Marked Final Fuzzy_logic_based_forest_fire_detection_using_wsn.docx

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK Prepared By Name

University Roll Number

ANVESH SHAW MOHONA DHAR AARIF HUSSAIN TARANG TAMANG DEBABRATA PAL

16900210006 16900211028 16900211001 16900211060 16900211020

Under the guidance of

Prof. Mamata Dutta A Project Report To be submitted in the partial fulfillment of the requirements For the degree of

Bachelor of Technology in Information Technology

Department of Information Technology Academy Of Technology Affiliated to

West Bengal University of Technology, West Bengal. May, 2015

Academy Of Technology

CERTIFICATE This is to certify that the project entitled FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK submitted to WEST BENGAL

UNIVERSITY OF TECHNOLOGY in the partial fulfillment of the requirement for the degree of BACHELOR OF TECHNOLOGY in INFORMATION TECHNOLOGY is original work carried out by the following students under my guidance: Name

University Roll Number

ANVESH SHAW MOHONA DHAR AARIF HUSSAIN TARANG TAMANG DEBABRATA PAL

16900210006 16900211028 16900211001 16900211060 16900211020

The matter embodied in this project is genuine work done by the student and has not been submitted whether to this University or to any other University/Institute for the fulfillment of the requirement of any course of study.

Prof. Mamata Dutta Assistant Professor Department of Information Technology Academy of Technology, Aedconagar, Hooghly-712121, West Bengal, India

Dated: Countersigned By

Prof. Amitava Nag Head, Department of Information Technology Academy of Technology, Aedconagar, Hooghly-712121, West Bengal, India

ii

STATEMENT BY THE CANDIDATES

Anvesh Shaw, Roll: 16900210006 Mohona Dhar, Roll: 16900211028 Aarif Hussain, Roll: 16900211001 Tarang Tamang, Roll: 16900211060 Debabrata Pal, Roll: 16900211020 B. Tech 8th Semester Dept. of Information Technology Academy of Technology

We hereby state that the Project Report entitled “FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK” has been prepared by us to fulfill the requirements of IT 892 during the period January 2015 to April 2015. _______________ _______________ _______________ _______________ _______________

iii

ACKNOWLEDGEMENT At the very outset, we would like to convey our sincere gratitude to our beloved founder-chairman Prof. J Banerjee and respected director Prof. D Bhattacharya for all the encouragement and support extended to us during the tenure of this project and also our years of studies in this institute. We are indebted to our guide Prof. Mamata Dutta for her epitome of guidance, assistance and cooperation that facilitated the successful conclusion of our project. We express our heartfelt thanks to our Head of the Department, Prof. Amitava Nag, who has been actively involved and very influential from the start till the completion of our project. We would also like to thank all teaching and nonteaching staffs of the Information Technology Department for their constant support and encouragement given to us. Last, but not the least, it is our great pleasure to acknowledge the wishes of friends and well wishers, both in academic and non-academic spheres.

Anvesh Shaw, Roll: 16900210006, Dept.: IT Mohona Dhar, Roll: 16900211028, Dept.: IT Aarif Hussain, Roll: 16900211001, Dept.: IT Tarang Tamang, Roll: 16900211060, Dept.: IT Debabrata Pal, Roll: 16900211020, Dept.: IT

iv

LIST OF FIGURES Figure

Label

Page No.

Fig 3.1

Sensor Network

10

Fig 3.2

Circuit diagram of the Sensor Network

10

Fig 4.1

Fuzzification process

12

Fig 6.1

Screenshot of Rules 1

19

Fig 6.2

Screenshot of Rules 2

20

Fig 6.3

Input and output parameters

21

Fig 6.4

Temperature range graph

22

Fig 6.5

Relative Humidity range graph

22

Fig 6.6

CO2 density range graph

23

Fig 6.7

Time range graph

23

Fig 6.8

Fire probability range graph

24

Fig 6.9

Different fire probabilities for different fire values

25

Fig 8.1

Screenshot of rule viewer 1

29

Fig 8.2

Screenshot of rule viewer 2

30

Fig 8.3

Screenshot of rule viewer 3

31

Fig 9.1

Screenshot of Gantt table

33

Fig 9.2

Screenshot of Gantt chart

34

v

LIST OF TABLES

Table

Page No.

Table 4.1: Input Fuzzy Variable

14

Table 4.2: Output Fuzzy Variable

14

Table 7.1: Expected Input & Output values

27

Table 7.2: Input & Output values based on real world test values.

