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

The Johns Hopkins University2 Outline Motivation Data and Model Experiments and Results Discussion

The Johns Hopkins University3 Event starts Detect Event Increase Sampling Frequency/Trigger Alarms Event ends Return to steady behavior 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

The Johns Hopkins University4 Rain Event Non-Event Days Sample Event

The Johns Hopkins University5 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 University6 First Principal Component Variable #1 Variable #2 X : Points in the original space O : Projection on PC1 Principal Component Analysis PCA : Finds axes of maximum variance in the collected data Reduces original dimensionality –Example: 2 variables  1 variable

The Johns Hopkins University7 Motivation for Using PCA Typical day: “Fits model well”Event day: “Large residuals”

The Johns Hopkins University8 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 University9 Why not Soil Moisture ? Reaction to event

The Johns Hopkins University10 Air Temp vs. Soil Temp Notice the phase lag for Soil Temperature

The Johns Hopkins University11 Data Preparation Build model for Air and Soil temperature AT 1_1 AT 1_2 …. … AT 1_144 AT 2_1 AT 2_2 …. … AT 2_144.. …. …... …. AT 10_1 AT 10_2 …. … AT 10_144.. …. …... …... …. t=10 t=20 … t= day, 10 sensors Size of matrix: [( # of days x 10 )  144 ]

The Johns Hopkins University12 Number of PCA basis required

The Johns Hopkins University13 PCA Bases (AT & ST) Eigenvector1 Is the Diurnal cycle similarity eigenvector1 for ST & eigenvector2 for AT

The Johns Hopkins University14 Event Detection Methods 1.Basic Method –Projections on the first principal component for AT 2.Highpass Method –Removes seasonal drift by looking at sharp changes in the local neighborhood 3.Delta method –Uses the inertia of the soil and seasonal drift

The Johns Hopkins University15 Method 1 : Basic Method Considers only Air Temperature First Basis Vector covers 55% of variation in the data AT 1_1 AT 1_2 …. … AT 1_144 AT 2_1 AT 2_2 …. … AT 2_144.. …. …. AT 10_1 AT 10_2 …. … AT 10_144 V 1_1 V 1_2. V 1_144 e 1_1 e 2_1. e 10_1 X = Average E1E1 E2E2 …….. ……………..E n-1 EnEn Day 1Day 2Day n 10 sensors First Basis Vector (PC1) 1 day Apply threshold on E 1 series Tag values below the threshold as events

The Johns Hopkins University16 Method 2 : Highpass Method Again, considers only Air Temperature Apply highpass filter on E 1 series  S 1 series Highpass filter detects sharp changes by focusing on a limited time window  removes seasonal drift Apply threshold on S 1 series –Tag values below the threshold as events

The Johns Hopkins University17 Method 3 : Delta Method Considers Air Temperature (AT) and Soil Temperature (ST) Create E 1 series for AT and ST Apply Highpass filter on E 1,AT & E 1,ST  S 1,AT & S 1,ST Compute Delta = S 1,AT - S 1,ST for all days Set a threshold on the Delta series

The Johns Hopkins University18 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 University19 Results MethodPrecisionRecallFalse Negatives Basic52.459%64%18 Highpass51.28%80%10 Delta54.79%85.106%7 Method shortcomings -Does not consider seasonal drift (Basic) -Does not use the inertia information of the soil (Basic, Highpass)

The Johns Hopkins University20 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 University21 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 University22 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 University23 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 University24 Training Set (Air Temp) Seasons exhibit “Diurnal Cycles” around their daily mean (DC component) Construct Zero-Mean Vectors for each Sensor i for each day (remove DC Component) Remove outliers using a simple median filter to build the training set X