1. A Multi-level Data Fusion Approach for Early Fire Detection Odysseas Sekkas Stathes Hadjiefthymiades Evangelos Zervas Pervasive Computing Research Group, Department of Informatics and Telecommunications, University of Athens, Greece Department of Electronics, T.E.I. Of Athens, Greece CIDM-2010, 25.11.2010, Thessaloniki, Greece

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

3. SCIER architecture

4. Sensing Subsystem Sensor Infrastructure – In-field sensor nodes (humidity, temp, wind speed/direction) – Out-of-field vision sensors (vision sensor) Sensor Data Fusion

5. Localized Alerting Subsystem-LACU Receives sensor data and executes fusion algorithms. Generates fused data with degree of reliability. Fused data fed to the Computing Subsystem. The false alarm rate (fire detection in case of no fire) is parameterized user requirements season of the year (e.g. summer) risk factor of the monitored area

6. Computing Subsystem (CS) Computation and Storage Environmental models Main functionalities of CS – Collect and store sensor-measurements from the area of interest – Perform fusion-algorithms to assess the level of risk – Trigger a simulation in case of an alarm, i.e. retrieve geographical data from the GIS Database on the terrain layout of the area of interest. Predictive Modeling (simulations of fire propagation using GRID Computing)

7. Computing Subsystem Architecture User Interface Fusion SubsystemLACU Manager GRID C.S. From/ToLACUs Simulation IF Simulation Subsystem FFSim Storage Subsystem DS Manager Data Storage DB GIS User Interface Fusion SubsystemLACU Manager GRID C.S. From/ToLACUs Simulation IF Simulation Subsystem FFSim Storage Subsystem DS Manager Data Storage DB GIS

8. Multilevel fusion scheme Monitors the distribution of sensor data (e.g. ambient temperature) Assigns in each sensor a probability on “fire” case Collects probabilities on “fire” case from in-field sensors and cameras Probabilities combined through DS theory in order to make a final decision about fire occurrence

9. First level fusion Sequence of random variables (e.g. values of temperature sensor) density in “no fire” case, μ 0 denotes the mean temperature value density in “fire” case, μ F denotes the mean temperature value superscripts e, h, f and m denote empirical, historical, forecasting and measured estimates respectively. empirical estimation of temperature Walters’ model [Walter ‘67]

10. First level fusion Change detection [Gombay ’05] – Cumulative Sum (CUSUM) test – conclude that a change from the initial μ 0 mean value to μ F occurs at time τ. Basic probability assignments (BPA) for each sensor or use an increasing function g(·) that maps the interval [μ 0,μ F ] to the interval [0,1]. The same techniques of change detection can be applied also for humidity sensors. In this case μ 0 denotes the ambient relative humidity which decreases in the “fire” case

11. Second level fusion Collection of probabilities on the “fire” case – camera: significant change in the contrast or the luminance of a scene is translated to a probability of “fire” – Cases where a camera tile does not oversee any sensor(s), or a/any sensor(s) is/are not overseen by a camera fusion process will be carried out taking into account the probabilities of a single camera tile or any sensor(s) respectively. Combination of probabilities through DS-theory [Shafer ‘76] decision of experts Si and Sj

12. Second level fusion For each sensor we need the BPAs – m(F), “fire” case – m(no - F), “no fire” case – m(F U no - F), the uncertainty of the sensor. For the fire detection we use the result m 123 … M (F) and compare it to a threshold t With 3 or more sensors we calculate m 123…M (F), m 123…M (noF) and m 123…M (F U no - F)

13. Fire front evolution The fusion result indicates “fire” in a specific location – SCIER CS initiates a simulation of several runs in the GRID infrastructure – each run computes the expected evolution of the fire front line for up to 180 minutes after fire detection – The model is fed with information about the topography, moisture content, type of the surface fuel dynamic environmental parameters such as the wind

14. Fire front evolution

15. Conlusions Adoption of a layered fusion scheme – cope with different type of sensors – use in-field and out-of-field sensors – increase the reliability of the system reduce false alarm rates satisfy the early detection requirement Future work: – use alternative combination rules other than DS – adoption of the Fuzzy Set theory to deal with uncertainty, imprecision and incompleteness of the underlying data

16. System Validation & Evaluation Gestosa, Portugal (experimental and controlled burns)

17. System Validation & Evaluation Stamata, Attica, Greece (system deployment)

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