-
1-4244-1455-5/07/$25.00 c�2007 IEEE
Fire Detection in the Urban Rural Interface through Fusion
techniques
E. ZervasDept. of Electronics
TEI-Athens12210 Athens, Greece
Email: [email protected]
O. Sekkas, S. Hadjieftymiades, C. AnagnostopoulosDept. of
Informatics and Telecommunications
University of Athens15784 Athens, Greece
Email: �sekkas,shadj, bleu�@di.uoa.gr
Abstract
Fires are a common, disastrous phenomenon that con-stitutes a
serious threat. Thus, early detection is of greatimportance as the
consequences of a fire are catastrophic.Towards this direction the
SCIER1 project envisages thedeployment of Wireless Sensor Networks
at the “Urban-Rural-Interface” (URI) and uses sensor fusion
techniquesto enhance the performance of the early fire detection
andfire location estimation processes.
1 Introduction
Fires are a common, disastrous phenomenon that con-stitutes a
serious threat. Because of their speed of spreadand intensity they
often lead to property damages, personalinjuries and loss of human
lives. Thus, early detection isof great importance as the
consequences of a fire (indooror outdoor) are catastrophic. Fires
that occur in wildland(forests, etc.) could also affect inhabited
areas. These areasare widely known as “Urban-Rural-Interface”
(URI), i.e.,zones where forests and rural lands interface with
homes,other buildings and infrastructures. In the URI, fires are
fre-quent due to the special nature of these zones; a fire could
bethe result either of human on one side of the zone, or
couldarrive from the other side (wildland). In such cases
earlydetection leads to an efficient control of the fire and
makesfeasible the immediate evacuation of the entire area if this
isconsidered necessary. Great technology effort has been in-vested
on the design of systems for fire detection and moni-toring. Most
of them make use of temperature and humiditysensors, smoke
detectors, infrared cameras, etc. In addition,aerial or satellite
images are frequently used for outdoor firedetection and
monitoring. In [3] a fire detection system is
1SCIER (Sensor & Computing Infrastucture for Environmental
Risks)is partially funded by the European Community through the FP6
IST Pro-gram. The work presented in this paper expresses the ideas
of the authorsand not necessarily the whole SCIER consortium.
proposed based on multi-sensor technology and neural net-works
(NNs). The sensed data include environmental tem-perature, smoke
density and CO density. The use of NNsfor automatic detection of
smoke is also proposed in [10].A system for wildfire monitoring
using a wireless sensornetwork (WSN) that collects temperature
relative humidityand barometric pressure is described in [4]. The
wirelessnetworked nodes communicate with a base station that
col-lects the sensed data. Satellite based monitoring [11] is
an-other method to detect forest fires but the scan period andthe
low resolution of satellite images make this method in-capable for
real-time detection. The authors in [2] and [7]propose systems
based on infrared (IR) technology for thedetection of fires.
The SCIER project will design, develop, and demon-strate an
integrated system of sensors, networking and com-puting
infrastructure for detecting, monitoring and predict-ing natural
hazards (fires, etc.) at the URI. The overall goalof the SCIER
system is to make the neglected URI zonesafer against any type of
natural hazards or accidents usingwireless sensor network
technologies, fusion techniques toassess dangerous situations, and
predictive models to esti-mate the evolution of the hazardous
phenomena. The fu-sion techniques used in SCIER are implemented in
a spe-cial component of the SCIER system: the Local AlertingControl
Unit (LACU). LACU controls a Wireless SensorNetwork (WSN) and is
responsible for the early detectionof potential fires, the fire
location estimation and the sub-sequent alerting functions. For the
detection phase, sen-sor readings are evaluated and probabilities
are assigned oneach situation. If the probability of fire event
exceeds a pre-determined threshold, the system shifts to fire
location esti-mation phase. In this phase the centre of the fire
outbreakand the radius of the fire spread are determined.
The rest of the paper is organized as follows: in Section2 the
SCIER architecture is described and the requirementsconcerning the
topology of the wireless sensors controlledby LACU are defined.
