COMPUTATIONAL INTELLIGENCE APPLIED TO WILDFIRE

PREDICTION USING WIRELESS SENSOR NETWORKS

V. G. Gasull

, D. F. Larios

, J. Barbancho

, C. León

and M. S. Obaidat

Department of Electronic Technology, University of Seville, Seville, Spain

Department of Computer Science & Software Engineering, Monmouth University, W. Long Branch, NJ 07764, U.S.A.

Keywords: Fuzzy system, Wireless sensor networks, Forest fire, Simulation.

Abstract: In this paper we present a novel algorithm, implemented in a wireless sensor network environment, for the

study of fire in a natural environment. This algorithm can offer two kinds of information: the risk of a fire

and, in case of an active fire, the propagation direction of the flames and their velocity. This information is

useful for fire rangers in order to obtain escape ways and determine strategies for putting off the fire. The

evaluation of the proposal algorithm is performed using an ad-hoc simulator. The preliminary results

obtained show that the proposed algorithm is a promising method that can be used to solve the most

common problems related to fire control and detection.

1 INTRODUCTION

Wireless sensor networks (WSNs) has been applied

to a wide range of applications, such as agriculture,

military, medical, industrial, environmental and

habitat monitoring, among others (Akyildiz et al.,

2002). One of the possible implementations of WSN

in natural environments is the study of forest fires;

based on preventing, detecting or predicting the

behavior of fire in a forest fire. Usually, a wireless

sensor network is composed of multiple nodes,

distributed on an area, which used data aggregation

to obtain information about the environment through

the signals gathered from the sensors.

Several research works that implement a WSN

for forest fire applications are presented in the

literature. Most of them base their research on the

detection of fire, such as (Kosuku et al., 2009) which

uses a WSN to gather information to be used in the

FARSITE simulator. In (Ramachandran et al., 2008)

a WSN based on a swarm-inspired system is used

for detecting wildfires. It is a probabilistic model

that focuses on responses for query requests in an

accurate manner. In (Zhang et al., 2009) a WSN

with a satellite monitoring system is used for forest

fire detection. Reference (Li et al., 2009) presents an

implementation scheme of communication oriented

WSN and monitoring computer using VB6.0 and

SQL SERVER 2000. The work in reference

(Hefeeda and Bagheri, 2007) is meant to reduce the

consumption on transmitting the information using

data aggregation by analyzing the Fire Weather

Index (FWI) System. The work reported in (Yu et

al., 2005) uses a neural network system to prolong

the lifetime of the sensor.

Some other works are related to the study of fire

evolution (Lei and Lu, 2010), fire prevention

(Obregon et al., 2009). Others realize real

experiments with WSN in order to evaluate its

robustness against real conditions of a wildfire

(Antoine-Santoni et al., 2009).

Other works are related to the use of WSN to

improve the security on evacuations (Cherniak and

Zadorozhny, 2010). Reference (García et al., 2008)

deals with the use of WSN to improve the

information gathering for fire-fighters, in order to

allow a better actuation to extinguish the fire.

Usually, we use a system that is based on a WSN

to collect the sensor information and a centralized

PC to process the information to obtain the various

parameters of fire forests to assess, for example the

risk of fire. Only a few distributed approach are

proposed, like the one in (Liu et al., 2009), where

the remote nodes process information of multiple

sensors (temperature and smoke) sending alarm to a

base station if a node detect an incident.

However, centralized algorithms could be a

problem in real situations since if some nodes fail

because they are burned, a path to the base station

G. Gasull V., F. Larios D., Barbancho J., León C. and S. Obaidat M..

COMPUTATIONAL INTELLIGENCE APPLIED TO WILDFIRE PREDICTION USING WIRELESS SENSOR NETWORKS.

DOI: 10.5220/0003513200140021

In Proceedings of the International Conference on Data Communication Networking and Optical Communication System (DCNET-2011), pages 14-21

ISBN: 978-989-8425-69-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

could disappear. Our proposal is based on a

distributed processing, that can transmit the

information to a nearby operator. Our scheme does

not need a base station, and offers two types of

information: local information and global

information. A fire fighter can access this

information with a portable device on every node of

the network.

