion filter • Color/light Match

  9. Fingerprinting sound Normalized occurrence count amplitude value 50 0 -50 time time Normalized amplitude value • Convert signals to time domain • 100 normalized values as feature of sound • Similarity of fingerprints • Compute 100 pair-wise distance between test fingerprint and all candidate fingerprint

  10. Fingerprinting Sound • Unreliable to be a matching scheme • Sound from the same place can vary over time. • Only use as a filter • If distance > threshold τthen discard from the candidate list

  11. Fingerprinting Motion Raw data SVM 1 moving -1 stationary • Use support vector machine(SVM) as classifier • Sequence of states as user’s moving pattern • Movement is prone to fluctuation • In a clothing store, Some users browse for a long time while others purchase clothes in haste. • Only use as a filter

  12. Fingerprinting Motion Sitting Browsing Walking • Compute motion fingerprint: Ratio = tmoving / tstatic • Bucket 1: 0.0 <= Ratio <= 0.2 Sitting (cafe) • Bucket 2: 0.2 <= Ratio <= 2.0 Browsing (clothing) • Bucket 3: 2.0 <= Ratio <= ∞ Walking (grocery)

  13. Fingerprinting Color / Light too much noise • Thematic color and lighting in different stores • Where to capture the picture? • random picture of surrounding • floor • Advantages of taking floor pictures • Privacy concern • Less noisy • Rich diversity in floor color • Easy to obtain

  14. Fingerprinting Color / Light too much noise k=2 sk: the sum of distance from all pixels to their (own cluster’s) centroid. t: convergence threshold k-mean clustering k++ sk-sk-1 < t no yes Bean Trader’s Coffee shop < c1, c2 …, ck > • How to extract colors and light intensity? • RGB • HSL(Hue-Saturation-Lightness) • Find color cluster and its size using K-means clustering algorithm

  15. Fingerprinting Color / Light Total size in C1 or C2 distance of centroid • Similarity of fingerprints • Assume C1 = {c11, c12, …, c1n}; C2 = {c21, c22, …, c2m} • Fingerprint matching • The candidate list with maximum similarity is declared to the matching fingerprint

  16. Fingerprinting Wi-Fi Fingerprint tuple: <{AP1_MAC_Addr, AP1_fraction_time}, {AP2_MAC_Addr, AP2_fraction_time}, {AP3_MAC_Addr, AP3_fraction_time}> • Wi-Fi fingerprint • Record MAC address from APs every 5 second

  17. Fingerprinting Wi-Fi fraction of time M: union of MAC address of fingerprints f1 and f2 • Similarity of fingerprints • Use as filter/matching module • In the absence of light/color, we use it as matching module. • Accuracy depend on location of shops.

  18. Implementation • Client and server • Client: Nokia 95 phones using Python as client • Server: Matlab and Python code and some data mining tools for fingerprinting algorithms. • Fingerprint database • Labor-intensive war-sensing at 51 stores • Store location: 46 business location in university town, 5 location in India

  19. Implementation

  20. Evaluation • SurroundSense(SS) test environment • War-sensed 51 shops organized in 10 clusters • 4 students visited the first nine clusters in university town, while 2 students visited the tenth cluster in India. • 4 localization models: • Wi-Fi only (Wi-Fi) • Sound, Accelerometer, Light and color ( Snd-Acc-Lt-Clr) • Sound, Accelerometer, Wi-Fi (Snd-Acc-Wi-Fi) • SurroundSense (SS)

  21. Evaluation – Per-Cluster Accuracy Similar hardwood floor in strip mall No Wi-Fi Best represented Same AP False negative Snd and Acc Restaurant

  22. Evaluation – Per-Shop Accuracy 30% shops 47% shops SS: 92% Snd-Acc-WiFi: 92% Snd-Acc-Lt-Clr: 75% WiFi: 75% To understand the localization accuracy on a per-shop basis

  23. Evaluation – Per-User Accuracy Simulate 100 virtual user, each assign 4~8 stores from cluster 1~9

  24. Evaluation – Per-Sensor Accuracy false negative Percentage localized using special sensors Number of shops left after special filter Hand-picked 6 samples to exhibit the merits and demerits of each sensor

  25. Conclusion Presented SurroundSense, a non-conventional approach for logical localization. Created fingerprints about ambient sound, light, color, movement and Wi-Fi and match them with fingerprint database to realize accurate logical localization. The evaluation achieved an average location accuracy of over 85% using all sensors.

  26. Discussion • The GPS 10 m, Wi-Fi and GSM 40m and 400m respectively. Why not use Wi-Fi to get initial location instead of using GSM? • Support vector machines (SVM), K-means clustering algorithm are used in paper, do you have any better machine learning methods? Such as Kalman filter, Particlefilter, and Wavelet Transform? • Can other sensors help? Such as compass and Bluetooth? • Energy consideration? Non-business location?

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