Section 3 discusses fire detectionand fire location estimation
based on sensor readings (tem-perature) and sensor location (GPS
coordinates). Finally in
-
Section 4 conclusions are presented and open research is-sues
are discussed.
2 System Architecture - RequirementsThe SCIER system constitutes
an integrated platform of
sensors and computing infrastructure capable of
deliveringvaluable real time information regarding natural hazards
atthe “Urban-Rural-Interface” (Fig. 1). As an environmen-tal hazard
approaches, or has occurred in the URI region,SCIER will provide
the capabilities of both monitoring andpredicting its evolution.
Sensors spread in the region willmonitor environmental parameters
(e.g., temperature) andfeed with already fused data the predictive
models in thecomputing infrastructure. SCIER system is composed
bythree entities: the Sensing Subsystem, the Computing Sub-system
and the Localized Alerting Subsystem.
Computing Subsystem
(predictive models)
P
UrbanArea
RuralArea
P Publicly Owned SensorP Publicly Owned SensorC Citizen Owned
Sensor
PP
P
PC
C
P P
URI
P-LACU
R-LACU
P-LACU
Computing Subsystem
(predictive models)
Figure 1. SCIER system at the URI.
��� ��� ����� ������
In URI two kind of sensors are deployed: Citizen OwnedSensors
(COS), installed by land/home owners in fixed andregistered
locations in private areas, and Publicly OwnedSensors (POS),
installed by state authorities in fixed andknown locations in
public areas. In SCIER system the useof temperature, humidity and
air direction/speed sensors isadopted.
��� ��� �������� ������
This subsystem is based on GIS (e.g., region, URI)where the
fused sensor information is fed. Multiple math-ematical
environmental models of different time scales areused in order to
establish a highly accurate tracking of thehazardous phenomenon.
The output of the models will, incertain cases, re-feed the sensor
infrastructure, which willbe capable of reconfiguring itself to
adapt to the dynamicsof the observed phenomenon and allow its best
monitoring.
DataBase
Fusion Component
Alerting component
Sensor proxy
Commu-nicationproxy
Local Alerting Control Unit (LACU)
Computing Subsystem
Sensors
Figure 2. LACU architecture.
��� ��� �������� ������� �����������
LAS includes the Local Alerting Control Unit (LACU)which
controls an area of deployed sensors and performsfusion algorithms
for fire detection and fire location estima-tion. In SCIER, two
types of LACUs are identified (Fig.1):
� Public LACU (P-LACU). This type of LACU is in-stalled and
operated by public authorities and controlsa wireless network of
Public Owned Sensors.
� PRivate LACU (R-LACU). This type of LACU con-trols Citizen
Owned Sensors and is installed by indi-viduals in order to protect
their private properties.
LACU is a device which resides between the ComputingSubsystem
and the Sensing Subsystem as depicted in Fig.2. The basic
components that comprise LACU and theirrole are described in the
following paragraphs.
� Communication proxy, which is responsible for thecommunication
between LACU and Computing Sub-system.
� Sensor proxy, which receives and stores readings trans-mitted
by the sensors deployed in the monitored area.
� Fusion component, which assesses sensor data and de-termines
if a fire outbreak occurs in the area. Further-more, it estimates
the exact location of the fire.
� Alerting component, which is triggered by FusionComponent and
provides notifications to the Comput-ing Subsystem and to the users
(in case of an R-LACU)when an emergency situation occurs (e.g
fire).
� Data Base, which stores historical data, the identifi-cation
of each sensor and its location as well as thereadings arriving
from the Sensor proxy.
The topology of the WSN controlled by a LACU (public orprivate)
plays an important role in the fusion process. The
-
density of sensors depends on the area that is monitored andthe
desired accuracy of fire location estimation. For the firedetection
process an issue arises regarding the false alarmrate of the
system. A false alarm is the situation when thedetection algorithm
decides that there is a fire and actuallythere is not. It is clear
that if we need a reasonable num-ber of false alarms then the
detection latency of the systemincreases (i.e. it is possible to
not detect a fire). The falsealarm rate is parameterized and it
depends on the user re-quirements (in case of an R-LACU), on season
of the year(e.g. summer) and on the risk factor of the monitored
area.
