axe soota ubiquitous and mobile computing vishal rathi...
TRANSCRIPT
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Ubiquitous and Mobile Computing CS 403x: Automatically Characterizing
Places with Opportunistic CrowdSensing using Smartphones
Ankit Kumar Vishal Rathi
Axe Soota
Computer Science Dept.
Worcester Polytechnic Institute (WPI)
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● Traditional location sensing systems only make use of WiFi and GPS
● The error in GPS-, GSM-, or WiFi-based location estimates often ranges between 10 and 400 meters
● 426 of the 1,241 place visits incorrectly reported based on the location estimate
Problem Statement
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Introducing CSP
● CSP - CrowdSense@Place● Interpretation of a location from Location Sensor
to user - as a place● Framework that exploits sensors that most
phone’s have● Smartly capture images and audio clips from
smartphones● Goal is to link place visits to various place
categories
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Current Approach
● Place-discovery techniques these days:
● Exploit large-scale data collections, like point-of-interest databases (Google) to allocate place descriptors
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Related Work
● Bing, Yelp
● Facebook, Twitter, FourSquare
● CenceMe - Similar application but doesn’t infer from images
● SenseCam - Goal to understand user’s environment
● VibN - Identifies points of interest in the city
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How is CSP different?
● CrowdSense@Place - Place classification based on existing methods to perform place segmentation
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Overview
● Smartphone Application○ Sensing and Data Collection○ Privacy Settings
● Offline server-side processing○ Processing and Location Detection
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Data Collection
● Audio detection○ “Do you have a Large size of these pants?”
● Pictures of objects
● Written Texts
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Methodology
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Smartphone Client
● Place Segmentation - WiFi fingerprinting and GPS to discover places
● Sensor Sampling - Simple heuristic to improve quality of data collected
● Privacy - Data resides on device for 24 hours
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Sensor Data Classifiers
● Optical Character Recognition (OCR)● Indoor Scene Classification● Objects Recognition● Speech Recognition● Sound Classification
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Place Modeling
● Data preprocessing○ Classifier Terms○ Mobility Terms
● Place Categorization
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Results - Classifiers
● Indoor scene classification (GIST features) has the largest impact
● OCR does not have a strong overall effect● Object detection, speech recognition, and sound
classification had major effects
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Results - Location Accuracy
● 69% Accuracy
● CSP outperforms GPS and Mobility by around 22% to 40%
● Mobility has 44% accuracy for workplace and 52% for college while CSP has 80% and 71% respectively
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Applications of CSP
● Enhanced Local Search &
Recommendations
● Rich Crowdsourced Point-of-Interest
Category Maps
● Understanding City-scale Behavior Patterns
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Limitations and Future Work
● Finer Place Categorization
● Privacy
● Activity vs. Place Category
● Energy Issues
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Conclusions
● 36 person study
● Seven-weeks total
● 1241 places on 5 locations
● Average accuracy of 69%
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What we liked/disliked about the paper?
Likes:
● Graphs and tabulated data findings● The intensive study conducted● Limitations and issues considered
Dislikes:
● Doesn’t address privacy concerns appropriately
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Questions
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References
● http://www.fengzhao.com/pubs/ubicomp12_cps.pdf