A framework for use of wireless sensor networks in forest fire detection
and monitoring
Yunus Emre Aslan, Ibrahim Korpeoglu
⇑
, Özgür Ulusoy
Bilkent University, Department of Computer Engineering, 06800 Ankara, Turkey
article info
Article history:
Available online 4 April 2012
Keywords:
Wireless sensor networks
Forest fire detection
Environmental monitoring
abstract
Forest fires are one of the main causes of environmental degradation nowadays. Current surveillance sys-
tems for forest fires lack in supporting real-time monitoring of every point of a region at all times and
early detection of fire threats. Solutions using wireless sensor networks, on the other hand, can gather
sensory data values, such as temperature and humidity, from all points of a field continuously, day
and night, and, provide fresh and accurate data to the fire-fighting center quickly. However, sensor net-
works face serious obstacles like limited energy resources and high vulnerability to harsh environmental
conditions, that have to be considered carefully. In this paper, we propose a comprehensive framework
for the use of wireless sensor networks for forest fire detection and monitoring. Our framework includes
proposals for the wireless sensor network architecture, sensor deployment scheme, and clustering and
communication protocols. The aim of the framework is to detect a fire threat as early as possible and
yet consider the energy consumption of the sensor nodes and the environmental conditions that may
affect the required activity level of the network. We implemented a simulator to validate and evaluate
our proposed framework. Through extensive simulation experiments, we show that our framework
can provide fast reaction to forest fires while also consuming energy efficiently.
Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Forest fires are a fatal threat throughout the world. It is reported
that for the last decade, each year, a total of 2000 wild fires hap-
pened in Turkey and more than 100,000 in all countries (Republic
of Turkey, 2011). Early detection of forest fires is very important
in fighting against fires. Spread features of forest fires show that,
in order to put out a fire without making any permanent damage
in the forest, the fire fighter center should be aware of the threat
in at most 6 min after the start of the fire (National Fire Danger Rat-
ing System (NFDRS), 2011). Besides early detection capability, esti-
mating the spread direction and speed of fire is also important in
extinguishing fires.
Unreliability of human observation towers, in addition to the
difficult life conditions of fire lookout personnel, has led the devel-
opment and use of various technologies aiming to make the fire
fighters aware of the forest fires as early as possible. Some impor-
tant technologies and systems that are currently used towards this
goal are: systems employing charge-coupled device (CCD) cameras
and infrared (IR) detectors, satellite systems and images, and
wireless sensor networks.
In a camera based system, CCD cameras and IR detectors are
installed on top of towers. In case of fire or smoke activity, the cam-
eras and detectors sense this abnormal event and report it to a
control center (Aerovision Web Page, 2011; B.C. Fire Lookout Tow-
ers, 2011; Toreyin, Dedeoglu, Gudukbay, & Cetin, 2006). However,
the accuracy of such a system is highly affected by terrain, time of
day, and weather conditions such as clouds, light reflections, and
smoke from innocent industrial or social activities. Another alterna-
tive technology for detecting forest fires is the use of satellites and
satellite images. Usually, satellites provide a complete image of the
earth every 1–2 days. This long scan period, however, is not accept-
able for detecting forest fires quickly. Additionally, the smallest fire
size that can be detected by such a system is around 0.1 hectare,
which also prevents fire detection just at the time when the fire
starts, and fire localization error is about 1 km, which is not very
accurate.
As a promising alternative, wireless sensor networks (WSNs)
are an emerging technology that can be used for forest fire detec-
tion and related activities (Akyildiz, Su, Sankarasubramaniam, &
Cayirci, 2002; Doolin & Sitar, 2006; Son, 2006; Yick, Mukherjee,
& Ghosal, 2008; Yu, Wang, & Meng, 2005). A wireless sensor net-
work consists of small, battery-powered, and low-cost sensor
nodes that have the capability of sensing, processing, and wireless
communication (Shyam & Kumar, 2010). Wireless sensor nodes
that are deployed to a forest can collect data such as temperature,
humidity, barometric pressure, and deliver this highly important
0198-9715/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.compenvurbsys.2012.03.002
⇑
Corresponding author. Tel.: +90 312 290 25 99; fax: +90 312 266 4047.
