StatSense

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