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