E-mail addresses: aslany@cs.bilkent.edu.tr (Y.E. Aslan), korpe@cs.bilkent.edu.tr
(I. Korpeoglu), oulusoy@cs.bilkent.edu.tr (Ö. Ulusoy).
Computers, Environment and Urban Systems 36 (2012) 614–625
Contents lists available at SciVerse ScienceDirect
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data in raw or processed form to a center via a base station (or sink
node), where incoming data can be analyzed automatically. As a
result, fires and some other related events can be detected at the
center without requiring manual, human-centric operations. There
are, however, a lot of issues to consider and resolve in using wire-
less sensor networks for forest monitoring and forest fire detection.
For example, the limited energy resources of sensor nodes and the
though environmental conditions can limit the success of forest
fire detection systems that are based on wireless sensor networks.
Constant surveillance of the whole forest is required and this may
cause excessive energy usage if not carefully planned. Therefore, a
wireless sensor network for forest fire detection should consider
several parameters and trade-offs together.
In this paper, we present a framework for the design of a wireless
sensor network for forest monitoring and fire detection considering
several goals simultaneously. As a part of our framework, we pro-
pose a network architecture and related protocols that will enable
both rapid detection of forest fires and cautious use of energy
resources. In our design, when there is no fire, the sensor network
is not very aggressive in sensing and communicating various sen-
sory data. But when there is a fire threat, the network operates in
an emergency mode, and senses and communicates as fast as possi-
ble. Similarly, since the risk of fire depends on terrain, season and
current weather conditions, our proposed design adapts its opera-
tion mode to the current level of risk of fire. In our proposed system
design, except for the periods of forest fire, the sensor nodes mostly
work in a low-duty cycle mode (regular day conditions). That is, sen-
sor nodes will not consume much energy while the environmental
conditions are normal and there is no fire. A distributed protocol
to run in each sensor node considers fire threat cautiously and in
case of an abnormal temperature change, informs the control center
about the possibility or occurrence of fire rapidly.
Our framework includes four major components: an approach
for deployment of sensor nodes, an architecture for the sensor net-
work for fire detection, an intra-cluster communication protocol,
and an inter-cluster communication protocol. We simulated our
proposed design to show the validity of the protocols and to eval-
uate the proposal. We report the simulation results and show how
the proposed framework can adapt to changing risk levels and in
this way use the energy resources efficiently without harming
the effectiveness of the system in detecting fires quickly.
The remainder of this paper is organized as follows. Section 2 dis-
cusses related studies on forest fire detection with wireless sensor
networks. Section 3 describes the proposed framework that includes
four major components mentioned above. Section 4 presents our
simulation environment and experimental results. Finally, Section
5 concludes the paper and provides a discussion on future work.
2. Related work
During the last decade, quite extensive research work has been
performed on wireless sensor networks, their protocols and algo-
rithms, and their applications (Akyildiz et al., 2002; Anastasi, Conti,
Di Francesco, & Passarella, 2009; Huang, Tseng, & Wu, 2007; Main-
waring, Culler, Polastre, Szewczyk, & Anderson, 2002; Yick et al.,
2008). Although these studies are not targeting specifically fire
detection application, the approaches proposed are adaptable to
various applications, including fire detection and monitoring. In
our work, we adapt some of these existing methods (like cluster-
ing) and integrate various approaches in the literature to come
up with a WSN design specifically targeting energy-efficient and
effective forest fire detection.
For fire detection application of wireless sensor networks, spe-
cifically, there has been a considerable amount of work carried out
as well. In one study, Doolin and Sitar (2006) provide experiments
through controlled fires in San Francisco area. Their system is com-
posed of ten sensor nodes with GPS capability. The sensor nodes
are deployed with ranges up to one kilometer and they sense
and forward temperature, humidity and barometric pressure val-
ues to a base station. The system was implemented and real-world
observations were gathered from the field. However, because of
the long distances between sensor nodes, the data arriving to the
sink is not valuable enough to detect a fire quickly and forecast
the spread direction of the fire. Also, with the growth of fire and
burning out some of the sensor nodes, the sensor network could
fail in delivering the data from all sensor nodes to the base station.
