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1 SurroundSense: Mobile Phone Localization via Ambience Fingerprinting Ionut Constandache Co-authors: Martin Azizyan and Romit Roy Choudhury
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2 Context Pervasive wireless connectivity + Localization technology = Location-based applications
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3 Location-Based Applications (LBAs) For Example: GeoLife shows grocery list when near Walmart MicroBlog queries users at a museum Location-based ad: Phone gets coupon at Starbucks iPhone AppStore: 3000 LBAs, Android: 500 LBAs
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4 Location-Based Applications (LBAs) For Example: GeoLife shows grocery list when near Walmart MicroBlog queries users at a museum Location-based ad: Phone gets coupon at Starbucks iPhone AppStore: 3000 LBAs, Android: 500 LBAs Location expresses context of user Facilitates content delivery
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5 Location is an IP address As if for content delivery
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6 Thinking about Localization from an application perspective…
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7 Emerging location based apps need place of user, not physical location Starbucks, RadioShack, Museum, Library Latitude, Longitude
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8 Emerging location based apps need place of user, not physical location Starbucks, RadioShack, Museum, Library Latitude, Longitude We call this Logical Localization …
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9 Can we convert from Physical to Logical Localization?
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10 Can we convert from Physical to Logical Localization? State of the Art in Physical Localization: 1.GPS Accuracy: 10m 2.GSMAccuracy: 100m 3.Skyhook (WiFi+GPS+GSM) Accuracy: 10m-100m State of the Art in Physical Localization: 1.GPS Accuracy: 10m 2.GSMAccuracy: 100m 3.Skyhook (WiFi+GPS+GSM) Accuracy: 10m-100m
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11 Widely-deployable localization technologies have errors in the range of several meters Widely-deployable localization technologies have errors in the range of several meters Can we convert from Physical to Logical Localization? State of the Art in Physical Localization: 1.GPS Accuracy: 10m 2.GSMAccuracy: 100m 3.Skyhook (WiFi+GPS+GSM) Accuracy: 10m-100m State of the Art in Physical Localization: 1.GPS Accuracy: 10m 2.GSMAccuracy: 100m 3.Skyhook (WiFi+GPS+GSM) Accuracy: 10m-100m
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12 Several meters of error is inadequate to logically localize a phone Physical Location Error
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13 Several meters of error is inadequate to logically localize a phone RadioShackStarbucks Physical Location Error The dividing-wall problem
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14 Contents SurroundSense Evaluation Limitations and Future Work Conclusion
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15 Contents SurroundSense Evaluation Limitations and Future Work Conclusion
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16 It is possible to localize phones by sensing the ambience Hypothesis such as sound, light, color, movement, WiFi …
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17 Sensing over multiple dimensions extracts more information from the ambience Each dimension may not be unique, but put together, they may provide a unique fingerprint
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18 SurroundSense Multi-dimensional fingerprint Based on ambient sound/light/color/movement/WiFi Starbucks Wall RadioShack
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19 B A C D E Should Ambiences be Unique Worldwide? F G H J I L M N O P Q Q R K
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20 Should Ambiences be Unique Worldwide? B A C D E F G H J I K L M N O P Q Q R GSM provides macro location (strip mall) SurroundSense refines to Starbucks
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21 + + Ambience Fingerprinting Test Fingerprint Sound Acc. Color/Light WiFi Logical Location Matching Fingerprint Database = = Candidate Fingerprints GSM Macro Location SurroundSense Architecture
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22 Fingerprints Sound: (via phone microphone) Color: (via phone camera) Amplitude Values -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Normalized Count 0.14 0.12 0.1 0.08 0.06 0.04 0.02 0 Acoustic fingerprint (amplitude distribution) Color and light fingerprints on HSL space Lightnes s 1 0.5 0 Hue 0 0.5 1 0 0.2 0.4 0.6 0.8 1 Saturation
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23 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Moving
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24 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Moving Queuing
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25 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Queuing Seated Moving
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26 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Pause for product browsing Moving
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27 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Pause for product browsing Short walks between product browsing Moving
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28 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Walk more Moving
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29 Fingerprints Movement: (via phone accelerometer) CafeteriaClothes Store Grocery Store Static Walk more Quicker stops Moving
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30 Fingerprints Movement: (via phone accelerometer) WiFi: (via phone wireless card) CafeteriaClothes Store Grocery Store Static ƒ (overheard WiFi APs) Moving
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31 Discussion Time varying ambience Collect ambience fingerprints over different time windows What if phones are in pockets? Use sound/WiFi/movement Opportunistically take pictures Fingerprint Database War-sensing
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32 Contents SurroundSense Evaluation Limitations and Future Work Conclusion
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33 Evaluation Methodology 51 business locations 46 in Durham, NC 5 in India Data collected by 4 people 12 tests per location Mimicked customer behavior
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34 Evaluation: Per-Cluster Accuracy Cluster No. of Shops 12345678910 4737455655 Accuracy (%) Cluster Localization accuracy per cluster
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35 Evaluation: Per-Cluster Accuracy Cluster No. of Shops 12345678910 4737455655 Accuracy (%) Cluster Localization accuracy per cluster Multidimensional sensing
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36 Evaluation: Per-Cluster Accuracy Cluster No. of Shops 12345678910 4737455655 Fault tolerance Accuracy (%) Cluster Localization accuracy per cluster
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37 Evaluation: Per-Cluster Accuracy Cluster No. of Shops 12345678910 4737455655 Accuracy (%) Cluster Localization accuracy per cluster Sparse WiFi APs
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38 Evaluation: Per-Cluster Accuracy Cluster No. of Shops 12345678910 4737455655 No WiFi APs Accuracy (%) Cluster Localization accuracy per cluster
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39 Evaluation: Per-Scheme Accuracy ModeWiFiSnd-Acc-WiFiSnd-Acc-Lt-ClrSS Accuracy70%74%76%87%
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40 Evaluation: User Experience Random Person Accuracy Average Accuracy (%) 0 10 20 30 40 50 60 70 80 90 100 1 0.9 0.8 0.7 0.6 0.5 CDF 0.4 0.3 0.2 0.1 0 WiFI Snd-Acc-WiFi Snd-Acc-Clr-Lt SurroundSense
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41 Economics forces nearby businesses to be different Not profitable to have 3 coffee shops with same lighting, music, color, layout, etc. SurroundSense exploits this ambience diversity Why does it work? The Intuition:
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42 Contents SurroundSense Evaluation Limitations and Future Work Conclusion
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43 Limitations and Future Work Energy-Efficiency Localization in Real Time Non-business locations
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44 Limitations and Future Work Energy-Efficiency Continuous sensing likely to have a large energy draw Localization in Real Time Non-business locations
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45 Limitations and Future Work Energy-Efficiency Continuous sensing likely to have a large energy draw Localization in Real Time User’s movement requires time to converge Non-business locations
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46 Limitations and Future Work Energy-Efficiency Continuous sensing likely to have a large energy draw Localization in Real Time User’s movement requires time to converge Non-business locations Ambiences may be less diverse
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47 Contents SurroundSense Evaluation Limitations and Future Work Conclusion
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48 SurroundSense Today’s technologies cannot provide logical localization Ambience contains information for logical localization Mobile Phones can harness the ambience through sensors Evaluation results: 51 business locations, 87% accuracy SurroundSense can scale to any part of the world
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49 Questions? Thank You! Visit the SyNRG research group @ http://synrg.ee.duke.edu/
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