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StatSense

StatSense. In-Network Probabilistic Inference over Sensor Networks Presented by: Jeremy Schiff. Motivation and Problem Formulation . Sensor Readings are inaccurate Sensor Fusion can improve “virtual readings” Exact Inference has too many messages Exponential in size of graphical model

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StatSense

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  1. StatSense In-Network Probabilistic Inference over Sensor Networks Presented by: Jeremy Schiff

  2. Motivation and Problem Formulation • Sensor Readings are inaccurate • Sensor Fusion can improve “virtual readings” • Exact Inference has too many messages • Exponential in size of graphical model • Input: Poor sensor readings + graphical model • Output: Good “virtual readings”

  3. Key Ideas of Your Solution • Perform approximate inference • Tree-Reweighted Loopy Belief Propagation • Distributed message passing algorithm • Constant size messages • Improve results from no inference • Need to deal with node churn and asymmetric links • Which scheduling works best? • Quality of Reading vs. Number of Messages

  4. Current Status and Future Plans • Functioning simulator • Performs Belief Propagation • Simulates dead nodes • Simulates asymmetric links • Simulates different scheduling schemes • Allows empirical exploration of performance • Approximation has limited theoretical guarantees • Plan to run this on motes

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