Lloret, Garcia, Bri, and Sendra (2009) use a wireless local area
network (WLAN) together with sensor-node technology for fire
detection. The system they propose mixes multi-sensor nodes with
IP-based cameras in a wireless mesh network setting in order to
detect and verify a fire. When a fire is detected by a wireless mul-
ti-sensor node, the alarm generated by the node is propagated
through the wireless network to a central server on which a soft-
ware application runs for selecting the closest wireless camera(s).
Then, real time images from the zone are streamed to the sink.
Combining sensory data with images is the most important contri-
bution of this study.
Hartung and Han (2006) developed a multi-tiered portable
wireless system for monitoring environmental conditions, espe-
cially for forest fires. Integrating web-enabled surveillance cameras
with wireless sensor nodes, the system can provide real-time
weather data from a forest. Three different sensor networks are de-
ployed to different parts of a forest and the communication be-
tween the networks is enabled by powerful wireless devices that
can send data up to ten kilometers range. The objective of the
study is to determine the behavior of forest fires rather than their
detection. With a wireless sensor network around an active fire,
they measure the weather conditions around the fire. Webcams
are also used to get visual data of the fire zone. Data gathered from
the sensor nodes and the webcams are aggregated at a base station
which has the capability of providing long distance communication
using satellites. Periodically, the sensor nodes measure the temper-
ature, relative humidity, wind speed and direction, and web-cams
provide continuous visual data to the base station.
In all the studies discussed above (Doolin & Sitar, 2006; Hartung
& Han, 2006; Lloret et al., 2009), the sensor nodes are deployed to
have quite large distances between each other and the sensory
data gathered at a center is supported with visual data obtained
with cameras. Our proposed system, however, considers a denser
deployment strategy where the distances between neighboring
sensor nodes are quite short. In this way, we are aiming to detect
forest fires in a much faster way and send the related information
to a center as quickly as possible.
Son (2006) propose a forest fire surveillance system in South
Korea in which a dynamic minimum cost path forwarding protocol
is applied. After gathering data, a sink node makes several calcula-
tions regarding the relative humidity, precipitation and solar radi-
ation, and produces a forest fire risk level. Different from this
study, we propose to do in-network processing in cluster-head
nodes rather than doing calculations only at a sink node. In this
way, in our system, a sink node gathers filtered and processed data,
not just raw data. Additionally, Son (2006) applies a minimum cost
path forwarding method that causes some sensor nodes (especially
the ones that are closer to the sink) to consume their energy much
faster than the others. Our system, on the other hand, applies a low
and fair energy consumption strategy by use of appropriate intra-
and inter-cluster communication protocols which take the remain-
ing energy levels of sensor nodes into account.
Yu et al. (2005) present a method which applies neural network
techniques for in-network data processing in environmental sens-
ing applications of wireless sensor networks. Several data fusion
algorithms are presented in their study. Maximum, minimum
Y.E. Aslan et al. / Computers, Environment and Urban Systems 36 (2012) 614–625
615
and average values of temperature and humidity data are calcu-
lated by the cluster-heads. Data are propagated to the sink only
if a certain threshold is exceeded. The main focus of this study is
data aggregation methods, hence energy consumption and forecast
capability issues are not discussed.
Ngai, Zhou,Lyu,andLiu (2010)providea general reliability-centric
framework for eventreporting in wirelesssensor networks which can
also be used in forest fire detection systems. They consider the accu-
racy, importance and freshness of the reported data in environmental
event detection systems. They present a data aggregation algorithm
for filtering important data and a delay-aware data transmission
protocol for rapidly carrying the data to the sink node.
Wenning, Pesch, Giel, and Gorg (2009) propose a proactive rout-
ing method for wireless sensor networks to be used in disaster
detection. The protocol is developed to be aware of a node’s
destruction threat and it can adapt the routes in case of a sensor
node’s death. The method can also adapt the routing state based
on a possible failure threat indicated by a sensed phenomenon.
