a multi-level data fusion approach for early fire detection odysseas sekkas stathes hadjiefthymiades...
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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
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