Model Based Event Detection in Sensor Networks

1. Jayant Gupchup, Andreas Terzis, Randal Burns, Alex Szalay Model Based Event Detection in Sensor Networks

2. Outline • Motivation • Data and Model • Experiments and Results • Discussion The Johns Hopkins University

3. Data Sampling in WSNs • Most environmental monitoring networks today sample at fixed frequencies • The failure of fixed frequency sampling • High Frequency: Generates large volumes of measurements • Low Frequency: Misses temporal transients • Solution: Adaptive Sampling based on the ability to detect events of interest • Benefits • Less but more “interesting” data • Conserve energy • Trigger alarms • Event-based querying in the back-end database Event starts Detect Event Increase Sampling Frequency/Trigger Alarms Event ends Return to steady behavior The Johns Hopkins University

4. Sample Event Rain Event Non-Event Days The Johns Hopkins University

5. Solution Outline • Model observed phenomena using Principal Component Analysis (PCA) • Project original measurements on to a feature space • Benefit: reduces dimensionality • Look for measurements deviating from average/expected behavior in the feature space The Johns Hopkins University

6. Principal Component Analysis X : Points in the original space O : Projection on PC1 Variable #2 First Principal Component Variable #1 • PCA : Finds axes of maximum variance in the collected data • Reduces original dimensionality • Example: 2 variables  1 variable The Johns Hopkins University

7. Motivation for Using PCA Typical day: “Fits model well” Event day: “Large residuals” The Johns Hopkins University

8. Specific Application • LifeUnderYourFeet: Environmental Monitoring network for soil moisture • Deployment details • 10 MICAz Sensors • Air Temperature (AT) • Soil Temperature (ST) • Soil Moisture • Light intensity • Deployed for a period of a year • Goal: Detect significant rain events The Johns Hopkins University

9. Why not Soil Moisture ? Reaction to event Reaction to event The Johns Hopkins University

10. Air Temp vs. Soil Temp Notice the phase lag for Soil Temperature The Johns Hopkins University

11. Data Preparation t=10 t=20 … t=1440 1 day, 10 sensors • Build model for Air and Soil temperature Size of matrix: [(# of days x 10) 144] The Johns Hopkins University

12. Number of PCA basis required The Johns Hopkins University

13. PCA Bases (AT & ST) Eigenvector1 Is the Diurnal cycle similarity eigenvector1 for ST & eigenvector2 for AT The Johns Hopkins University

14. Event Detection Methods • Basic Method • Projections on the first principal component for AT • Highpass Method • Removes seasonal drift by looking at sharp changes in the local neighborhood • Delta method • Uses the inertia of the soil and seasonal drift The Johns Hopkins University

15. Method 1 : Basic Method • Considers only Air Temperature • First Basis Vector covers 55% of variation in the data First Basis Vector (PC1) = X 10 sensors 1 day Average Day 1 Day 2 Day n • Apply threshold on E1 series • Tag values below the threshold as events The Johns Hopkins University

16. Method 2 : Highpass Method • Again, considers only Air Temperature • Apply highpass filter on E1 series  S1 series • Highpass filter detects sharp changes by focusing on a limited time window  removes seasonal drift • Apply threshold on S1 series • Tag values below the threshold as events The Johns Hopkins University

17. Method 3 : Delta Method • Considers Air Temperature (AT) and Soil Temperature (ST) • Create E1 series for AT and ST • Apply Highpass filter on E1,AT & E1,STS1,AT& S1,ST • Compute Delta =S1,AT-S1,ST for all days • Set a threshold on the Delta series The Johns Hopkins University

18. Evaluation • Data Set • Test Period : 225 days between September, 2005 – July, 2006 • 48 major rain events occurred during this period • Reported by the BWI weather station • Evaluation metrics • Precision (true positives) • Recall • Number of false negatives The Johns Hopkins University

19. Results • Method shortcomings • Does not consider seasonal drift (Basic) • Does not use the inertia information of the soil (Basic, Highpass) The Johns Hopkins University

20. Event detection for 12/13/2005 – 01/02/2006 Due to the inertia of the soil, ‘Delta method’ shows sharper negative peaks for event days. The Johns Hopkins University

21. Future work • Implement “Online event detection” • Compute Basis vectors from historic data • Load the ‘basis vectors’ and ‘threshold’ values on the motes • Detect localized events by forming clusters of motes with similar eigen-coefficients • Apply technique for faulty sensor detection • Consider variants of PCA (Gappy-PCA, online-PCA) The Johns Hopkins University

22. Acknowledgements • Ching-Wa Yip 1 - PCA C# library and Discussions. • Katalin Szlavecz 2 & Razvan Musaloui-E 3 • Domain expertise and data collection. • Jim Gray 4 & Stuart Ozer 4 • Online database 1 : JHU, Dept of Physics & Astronomy 2 : JHU, Dept of Earth and Planetary science 3 : JHU, Dept of Computer Science. 4 : Microsoft Research The Johns Hopkins University

23. Future work • Online event detection on the motes • Apply this method for faulty sensor detection • Detect localized events by forming clusters of motes with similar eigencoefficients. • Consider incomplete days using Gappy-PCA. • Explore incremental & robust PCA techniques. The Johns Hopkins University

24. Training Set (Air Temp) • Seasons exhibit “Diurnal Cycles” around their daily mean (DC component) • Construct Zero-Mean Vectors for each Sensori for each day (remove DC Component) • Remove outliers using a • simple median filter to • build the training set X The Johns Hopkins University