Hefeeda and Bagheri (2009) developed a wireless sensor net-
work for forest fire detection based on Fire Weather Index (FWI)
system which is one of the most comprehensive forest fire danger
rating systems in USA. The system determines the spread risk of a
fire according to several index parameters. It collects weather data
via the sensor nodes, and the data collected is analyzed at a center
according to FWI. A distributed algorithm is used to minimize the
error estimation for spread direction of a forest fire.
Garcia and Serna (2008) present a simulation environment that
can create a model for a fire by analyzing the data reported by sen-
sor nodes and by using some geographical information about the
area. The use of topography of the environment distinguishes the
study from some other solutions. The estimation of the spread of
a fire is sent to hand-held devices of fire fighters to help them in
fighting against the fire in field.
The studies described above (Hefeeda & Bagheri, 2009; Ngai
et al., 2010; Son, 2006; Wenning et al., 2009; Yu et al., 2005) con-
sider and handle a single aspect of environmental monitoring and
forest fire detection. In our proposed system, on the other hand, we
deal with multiple parameters and trade-offs. We consider and aim
both energy-efficiency and early-detection. We also incorporate
environmental conditions, obstacles and features in our protocols.
There has been also a significant amount of work performed for
clustering in wireless sensor networks (Abbasi & Younis, 2007; Ci,
Guizani, & Sharif, 2007; Dimokas, Katsaros, & Manolopoulos, 2010;
Heinzelman, Chandrakasan, & Balakrishnan, 2000; Liu, Lee, &
Wang, 2007; Machado, Zhang, Wang, & Tekinay, 2010; Park, Choi,
Han, & Chung, 2009; Soro & Heinzelman, 2009). These works, how-
ever, consider mostly how clusters are formed and maintained for
various applications. They are not focusing on use of clusters for
fire detection application. Therefore, their focus and scope are
much different than the cluster communication protocols we are
proposing in this paper. Moreover, in this paper we are not concen-
trating on how clusters are formed, but on how clustered hierarchy
is utilized in a most efficient and effective manner for detecting
and monitoring fires.
3. Proposed fire detection system design
In this section, we describe our WSN-based fire detection sys-
tem. We first identify the following as some of the important de-
sign goals and features that a wireless sensor network should
have in order to be able to successfully monitor a forest and detect
fires.
1. Energy efficiency: Sensor nodes are powered with batteries,
therefore a wireless sensor network deployed for fire detec-
tion should consume energy very efficiently. Energy
consumption should also be balanced fairly among nodes.
Usually the deployment area is very large and thousand of
sensor nodes may be needed, and therefore replacing bat-
teries may be too costly, impractical or even not possible.
2. Early Detection and Accurate Localization: It is important to
detect a forest fire as early as possible and to estimate the
fire location with high accuracy. A forest fire usually grows
exponentially and it is crucial that the fire should be
detected and interfered in about six minutes to prevent
the fire from spreading to a large area (National Fire Danger
Rating System (NFDRS), 2011). Accurately estimating the
fire position is important to send the fire fighting personnel
to the correct spot in the shortest possible amount of time.
3. Forecast Capability: Being able to forecast the spread direc-
tion and speed is important for planning fire fighting, being
proactive in mobilizing resources, and warning the sur-
rounding area. Accurate forecasting requires accurate and
fresh sensory data to arrive at the decision and control cen-
ter from all points of the forest, especially from and around
the region where the fire has occurred (i.e., critical zones).
4. Adapting to Harsh Environments: A sensor network for forest
fire detection will operate usually in harsh environments
and therefore should be able to deal with and adapt to harsh
conditions. It should be able to recover from node damages,
link errors, high temperature, humidity, pressure, etc.
Our aim in this work is to consider the above goals as much as
we can in designing a wireless sensor network for fire detection.
Besides these goals, there may be some other crucial requirements
for a WSN designed for fire detection, such as providing security,
coping with vandalism, incorporating self-healing mechanisms,
and being able to self-organize. We do not consider these require-
ments in this work and leave them as future work issues.
Our proposed framework involves the design of four main
parts: (1) a sensor deployment scheme, (2) a clustered network
architecture, (3) an intra-cluster communication protocol, and (4)
an inter-cluster communication protocol. Next, we describe the de-
sign of each of these parts in more detail.
