a fresh perspective: learning to sparsify for detection in massive noisy sensor networks

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A Fresh Perspective: Learning to Sparsify for Detection in Massive Noisy Sensor Networks IPSN 4/9/2013 Matthew Faulkner Annie Liu Andreas Krause

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A Fresh Perspective: Learning to Sparsify for Detection in Massive Noisy Sensor Networks. Matthew Faulkner Annie Liu Andreas Krause. IPSN 4/9/2013. Community Sensors. More than 1 Billion smart devices provide powerful internet-connected sensor packages. Video Sound. GPS Acceleration - PowerPoint PPT Presentation

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A Fresh Perspective: Learning to Sparsify for Detection

A Fresh Perspective:Learning to Sparsify for Detection in Massive Noisy Sensor Networks

IPSN 4/9/2013

Matthew Faulkner Annie Liu Andreas Krause1Community SensorsMore than 1 Billion smart devices provide powerful internet-connected sensor packages.VideoSoundGPSAcceleration RotationTemperatureMagnetic Field LightHumidityProximity

2Dense, City-wide Networks

Signal Hill Seismic Survey5000 SeismometersWhat could dense networks measure?3Dense, City-wide NetworksWhat could dense networks measure?

Signal Hill Seismic Survey5000 Sesimometers

4Long Beach Seismic Network

5Caltech Community Seismic NetworkDetecting and Measuring quakes with community sensors

16-bit USB Accelerometer

CSN-Droid Android App6Scaling with Decentralized Detection

Quake?

5000 Long Beach: 250 GB/day300K LA: 15 TB/day7Scaling with Decentralized Detection

Optimal decentralized testsHypothesis testing[Tsitsiklis 88]

Local Detection

Quake?But strong assumptions89Weak Signals in Massive NetworksNo pick Pick10Weak Signals in Massive NetworksNo pick Pick11Weak Signals in Massive NetworksNo pick Pick12Weak Signals in Massive NetworksNo pick Pick

Trading Quantity for Quality?

Detecting arbitrary weak signals requires diminishing noise13Sparsifiable Events

14A Basis from Clustering 1-100 1-100 -1-111 1-100001-111-1-11111

Hierarchical clustering defines an orthonormal basisHaar Wavelet Basis15Latent Tree Model

Hierarchical dependencies can produce sparsifiable signals.

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Latent Tree Model

Hierarchical dependencies can produce sparsifiable signals.

17From Sparsification to Detection

Applying the basis to observed data gives a detection rule

Lots of noisy sensors can be reliable!18

Learning a Sparsifying BasisGiven real data, can we learn a sparsifying basis?

ICA [Hyvrinen & Oja 00]

Efficient, but assumes noise-free observations XContinuous, smooth19Learning a Sparsifying BasisGiven real data, can we learn a sparsifying basis?

SLSA [Chen 2011] Learns the basis from noisy data20Synthetic Experiments

Event signals generated from Singhs Latent Tree Model

Gaussian noise

Binary noise

Learned bases (ICA, SLSA) outperform baseline average and wavelet basis

Noise VarianceBinary Error Rate21Outbreaks on Gnutella P2P1769 High-degree nodes in the Gnutella P2P network.

snap.stanford.edu40,000 simulated cascades.

AUC(0.05)

Learned bases (SLSA, ICA) outperform scan statistics

Binary noise rate22Japan Seismic Network

2000+ quakes recorded after the 2011 Tohoku M9.0 quake

721 Hi-net seismometers

AUC(0.001) small tolerance to false positive

Binary noise rate23Japan Seismic Network

Learned basis elements capture wave propagation

AUC(0.001) small tolerance to false positiveBinary noise rate24Long Beach Sesimic Network

1,000 sensors Five M2.5 - M3.4 quakesLong Beach Seismic Network

2000 simulated quakes provide training data

Learned bases (SLSA, ICA) outperform wavelet basis and scan statistics

Caltech Community Seismic Network

128 sensors Four M3.2 M5.4 quakes27Caltech Community Seismic Network

Trained on 1,000 simulated quakes

Learned bases (SLSA, ICA) detect quakes up to 8 seconds fasterConclusions Theoretical guarantees about decentralized detection of sparsifiable events Framework for learning sparsifying bases from simulations or sensor measurements Strong experimental performance on 3 seismic networks, and simulated epidemics in P2P networksReal-time event detection in massive, noisy community sensor networks29