For a fire to take place, it is necessary to have the

coincidence in time and space of different factors.

These factors are summed-up in the Figure 1; it is

called the triangle of Fire. In a typical wildfire, fuel

is wood and vegetation and the oxidizing are the air.

Apart from fuel and oxidization, it is necessary to

have activation energy. In a wildfire, the energy can

be originated by various factor, like lightning,

replica, negligence, deliberated and unknown

causes, but in most of cases (around a 95%), it is

caused by human.

Figure 1: Triangle of fire.

Because of that, in order to evaluate the risk of

fire it is necessary to consider sociological

information, not only environmental condition. The

proposed algorithm, called ISFPWSN (Intelligent

System for Fire Prediction using Wireless Sensor

Networks), takes this information into account in

order to evaluate the risk of fire.

Apart from the risk of fire, other goal of

ISFPWSN is to offer information about the behavior

and evolution of the fire in case of wildfire. It

reduces the risk of the people exposed and improves

the fire detection because it offers information about

secure ways of escape and permits evaluation of a

strategy for fire extinguishing.

ISFPWSN is based on computational intelligence

algorithms that use fuzzy inference systems. It is

because fuzzy system has many advantages for

WSN applications (Marin-Perianu and Havinga,

2007), such as its simplicity, which permits

execution on devices with limited capabilities, or its

ability to manage imprecise and uncertain

information. All of these characteristics allow us to

obtain a robust system without a high computational

load.

The remainder of this paper is organized as

follow: Section 2 presents the proposed system

ISFPWSN. On Section 3 the simulator developed for

test ISFPWSN is described. Finally, in Section 4 we

present concluding remarks and provide a discussion

of future works.

2 PROPOSED SYSTEM

ISFPWSN is designed using a wireless sensor

network, as it described on section 2.3. The

operation of the system can described as below:

The nodes of the network execute their local

processing algorithms to obtain the local estimates.

Then they send these local estimates other nodes in

the network. Every node of the network gathers its

information and executes the distributed processing

algorithms. Subsequently, all the nodes share their

information about the risk of fire or, in case of a

forest fire, its evolution. ISFPWSN does not need a

base station to gather the information. This increases

the robustness of the network in case of wildfire,

because if some nodes were damaged from the fire,

it is possible that they do not exit a route to send the

information to the base station.

On the other hand, since all the nodes share the

estimation and behavior of the fire, a firefighter can

access to this information anywhere on the network,

which is a great aspect. ISFPWSN is based on two

processes: a local process and a distributed process,

these processes are described in sections below.

2.1 Local Processing

Local processing is executed on every node of the

network. Nodes evaluate the state of the fire with the

information provided by the sensors connected up to

the nodes. This algorithm offers a partial solution of

the global state of the system since this method only

uses local information to get the results. The

proposed local processing is basically a fusion and

data aggregation.

Within local processing there are two algorithms;

an algorithm to obtain the probability of fire

occurrence (PF) and an algorithm to obtain the

behavior of fire (BF).

2.1.1 Probability of Fire Occurrence (PF)

This algorithm determines the risk of fire in the

environment in the neighborhood of the node. The

COMPUTATIONAL INTELLIGENCE APPLIED TO WILDFIRE PREDICTION USING WIRELESS SENSOR

NETWORKS

summary of algorithm is shown Figure 2.

The output of the PI algorithm is a value between

0 and 100, which indicates the probability of ignition

of a forest fire after normalization. This algorithm is

divided up in two blocks: an environmental

processing and a sociological processing. Both

methods are based on a fuzzy logic engine.

Figure 2: Algorithm of the probability of fire.

Table 1 summarizes the fuzzy sets of the output

on both processing outputs (environmental and

sociological).

Table 1: Fuzzy sets of the outputs of environmental and

sociological processing.