3.1. Sensor deployment scheme
The sensor node deployment scheme can affect the design and
performance of all aspects of the system. In a deployment scheme,
there are two major decisions to make: (1) What should be the aver-
age distance between neighboring sensor nodes? (2) What should be
the deployment pattern or distribution (random or a regular pat-
tern)? The requirement for low and balanced energy consumption,
early detection, desire to achieve low channel contention, properly
covering the region, the terrain and other parameters of the forest
should be considered in making those decisions.
The average deployment distance between neighboring sensor
nodes is an important parameter that affects the performance of
a wireless sensor network deployed for fire detection. The time
to detect a temperature increase at a node due to a fire is related
with the distance of the node to the fire ignition location. There-
fore, in order to reduce the expected fire detection time, the aver-
age distance between neighboring sensor nodes should be reduced.
But this may contradict with the goal of reducing collisions which
is expected to happen more when a network becomes denser.
Hence, there is a trade-off between reducing the fire detection time
and collision probability.
Some studies about spread characteristics of forest fires show
that the time required for a sensor node to be aware of fire depends
also on the environmental conditions like the fuel type of the
forest, the ignition level, the slope of the location and the power
of wind (Morvan et al., 2002; Washington State University,
616 Y.E. Aslan et al. / Computers, Environment and Urban Systems 36 (2012) 614–625
2011). The effects of such environmental factors on forest fires are
investigated in National Fire Danger Rating System (NFDRS)
(2011). NFDRS calculates a fire spread component (SC) value for
a forest, which represents the forward spreading rate of a fire in
meters per minute and which depends on fuel model of the forest,
wind speed and slope of the zone.
Inspired by NFDRS, in our system, while determining the appro-
priate average distance between neighboring sensor nodes, we
consider an importance value (I) for the forest as a parameter. The
I value of a forest depends on how important the forest is to protect
from fires. For example, a forest surrounding a cultural heritage
site may be considered to be more important than a forest that is
on top of a mountain. The importance value also depends on the
spread component of the forest. A forest with higher fire danger
rate, i.e. with a larger SC value, is considered again to have a larger
importance value. The required maximum fire detection time (T,in
seconds), the initial energy of sensor nodes (E, in Joules), and the
required network lifetime (N, in seconds) are some other parame-
ters that may affect the decision of what the average distance be-
tween neighboring sensor nodes should be. Considering all these
different parameters into account, we propose the following
approximate formula to determine the average distance d (in me-
ters) between neighboring sensor nodes:
d ¼
a
ET
NI
2
ð1Þ
where
a
is a normalization factor determined empirically. As seen
from the formula, we propose the average distance to be propor-
tional to initial energy level of nodes (E) and the required fire detec-
tion time (T), and to be inversely proportional to the required
network lifetime (N) and the square of the importance value (I)of
the forest. I is a unit-less parameter that can have a value between
1 and 10 (1: not important at all; 10: of maximum importance), and
we propose squaring the I value to have more effect on the result
compared the other factors. The unit of
a
is meter/Joule. Note that,
the value of
a
is to be found experimentally, which is not focused in
this paper.
As mentioned earlier, another important factor that affects the
performance of a fire detection WSN system is the deployment pat-
tern of sensor nodes. Two general approaches can be considered to
define the deployment pattern: (1) regular deployment, or (2) ran-
dom deployment. In case of regular (homogeneous) deployment,
nodes are deployed according to a regular pattern and we have
nearly equal distance between neighboring nodes. Therefore, all
sensor nodes transmit their messages to similar distances. This
leads to balanced transmit energy consumption throughout the
network. In random (non-homogeneous) deployment, nodes are
deployed randomly (from a plane maybe) without following a reg-
ular pattern, hence the distance between two neighboring nodes is
a random value, which may or may not be uniformly distributed. In
this case, some sensor nodes may have quite distant neighbors and
therefore may have to transmit to longer distances. Since the trans-
mit energy consumption increases exponentially with the distance,
those sensor nodes will consume much more energy due to trans-
missions and therefore will run out of energy earlier. Transmitting
to longer distances to reach to some neighbors may also increase
the interference on other nodes and may cause an increase in the
collision probability. Additionally, an increased distance between
neighboring nodes at some locations of the forest may increase
the fire detection time at those locations.