3 Fire detection and Fire Location Estima-tion
The two problems of fire detection and fire location es-timation
are treated separately. Temperature sensors, scat-tered at the LACU
area, continuously monitor the environ-ment for abrupt temperature
changes. Upon a fire event de-tection, the sensors continue to send
their readings at theLACU where a fire location estimation process
follows.
��� ��� ��������
Several sequential detection techniques have been pro-posed in
the literature including Bayesian formulations [8]and the CUSUM
procedure[6]. In this paper we follow asimple approach formulating
detection as a binary hypoth-esis problem and using the Maximum
Likelihood (ML) cri-terion to decide upon the two events: No Fire
(Hypothesis0) and Fire (Hypothesis 1). That is,
�� � arg ����������
����� (1)
where � is the set of measurements and ����� is the p.d.f.of the
measurements under hypothesis �, We assume that����� follows a
Gaussian distribution
����� � � ��� ����and we attempt to model the parameters�� and
��
Let us consider � sensors placed ad hoc in the LACUarea, as
shown in Fig. 3, and let �� denote the radius ofa disk with center
the sensor �. Assuming disks of equalradius (�� � �), we can find
the minimum value of �, suchthat a full coverage of the LACU area
is obtained, that is
� � � �� s.t.c. �� � � �where � is the LACU area. This
calculation is possiblesince the position of the sensors is known
beforehand at theLACU. The value of � is critical regarding the
redundancyof the measurements and the detection latency. In the
ab-sence of a fire event the measurements of sensor � are mod-eled
as
�� � �� � � (2)
Sensor
Fire
LACU Area
Figure 3. Temperature sensors in the LACUarea.
where � is the sensor measurement noise, assumed to beGaussian
with zero mean and variance �� which is pro-vided by the sensor
manufacturer. Next we assume that � �is Gaussian with mean ���� and
variance �� , that is
�� � ������ �� � (3)The mean ���� depends on the hour of the
date and themonth of the year and can be estimated from statistical
data(stored in the database of LACU), empirical models,
fore-casting, or even the sensors themselves. Thus, ���� can
beobtained by a weighted average of all aforementioned esti-mation
techniques, such as
���� � ������� � ���
���� � ���� ��� � ���
�����
� � �� �� � �� � �� � ��
where the superscripts �� �� � and � denote empirical,
his-torical, forecasting and measured estimates respectively.For
example, Walters’ model ([9]) provides the “averageunit curve”
given by
���� � ���� ��� � �������� � ���� ���� � �������
���� ���� � �������
where ���� is the unit ordinate at time � (Local ApparentTime,
LAT), � is angular measure of the time of day, �(LAT), � � � ���, �
� ���. Using this formula alongwith
���� ������� ������ � ��
we can estimate the screen temperature ����� at time �.���� is
the maximum temperature, �� is the minimumtemperature of the day
and �� is the mean of the 24 hourlytemperatures. For these
parameters we can use statisticaldata or the previous day values.
For example if ��� �����, ��� � ���� and �� � ��� we obtain the
dailyvariations of temperature of Fig. 4, which shows that
tem-perature is higher at 2:00-3:00 pm. For the estimation of�����
by the sensors themselves, we drop the � highest
-
0 5 10 15 20 24
10
15
20
25
30
35
Time of day (h)
Tem
pera
ture
Figure 4. Empirical estimation of ����
and lowest temperature measurements out of the � avail-able and
we estimate ����� by
����� ��
� � ��
��������
���
where ��� are the ordered (in increasing value)
temperaturemeasurements. This averaging can be considered as a
sim-ple data fusion process.
The parameter � in (3), controls the variations (i.e.clouds,
rainy day) from the mean value ����. Next sup-pose that we have �
measurements ���� ��� � � � � � fromdifferent nodes available to
us. Consider the �-dimensionalvector
� � ���� ��� � � ��Under Hypothesis 0 (no fire event) this
vector is Gaussiandistributed with mean�� � ������ � � � � ������
and covari-ance matrix ��. The covariance matrix has the form
�� �
�����
�� �
�� ���
�� ����
���
��
�� �
�� ����
......
. . ....