Name Membership function

Very low Trapezoid (0,0,10,17)

Low Triangle (13,25,37)

Median Triangle (35,47,59)

High Triangle (57,69,81)

Very high Trapezoid (79,90,100,100)

• Environmental Processing: This processing

method tries to obtain the fire likelihood as a

function of the environmental variables. This is the

classical assumption considered in others fire

simulators. Most simulators base his calculations on

the Rothermel model (Rothermel, 1983) or the FWI

index (Canadian Forest Fire Danger Rating System,

1985). The input variables considered for this

process are temperature and fuel moisture. Because

of historical data of fires, these magnitudes appear as

the most relevant ones. The fuzzy sets of the inputs

are shown in table 2.

The environmental processing has the base of

knowledge described on Table 3.

• Sociological Processing: This processing is

intended to obtain the fire likelihood as a function of

the sociological variables. Figure 3 shows the causes

of forest fires in Spain during the period 1996-2005.

The figure shows that approximately 95% of the

wildfires are caused by the human. Moreover, a

great percentage of these wildfires are produced in

weekend. Because of it, we think it is necessary to

consider sociological variables to estimate the risk of

fire. Despite its importance, this kind of information,

in general, is not considered by fire simulator

because it if difficult to assess. This is because

sociological behavior is uncertainly and it is not easy

to model with classic techniques. Fuzzy logic is a

good approach to evaluate this kind of behavior.

Table 2: Fuzzy sets of the inputs of the environmental

processing.

Name Set Membership function

Temperature

Low Trapezoid (-10,-10,20,40)

Median Triangle (15,30,45)

High Trapezoid (35,65,100,100)

Fuel

Moisture

Low Trapezoid (0,0,10,30)

Median Triangle (20,40,60)

High Trapezoid (35,65,100,100)

Table 3: Base of knowledge of the environmental

processing.

Temperature F. Moisture Output

Low High Very low

Low Median Low

Low Low Medium

Median High Low

Median Median Medium

Median Low High

High High Medium

High Median High

High Low Very High

By analyzing this information, we can deduce

that in most cases the risk of fire. Most importantly

the number of visitor to the natural park is

considered more important than the environmental

variables. Because of that, is important to consider

information such as holidays or weekends to

estimate the probability of fire. Considering this

information is the mission of the sociological

processing. This system is also implemented with a

fuzzy logic engine. The input variables are day of

week, date and time/hour. The considered fuzzy sets

are summarized in Table 4. A low season for park

visits is considered between November and April

while the high season considered between May and

October. Low visit hours represent times when it is

cold during the day while high visit hours are the

hours when it is hot during the day.

DCNET 2011 - International Conference on Data Communication Networking

Figure 3: Causes of Wildfires.

Table 4: Fuzzy sets of the inputs of the sociological

processing.

Name Set Membership function

DOW

Mon-Thurs Trapezoid (0,0,3,3)

Fri-Sun Triangle (3,5,7)

Date

Low Trapezoid (0,2,5,8)

High Triangle (6,9,12)

Hours

Low Trapezoid (0,3,11,14)

High Triangle (12,17,24)

It is important mention that these rules apply to

Spain, where the dry months are between April and

November. The Sociological processing has the base

of knowledge described in Table 5.

2.1.2 Fire Behavior (FB)

This algorithm obtains the direction of the fire front

and its velocity in an area around the node. It does

not consider the topology of the terrain; it is used on

the distributed algorithm for prediction of direction

and velocity of the fire.

This algorithm considers the nodes as isolated

devices with their local information of wind

direction and velocity, temperature, humidity and

daily rain. This algorithm is executed only in case of

fire.

The velocity of propagation of the fire is

calculated using the formula below:

V = a(U + 1)b (1)

where V is the velocity of propagation of fire

(m/min), U is the wind velocity (m/s), a=0.233 and

b=1.332 for a “Pinnus Pinea” forest, as we consider

in this work. The direction of propagation can be

obtained as follows:

• If wind velocity = 0 Km/h: The fire follows a

radial pattern, with the same velocity on all direction

• For other cases: The fire front has the direction

of the wind and will trace an ellipse

Table 5: Base of knowledge of the sociological

processing.