As regular deployment pattern alternatives, two popular layout
models are proposed: square layout and hexagonal layout (Lloret
et al., 2009). In square model, the region is considered to be divided
into squares (a grid of squares) and sensor nodes are placed at the
corners of squares and cluster-heads are placed in the centers of
squares. In such a deployment, the maximum distance between
the fire ignition location and the closest sensor node will be
a
ffiffi
2
p
,
where a is the side-length of the squares.
In hexagonal layout, the region is considered to be divided into
hexagons. Sensor nodes are placed at the corners of the hexagons
and cluster-heads at the centers. In this case, the maximum dis-
tance between a fire ignition location and the closest sensor node
will be
b
2
, where b is the distance between two far most corners
of a hexagon. Sample deployments according to square and hexag-
onal patterns are shown in Figs. 1 and 2, respectively. The square
layout has less sensor nodes per cluster compared to the hexagon
layout. Therefore, each cluster-head is less loaded, but for a fixed
number of sensor nodes, it needs more cluster-heads. With less
sensor nodes per cluster, the congestion will be managed better
in the square model. It is also more robust layout due to having
more cluster-heads. Therefore, we prefer square layout and use it
in our simulations.
With irregular (random) deployment, we cannot guarantee a
maximum distance between a fire ignition location and the closest
sensor node. Therefore, we expect the distance between a fire igni-
tion location and the closest sensor node to be higher in random
deployment. So, regular deployment is preferable if it is possible
to do so.
Even though we may want to deploy according to a regular pat-
tern, however, considering the geography of the region, it is highly
possible that in some cases we may not be able to deploy all sensor
nodes with a regular grid pattern. There will be some nodes which
have to be deployed to distant locations from other sensor nodes
because of the geography of the area (for example, because of a
small lake inside the forest). Those distant sensor nodes will have
to send their messages to longer distances and therefore will con-
sume more energy than the other nodes. In order to remedy the
problem to some degree, those sensor nodes may be deployed with
higher initial energy levels if possible.
3.2. Network architecture and topology design
Efficient and effective operation of a WSN depends also on the
architecture and logical topology of the network. We designed
the architecture and logical topology of our WSN considering the
goals of a fire detection system and limitations of wireless sensor
nodes.
There are two possible alternatives for the network topology:
flat and hierarchical. In flat topology, sensor nodes run in a totally
distributed manner with equal responsibilities. In a hierarchical
clustered topology, some nodes are designated as cluster-heads
Fig. 1. A sample square layout network architecture.
Y.E. Aslan et al. / Computers, Environment and Urban Systems 36 (2012) 614–625
617
with more responsibility to control other member (ordinary)
nodes. We performed several tests (described later in Section
4.2.2) and observed that use of a clustered topology provides
important advantages for fire detection application of sensor net-
works. Hence, we propose a clustered logical topology for the net-
work to properly and adaptively control the sensor nodes under
various conditions. Clustered topology has benefits in terms of
achieving effective control of nodes depending on changing condi-
tions, rapid reaction to fire threat, and energy and bandwidth effi-
ciency. It also enables data aggregation or data fusion (Hall &
Llinas, 1997) to be performed at well-designated nodes, i.e. clus-
ter-heads. In this way, the volume of traffic carried inside the net-
work can be reduced and faster reaction to urgent events can be
done. This is especially useful for fire detection applications, be-
cause most of the time the maximum temperature from a region
is needed instead of individual temperature values from all sensor
nodes. Moreover, cluster-heads can apply smart scheduling and
adaptive transmissions to reduce the load on sensor nodes closer
to the sink.
In a clustered topology, a specific number or percentage of sen-
sor nodes (where this depends on some system parameters and
deployment) will form a group (a cluster) and connect to a clus-
ter-head which will have some additional responsibilities. The
cluster-heads may have superior physical capabilities as well, such
as being equipped with a GPS module or having larger memory,
processing, and energy resources. They should also have the capa-
bility to adjust their transmit power to transmit to longer distances
when necessary. An example illustration of the clustered network
architecture is shown in Fig. 1.
