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

Fire Detection in Urban Rural Interface (URI or WUI)

Work in the framework of SCIER (FP6-IST) (Sensor & Computing Infrastructure for Environmental Risks)

zone ofinterest

LACU

Computing Subsystem

Public infrastructure

private infrastructure

con

trol

Local Alerting Control Unit LACU

LACULACU LACU

LACU

LACU

SCIER architecture

Sensing Subsystem

Sensor Infrastructure– In-field sensor nodes (humidity, temp, wind speed/direction)– Out-of-field vision sensors (vision sensor)

Sensor Data Fusion

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

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)

Computing Subsystem Architecture

User Interface

Fusion SubsystemLACU Manager

GRID C.S.

From/To LACUs

Simulation IF

Simulation Subsystem

FF Sim

Storage Subsystem

DS Manager

Data StorageDB

GIS

User Interface

Fusion SubsystemLACU Manager

GRID C.S.

From/To LACUs

Simulation IF

Simulation Subsystem

FF Sim

Storage Subsystem

DS Manager

Data StorageDBDB

GIS

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

First level fusion

Sequence of random variables (e.g. values of temperature sensor)

density in “no fire” case, μ0 denotes the mean temperature value

density in “fire” case, μF 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]

First level fusion

Change detection [Gombay ’05]– Cumulative Sum (CUSUM) test– conclude that a change from the initial μ0 mean value to μF

occurs at time τ.

Basic probability assignments (BPA) for each sensor

or use an increasing function g(·) that maps the interval [μ0,μF] tothe interval [0,1].

The same techniques of change detection can be applied also for humidity sensors. In this case μ0 denotes the ambient relative humidity which decreases in the “fire” case

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

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.

)()()()()()(1

1)( 21212112 FmFnoFmFnoFmFmFmFm

KFm

)()()()( 2121 FmFnomFnomFmK

For the fire detection we use the result m123…M(F) and compare it to a threshold t

•With 3 or more sensors we calculate

m123…M(F), m123…M(noF) and m123…M(F U no - F)

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

Fire front evolution

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

System Validation & Evaluation

Gestosa, Portugal (experimental and controlled burns)

System Validation & Evaluation

Stamata, Attica, Greece (system deployment)

Thank you

http://p-comp.di.uoa.gr