Day of week Date Hour Output

Monday-Thursday Low Low Very low

Monday-Thursday Low High Low

Monday-Thursday High Low Low

Monday-Thursday High High High

Friday-Sunday Low Low Low

Friday-Sunday Low High Median

Friday-Sunday High Low Median

Friday-Sunday High High Very high

This algorithm also implements an alarm system

for risk of major fires, according to the rule of 30%.

This rule says that a great forest fire can be produced

with the conditions described on Table 6.

Table 6: Rule of 30%.

Parameter Value

Temperature > 30%

Humidity < 30%

Wind velocity > 30km/h

Days without rain > 30

2.2 Distributed Processing

The distributed processing is executed on every node

of the network; with the information gathered from

the broadcast messages sent by all nodes in the

network. This permits checking the global state of

the environment without the need for a base station

that collect all the information. This is especially

useful for the study of wildfire as the route between

any nodes to the base station can disappear if any

amounts of nodes are burned by the wildfire.

Moreover, this allows obtaining the global

information, anywhere into the network. It can help

fire-fighters who only need a small mobile device to

check the evolution of the wildfire.

The distributed processing scheme combines all

the partial solutions from the local processing of the

nodes with two other algorithms: risk of fire and

prediction of the direction and the velocity of the fire

algorithms. These algorithms provide results to

assess the overall state of the environment.

2.2.1 Risk of Fire (RF)

This algorithm evaluates the global risk of fire in the

COMPUTATIONAL INTELLIGENCE APPLIED TO WILDFIRE PREDICTION USING WIRELESS SENSOR

NETWORKS

environment. In this case, it combines the results of

every node in the network using the next formula:

 =

∑



(2)

Where N is the number of nodes, PFnodes is the

global estimation of the probability of fire and RF

represents the risk of fire.

The output of this algorithm is a value between 0

and 100, which will be encoded in one of five

possible risk levels that have been considered.

Values above 60 represent a high risk of forest fire.

Its output value can be classified according to their

value as described on Table 7.

2.2.2 Prediction of Direction and Velocity of

the Fire (PD)

This algorithm determines the global characteristic

of the wildfire. This information is useful for the

firefighters in order to determine the best way to

extinguish a fire and the route for escape to be used

which increases the safety aspect of the approach.

Table 7: Evaluation of the risk of fire.

Value RF Probability of Fire

RF ≤ 10 Very Low

10 < RF ≤ 30 Low

30 < RF ≤ 40 Median

40< RF ≤ 60 High

RF>60 Very High

This algorithm is based on a fuzz logic system

and offers two classes of outputs: the directions of

the fire and its velocity.

Figure 4: Considered directions of the fire.

Direction of fire is divided into 8 direction (D1

to D8), with 45 degrees between them and with D1

representing the direction of the node with the

maximum slope in relation with a considered node.

This is represented in Figure 4. Every Di variable

can only have two values: on or off. On signifies that

the fire is going to advance in its direction. Off

signifies that fire is not going to advance in it

direction.

The velocity of propagation is represented by

five values: very low, low, medium, high and very

high.

As inputs, this algorithm uses the local

estimations of the FB (Fire Behavior) algorithm, the

speed of the wind and its velocity and the

topography of the terrain. The proposed fuzzy logic

system is show on Figure 5.

Figure 5: Representation of the PD Algorithm.

The fuzzy system uses two internal parameter

calculated based on the local estimation of the

direction of fire: the consensus direction of fire

(DFc) and the consensus velocity of fire (VFc). Both

are evaluated as the median of the local estimations

of the system.

Table 8 summarizes the implementation rules.

Due to the large number of rules that the system has,

Table 8 only represents a summary of these. All

others rules can be obtained along the same lines

shown in this table.

Table 8: Rules of PD Algorithms.