��
�� ��
�� �� � ��
�����
It should be noted that in the absence of fire the sensor
mea-surements are highly correlated especially for the
nearbysensors. That is the values �� should be close to 1. We
canassociate correlation coefficients based on the distance oftwo
sensors, or even estimate the matrix �� from the sensormeasuements
through the formula
��� ��
�����
��� ������� �����
Next we model the mean and the variance of the
Gaussiandistribution of the measurements given Hypothesis 1
(Fireevent). In this case the measurements of sensor � take
theform
�� � �� � �� � � (4)
where �� and � have been defined in the no-fire case, andthe
random variable �� measures the excess temperature dueto fire. This
variable is modeled as Gaussian with mean����� and variance �� ,
that is
�� � � ������� �� � (5)It should be noted that the mean ����� is
a function of thedistance of the sensor from the fire front. If
heat radiationis the main phenomenon, then only the sensors that
are inthe vicinity of the fire event will detect fire. In view of
thismatter we consider a heat radiation model of the form
����� ���
�� � ��
(6)
where �� is the excess temperature at the fire location, �is the
distance of the sensor from the fire front and � anexponent which
can be determined through physical lawsand/or simulation. To this
end we consider a fire model de-scribed by the following system of
partial differential equa-tions which is derived from the
conservation of energy, bal-ance of fuel supply and fuel reaction
rate [1][5].
��
� � � �!�� � � " �� �#$��������� � ��� � ��
�$
� � ���$���������� � � � (7)
where � and $ denote temperature and fuel mass
fractionrespectively, with the initial conditions $ � �� � �, � �
�� ���. The diffusion term � �!�� � models short-range heattransfer
by radiation, " �� models heat convected by thewind, ������ is the
heat lost to the atmosphere (� is theambient temperature),
��$��������� is the rate of fueldisappearance due to burning,
whereas #$��������� isthe rate of heat generation due to burning.
Fig. 5, showsan example of the propagation combustion wave and
fuelsupply mass fraction under no wind conditions (" � ) and! �
������ �����������, # � ������� ������,� � ������ � ���, � � ���� �
������, �� ���������, � � �
� and �� � �
�. The values of #and �� are an order of magnitude greater than
those usedin [5] thus resulting in an extremely fast fire spread
model.
Fig. 6 shows the temperature profile after 30 secondsfrom fire
ignition at �� � for the parameters of the firemodel of Fig. 5. As
it is observed the fire front exhibits asteep fall from 1000 K, at
position�����, down to 330 K,at �����. That is, the temperature
sensors will increasetheir readings by ��� if the fire front is ��
away. Forthe extreme fire spread model considered, this distance
willbe covered in about �� which is enough for the sensor
totransmit several measurements before being burnt. For thisfire
spread model we can estimate the exponent � in (6) tobe equal to
���.
-
Tem
pera
ture
(K)
Fuel
mas
s fra
ctio
n
Distance (m)
Distance (m)Time (sec)
Time (sec)
Figure 5. a) Propagating combustion wave b)Fuel supply mass
fraction
-50 -40 -30 -20 -10 0 10 20 30 40 50300
400
500
600
700
800
900
1000
1100
Tem
pera
ture
(K)
Distance from ignition point (m)
Figure 6. Temperature profile after �� fromignition.
Since each sensor monitors an area of radius �, the dis-tance �
from the fire ignition point to the sensor is a randomvariable with
pdf
����� ���
��
Therefore, in view of equations (5), (6) we obtain
�������
� ��
���%�
���
�
�� � ��
�� � ��
��&���
���
����
A simplification is obtained if we substitute ����� in (5) byits
mean value
�� �
� ��
������� �����
��� ������
�� � ��� ������
�� � ��
��� ����� ��Under these conditions �� in (4) is Gaussian
distributed withmean ������� and variance �� �
�� �
��. Due to the fact
that the number of sensors that detect fire varies (dependingon
fire spread and spotting), the estimation of the joint
dis-tribution of the measurement vector � � ���� ��� � � � � �
��
is very complicated. Therefore, we resort to a subopti-mal
decision-fusion classifier where all measurements aretreated as
independent. For each sensor a decision is madefor the existence of
fire in its control territory using the MLcriterion and the
distributions of �� when it takes the formin (2) and (4)
respectively. ROC curves for different valuesof � are provided in
Fig. 7. Given a desirable false alarm
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False Alarm
Posi
tive
dete
ctio
n
R=10 m
R=20 m
Figure 7. ROC curves (���� � �
�, � � �,
� � ��, � � �, � � ���, �� � �
�).
