1. Fire Detection in the Urban Rural Interface through Fusion Techniques Evangelos Zervas Odysseas Sekkas Stathes Hadjiefthymiades Christos Anagnostopoulos T.E.I. Of Athens, Department of Electronics Pervasive Computing Research Group, Department of Informatics and Telecommunications University of Athens, Greece MASS-GHS07, 08.10.2007, Pisa, Italy

2. Fire Detection in Urban Rural Interface (URI) Early work in the framework of SCIER (FP6-IST) (Sensor & Computing Infrastructure for Environmental Risks) zone of interest

3. Fire Detection in URI: Architecture Local Alerting Control Unit (LACU) Early fire detection Fire location estimation Alerting function Citizen Owned Sensors Publicly Owned Sensors Types of sensors: Temperature Humidity Wind flow Cameras

4. Physical Model Temperature ( T ) Fuel mass function ( F ) after 30sec. from ignition Fire is sensed only fewer meters from the ignition point

5. Binary hypothesis problem ML Criterion:The “No Fire” Case sensor measurement for sensor j Gaussian with mean μ(h) Mean μ(h) depends on: time (hours/month), empirical models, forecasting, sensor readings that are more up-to-date [Walter’s model] [Drop the D highest and lowest temperature measurements out of K available] sensor measurement noise (zero mean )

6. ML Criterion: The “Fire” Case random variable q j measures the excess temperature due to fire Gaussian with mean μ q (h) We consider a heat radiation model with mean μ q (h) depending on: Δ H (excess temperature at fire location) x (distance of the sensor from the fire front) a (exponent obtained from the physical model)

7. Receiver Operating Characteristics (ROC) Parameters: μ(h) = 300K, σ s = 3 K, σ n = 0.5 K, σ q = 1 K, a= 2.3, Δ H= 700K

8. Receiver Operating Characteristics (ROC) R: monitoring area of temperature sensors for creating a dense lattice of sensors for fire early detection R

9. Current Work in SCIER Use of (fuzzy) Neural Nets and/or BN for classification using data from temperature and humidity sensors, Use of alternative criteria, i.e. CUSUM sequential algorithm, Use information fusion at a higher level (Computing Subsystem) taking into account the vision sensors.

10. Thank you http://p-comp.di.uoa.gr