DarwinPhones - DARWIN PHONES THE EVOLUTION OF SENSING AND...

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DARWIN PHONES: THE EVOLUTION OF SENSING AND INFERENCE ON MOBILE PHONES PRESENTED BY: BRANDON OCHS Emiliano Miluzzo, Cory T. Cornelius, Ashwin Ramaswamy, Tanzeem Choudhury, Zhigang Liu, Andrew T. Campbell, "Darwin phones: the evolution of sensing and inference on mobile phones," In Proc. of 8th ACM Conference on Mobile Systems, Applications, and Services (MobiSys), 2010, pp. 5-20.
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What does Darwin do? A Smartphone platform for urban sensing Proof of concept model uses microphone Communicates with other local devices to improve inference accuracy (collaborative inference) Framework can be expanded to gather information using a range of sensor data
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What about battery life? Communicates with backend server to do the CPU-intensive machine learning algorithms Local devices share models rather than re- computing them Sensing is enabled/disabled as the system sees fit
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Common Urban Sensing Challenges Human burden of training classifiers Ability to perform reliably in different environments (indoor vs outdoor) The ability to scale to a large number of phones without hurting usability and battery life. Darwin overcomes all of these through classifier/model evolution, model pooling, and collaborative inference
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Types of Learning Supervised: Given a fully-labeled training set Semi-Supervised: Given a small training set that is evolved Unsupervised: No training set is given
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Darwin Steps Evolution, Pooling, and Collaborative Inference These represent Darwin’s novel evolve-pool-collaborate model implemented on mobile phones
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Classifier Evolution Automated approach to updating models over time Needs to account for variability in sensing conditions and settings Variability in background noise and phone location require separate models
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Model Pooling Reuses models that have already been built and evolved on other phones Exchange classification models
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DarwinPhones - DARWIN PHONES THE EVOLUTION OF SENSING AND...

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