DFc VIc Slope Outputs

DFc=

Low

D1=low D1, V1 = very low

D1=medium D1, V1 = low

D1=high D1, V1 = medium

Medium

D1=low D1, V1 = low

D1=medium D1, V1 = medium

D1=high D1, V1 = high

High

D1=low D1, V1 = high

D1=high D1, V1 = very high

DFc=

High

D1=low D1 = off

D1=medium D1, V1 = low

D1=high D1, V1 = medium

D3=low D3, V3 = low

D3=medium D3, V3 = medium

D3=high

D3, V3 = high

D4, V4= very high

DCNET 2011 - International Conference on Data Communication Networking

2.3 Infrastructure of ISFPWSN

ISFPWSN is made up of two devices: anchor nodes

and portable devices. A brief description about them

is given below.

2.3.1 Anchor Nodes

Anchor nodes are made up of wireless sensors that

form a wireless sensor network. These devices can

communicate among each other using the

appropriate routing protocols. The nodes execute all

the algorithms of ISFPWSN. Every Anchor node

obtains the next measures of sensor which include:

temperature, wind direction, wind velocity, daily

rain and humidity.

This information can be obtained from a small

and cheaper weather station attached to each node,

where the node can gather the information through a

communication port, such as RS232-C or RS-485. It

is interesting to provide power for these systems

using renewable energy sources, such as solar panels

or wind energy. Figure 6 shows an example that uses

solar energy.

Figure 6: Example of an anchor node.

2.3.2 Portable Devices

The other devices used in the project are the

measurement apparatus. They are a small and

portable devices used by the fire-fighters to obtain

the information of the environment. These devices

can access all information on the state of the fire and

can help in case of fire to provide information about

its future evolution. This information is useful both

to the extenuation and to determine secured ways for

escape; increasing the safety of the workers.

3 SYSTEM SIMULATOR

In order to test the proposed algorithms a C++ ad-

hoc simulator has been developed. Figure 7 shows

the main windows of the simulator. This simulator

has two classes of inputs: static inputs and dynamic

inputs.

Static inputs represent characteristic of the

terrain and the topology of the network. For the first

study we have considered localization on the

“Pinsapar” (see Figure 8) on the natural park of

Grazalema, Cádiz, Spain. These variables cannot

change on execution time. The topology of the

terrain is summarized in Table 9.

Figure 7: Main window of the simulator.

Table 9: Topology of the localization of the nodes.

Node Slope (%) Orientation (Northº)

1 38.6 220

2 41.8 190

3 37 182

4 15 190

5 21.8 235

6 25 220

7 27 170

8 25 220

9 33 235

10 12 180

Dynamic variables are the measures of the

sensors of every node. The information can be

change on the window of configuration of the

COMPUTATIONAL INTELLIGENCE APPLIED TO WILDFIRE PREDICTION USING WIRELESS SENSOR

NETWORKS

sensors (see Figure 9). This window appears when

you double click over a sensor of the main window.

Figure 8: Localization considered for the simulations.

Figure 9: Window with the configuration of a node.

This simulator can show a graphic representation

of the risk of fire (Figure 10) and the direction of fire

(Figure 11).

Figure 10: Window with the representation of the risk of

fire.

All the partial and global results of the

simulations can be obtained on a console window

(see Figure 12).

With this simulator, ISFPWSN algorithm has

been verified. In all of the simulated scenarios,

ISFPWSN offers a correct response. Its response is

similar to that obtained by BehavePlus simulator

using the same inputs.

4 CONCLUSIONS AND FUTURE

WORK

In this work, the ISFPWSN system is presented. It is

a system that uses collaborative processing

techniques over wireless sensor networks in order to

cooperate with the wildfire fighting. This system can

act in two ways: first, it can determinate the risk of

fire and, in such a case, this network gives useful

information to the fire fighters.

Figure 11: Window with the representation of the direction

of a fire.

In case of fire, the system gives information

about the location and direction of the fire fronts.

This information can be used to study in effective

manner the control of the fire and to design escape

routes in order to enhance the safety of workers.

All of this information can be obtained by a

firefighter with a portable device that can

communicate with the designed WSN. One aim of

wireless sensor networks is to be economical,

permitting the development of the system in a huge

area with a low cost. As future works, we plan to

build a prototype of the system on a real

environment in order to test the proposed

algorithms.