3.3. Environment aware intra-cluster communication protocol
In a clustered network architecture, protocols for intra-cluster
communication and inter-cluster communication have to speci-
fied. In this section we describe our intra-cluster communication
protocol (communication inside a cluster), and in the next section
our inter-cluster communication protocol (communication among
the cluster-heads).
WSN protocols should be designed to be adaptive to the current
environmental conditions, like the current season or the current
daily average temperature, and also to whether there is a fire
threat at the moment or not. In times when there is no fire and
the risk of fire is quite low, the network should aim to decrease
the message overhead throughout the network and the data should
be forwarded to the sink with minimum cost, so that less energy is
consumed at sensor nodes. This should be done, of course, without
compromising the fire detection capability. In a possible fire threat
time or as the fire spreads, however, energy optimization will be a
less critical goal for the network, and reacting to fire rapidly and
delivering data to sink as fast as possible will be a more critical is-
sue. Therefore, we designed our cluster communication protocols
to be adaptive to changing environmental and weather conditions
and whether there is fire threat at the moment.
Our cluster communication protocols are different than the
clustering work in literature, because our clustering protocols are
designed specifically to be effective and efficient for fire detection
and monitoring. We are more concerned about how a clustered
topology is employed, operated and utilized rather than how topol-
ogy is formed. Our communication protocols have unique features
designed for fire detection applications, such as having adaptive
mechanisms to react to fires quickly and energy-efficiently.
Our intra-cluster communication protocol, that provides com-
munication in a cluster among the cluster members and the clus-
ter-head, consists of four phases: initialization phase (which
involves also defining message sending sequence), risk-free time
(regular time) phase, fire-threat (fire-time) phase, and progressed-
fire phase. Each phase is implemented via a set of messages ex-
changed between a cluster-head and its member nodes. Next, we de-
tail the actions performed in each phase.
When booted up, sensor nodes start in the initialization phase.In
this phase, member nodes of a cluster are initialized and set up to
connect to their cluster-heads. Since the focus of our paper is not
formation of clusters, we assume that the clusters are statically
formed and configured. We leave a dynamic clustering approach
for fire detection to be out of scope of the paper and as a future
work.
When a cluster-head has all members connected, it assigns a
data message sending sequence to be followed by the member
nodes to coordinate access to the shared wireless channel and
avoid collisions. This sequence (time slot information) of each
member is sent to the member along with a frequency (duration)
parameter which indicates how frequently a sensor node will send
data messages to its cluster-head. This frequency is a dynamically
adjusted parameter that depends on the current fire danger rate
calculated by each cluster-head. It is a time and space dependent
parameter. The current value of the fire danger rate at a cluster-
head indicates the risk of fire at that time and at that location. A
higher rate will cause more frequent sending of data messages
from the sensor nodes to the cluster-head.
Additionally, a cluster-head sends information about fire
threshold levels to each of its connected nodes. Using these thresh-
olds sensor nodes can determine the current risk level of fire. After
a cluster-head sends all the required initialization information to
the connected nodes, the next phase starts at the cluster, which
is the risk-free time phase.
Nodes are in risk-free time phase during the times when the fire
risk is low. At those times, the system adapts itself by decreasing
its activity level, so that it can achieve energy efficiency without
compromising the fire detection capability. The frequency of send-
ing temperature data from sensor nodes to a cluster-head is
lowered.
Additionally, in this phase sensor nodes can be put into sleep
mode for a while in order to save more energy. The nodes in a clus-
ter can be put into sleep in a Round Robin fashion, so that a bal-
anced energy consumption is achieved. In sleep more, the
sensing frequency of a sensor node is set to be very low. Sleep per-
iod and activity level can be made location-dependent, i.e., adap-
tive to the fire danger rate of a region.
When the temperature or humidity level exceeds the config-
ured threshold level at a sensor node, the fire threat phase is started
Fig. 2. A sample hexagonal network architecture.
618 Y.E. Aslan et al. / Computers, Environment and Urban Systems 36 (2012) 614–625