rate and knowing the radius � for each node a thresholdcan be
set to decide for a fire event in its territory. The de-tection
probabilities are assessed at the LACU for the finaldecision
fusion. This scheme enables LACU to make deci-sions no matter how
many fire spots exist in its area. More-over, LACU does not have to
recalculate the likelihood ofthe vector � if some of the sensors is
known to be burnt ormalfunctioning.
��� ��� ������� �������
Assuming a homogeneous fuel terrain and no wind con-ditions the
spread of fire will be circular. Moreover, if theWSN is sparse and
the fire starts close to a sensor '� then,due to the sharp leading
edge of fire temperature profile,only this sensor will notice a
temperature increase. Thus, atthis time instant �, the fire front
is known at the LACU to belocated in a small disk around sensor '.
As the fire spreads,another sensor '� will sense the temperature
increase at alatter time �. Needless to say that sensor ' may be
burntby this time, but its measurements at time � have
beentimestamped and stored at the LACU. A typical situationis
depicted in Fig. 8 in case that the fire started in the con-vex
hull of the sensors. Knowing the characteristics of the
-
-50 -40 -30 -20 -10 0 10 20 30 40 50A B
Sensor j Sensor i
Figure 8. Combustion wave reaching twosensors.
combustion wave, i.e. speed of propagation "� , LACU is
inposition to estimate the distance AB as ��� � "� � � �
��.Therefore the fire ignition point lies on the line normal to
themidpoint of AB. A third sensor reading is needed to com-plete
the triangularization process and find the fire startingpoint.
Moreover, LACU is able to produce contours of fireprobabilities and
zones of burnt sensors which can be usedto get an estimate of the
fire front and the ignition point re-spectively. For example, if
sensor nodes '�� '�� � � � � '� areburnt at time instants �� �� � �
� � � ( � � � � � �)the fire starting point is esimated via
���� (�� �
����
���� (���
� � �
where the weights � are larger for these nodes burnt earlier.On
the other hand knowing the fire front at various timeinstants based
on increased sensor measurements helps toestimate the fire spread
speed and direction. NeighboringLACUs can be benefit from these
estimates increasing thesampling rate of their sensors and their
awareness status.
4 Conclusions and Future WorkIn this paper we presented the
functionality of the Lo-
cal Alerting Control Unit (LACU) component, as a part ofthe
overall SCIER architecture, regarding the early detec-tion of
potential fires and the fire location estimation pro-cess. We
propose a method for fire detection on an areacovered by
temperature sensors. Sensor readings are pro-cessed using the
Maximum Likelihood (ML) criterion todecide upon the two events
(Fire and No Fire). Moreover,the fire location estimation method
that is proposed helpsdetermining the fire ignition point and the
fire front. Suchinformation is obtained by fusing multiple sensor
readingsand knowing the location of each sensor. Knowledge ofthe
fire front helps in adapting the data acquisition process
(e.g. increase sampling rate) and in alerting neighboringLACUs
that may be affected by the fire. Currently, openresearch issues
are investigated in the context of LACU andespecially in the design
and implementation of the fusioncomponent. Besides temperature,
there are also other typesof sensors that can be deployed in the
area and contributeto the fusion process. Humidity sensors offer an
alternativeway for checking and evaluating the air temperature
mea-surements; in general air humidity declines as air tempera-ture
arises. Modelling this relationship we will be capableto confirm
temperature readings, thus eliminating the prob-ability of taking
into account the data sent from a defectivesensor. Furthermore, the
speed and direction of the windthat flows in the monitored area and
the non-homogeneousterrain affects the spread model of the fire.
Incorporatingsuch information, the fire models become more
realistic andthe fire detection and fire location estimation
process moreaccurate.
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