DCNET 2011 - International Conference on Data Communication Networking

Figure 12: Window with console of result.

ACKNOWLEDGEMENTS

This work has been supported by the Consejería de

Innovación, Ciencia y Empresa, Junta de Andalucía,

Spain, through the excellence project ARTICA

(reference number P07-TIC-02476).

REFERENCES

Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and

Cayirci, E. (2002). Wireless sensor networks: A

survey. Computer Networks, 38(4):393-422.

Antoine-Santoni, T., Santucci, J. F., de Gentili, E., Silvani,

X. and Morandini, F. (2009). Performance of a

protected wireless sensor network in a fire. Analysis of

fire spread and data transmission. Sensors, 9(8):5878-

5893.

Canadian Forest Fire Danger Rating System (CFFDRS).

(1985). Equations and FORTRAN program for the

Canadian Forest Fire Weather Index System.

Cherniak, A. and Zadorozhny, V. (2010). Towards

adaptive sensor data management for distributed fire

evacuation infrastructure. MDM’10. 11th

International Conference on Mobile Data Managemet,

pages 151-156.

García, E. M., Serna, M. A., Bermúdez, A., and Casado,

A. (2008). Simulating a WSN-based wildfire fighting

support system. ISPA’08. International Symposium on

Parallel and Distributed Processing with Application,

pages 896-902.

Hefeeda, M., and Bagheri, M. (2007). Wireless sensor

networks for early detection of forest fires. IEEE

International Conference on Mobile Adhoc and Sensor

Systems, pages 1-6.

Kosucu, B., Irgan, K., Kucuk, G., and Baydere, S. (2009).

FireSenseTB: A wireless sensor networks testbed for

forest fire detection. IWCMC'09, 6th International

Conference on Wireless Communications and Mobile

Computing: Connecting the World Wirelessly, pages

1173-1177.

Lei, Z., and Lu, J. (2010). Distributed coverage of forest

fire border based on WSN. 2nd International

Conference on Industrial and Information Systems,

pages 341-344.

Li, W., Zhang, J., Zhang, J., Xia, Z., and Wang, G. (2009).

The Monitoring of Host Computer for Forest Fire

Detection System based on Wireless Sensor Network.

WiCom’09, 5th International Conference on Wireless

Communications, Networking and Mobile Computing,

pages 1-4.

Liu, S., Tu, D., and Zhang, Y. (2009). Multiparameter fire

detection based on wireless sensor network. ICIS’09.

IEEE International Conference on Intelligent Systems,

pages 203-206.

Marin-Perianu, M., and Havinga, P. (2007). D_FLER - A

distributed fuzzy logic engine for rule based wireless

sensor networks. LNCS 2007, vol. 4836; 86-101.

Obregón, P. D. P., Sondón, S., Sañu, S., Masson, F.,

Mandolesi, P. S., and Julián, P. M. (2009). System

Based on Sensor Networks for Application in Forest

Fire Prevention. EAMTA’09. Micro-Nanoelectronics,

Technoloy and Applications, pages 61-65.

Ramachandran, C., Misra, S., and Obaidat M. S. (2008). A

probabilistic zonal approach for swarm-inspired

wildfire detection using sensor networks. International

Journal of Comunication Systems, 21(10);1047-1073.

Rothermel, R. C. (1983). How to predict the spread and

intensity of forest and range fires. US Department of

Agriculture, Forest Service, General Technical Report,

INT-143.

Yu, L., Wang, N., and Meng, X. (2005). Real-time Forest

Fire Detection with Wireless Sensor Networks.

International Conference on Wireless Communication,

Networking and Mobile Computing, vol.2, pages

1214-1217.

Zhang, J., Li, W., Yin, Z., Liu, S., and Guo, X. (2009).

Forest Fire Detection System based on Wireless

Sensor Network. ICIEA’09, 4th IEEE Conference on

Industrial Electronics and Applications, pages 520-

523.

COMPUTATIONAL INTELLIGENCE APPLIED TO WILDFIRE PREDICTION USING WIRELESS SENSOR

NETWORKS