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