28

vi

ABSTRACT

The detection and prevention of forest fire is a major problem now a day. Timely detection allows the prevention units to reach the fire in its initial stage and thus reduce the risk of spreading and the harmful impact on human and animal life. Because of the inadequacy of conventional forest fire detection in real time and monitoring accuracy the Wireless Sensor Network (WSN) is introduced. This project proposes a fuzzy logic based implementation to manage the uncertainty in forest fire detection problem. Sensor nodes are used for detecting probability of fire with variations during different time in a day. The Sensor nodes sense temperature, humidity, light intensity, CO2 density and time and send the information to the base station. This proposed system improves the accuracy of the forest fire detection and also provides a real time based detection system as all the input variables are collected in real time basis. Wireless sensor networks appearing in many wireless communication developments. WSN modules sense a variety of phenomena including temperature, relative humidity and smoke which are helpful in fire detection systems. A sensor network deployed in forest reports its data to a processing center for possible actions, such as alerting local residents and dispatching firefighting crews. Sensors are deployed uniformly at random in the forest. Forest fires are disasters which, cause loss of life, property and destruction of thousands of hectares of forest land in various places during the summer every year. The project is to address applications for detecting large scale temperature field monitoring for Forest Fire Detection. This proposed technique is given for detection of forest areas which have high probability of catching a fire.

vii

TABLE OF CONTENTS

Contents

Page No.

Certificate

ii

Statement by The Candidates

iii

Acknowledgement

iv

List of Figures

v

List of Tables

vi

Abstract

vii

Chapter 1

Introduction

1

Chapter 2

Related Works

4

Chapter 3

Problem Definition & Objectives

8

Chapter 4

Proposed Solution for Forest Fire Detection

11

Chapter 5

Software & Hardware Requirement Specification

15

Chapter 6

Project Implementation

17

Chapter 7

User Input & Output

26

Chapter 8

Graphical View of Rules with Input & Output

28

Chapter 9

Project Planning & Scheduling

32

Chapter 10

Future Scope & Further Enhancement

35

Conclusion

37

Bibliography

38

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

INTRODUCTION

1

CHAPTER 1 INTRODUCTION A wildfire is an uncontrolled fire in an area of combustible vegetation that occurs in the countryside or a wilderness area. Other names such as brush fire, bush fire, forest fire, desert fire, grass fire, hill fire, peat fire, vegetation fire, and wildfire may be used to describe the same phenomenon depending on the type of vegetation being burned, and the regional variant of English being used. A wildfire differs from other fires by its extensive size, the speed at which it can spread out from its original source, its potential to change direction unexpectedly, and its ability to jump gaps such as roads, rivers and fire breaks. Wildfires are characterized in terms of the cause of ignition, their physical properties such as speed of propagation, the combustible material present, and the effect of weather on the fire. Fast and effective detection is a key factor in wildfire fighting. Early detection efforts were focused on early response, accurate results in both daytime and nighttime, and the ability to prioritize fire danger. However, with the increase in global temperatures forest fires are becoming extremely common. It is therefore necessary that such dangers be identified early to prevent extreme damage to property and life. In this project we propose a new real time forest fire detection method by using wireless sensor networks. Our goal is to detect and predict forest fire promptly and accurately in order to minimize the loss of forests, wild animals and people in the forest fire. In our proposed paradigm, a large number of sensor nodes are densely deployed in a forest. Sensor nodes collect measured data (temperature, relative humidity) and send to the respective cluster nodes. It has been shown in the literature that about 20% of CO2 emissions in the atmosphere are due to forest fires. It is also known that the soil becomes more susceptible to erosion since it is left bare. WSN has enabled a more convenient early warning system and secondly, WSN provides a system able to learn about the phenomena of natural disasters. The losses due to these disasters are increasing in an 1

alarming rate. In order to minimize damage, early detection of forest fires is a crucial issue. Without a clear and correct understanding of the distribution and dynamics of forest fires, it is impossible to effectively manage them. Thanks to the technology that enables the deployment of devices called motes, in large numbers directly into fire zones, wireless sensor networks (WSNs) can significantly improve the accuracy and density of parametric measurement of physical phenomena. It will be beneficial to detect the pre-cursors of these disasters, early warn the population, evacuate them, and save their life.

1.1 Purpose Of This Study A Forest Fire can be defined as a conflagration and the free fire propagation on vegetation in forests, jungles and other areas. In the area of the eastern hills in Bogota, are often presented this kind of phenomena. There are three known types of fires, mainly determined by the nature of the fuel: the glass or air fires, underground fires and surface fires. There are forests fires which are caused by the human, nature and man-nature interaction. Internationally, 48% of the causes of forest fires are the result of agricultural activities, 17% were unintentional, 16% of bonfire, careless smoking 8%, 3% forestry activities, 1% rights of way, 1% other activities production and 6% other causes. In Colombia, according to the Ministry of Environment, 95% of wildfires are caused by man, either intentionally or negligently. Fire detection is always been a crucial challenge for human, moreover detecting fire using automated sensors definitely requires efficient and accurate ways. Since fire depends on more than one physical/environmental condition simultaneously, so in this project we have used fuzzy type-2 logic for fire detection. Fuzzy gives best results in such cases because there is an uncertainty about how much extent of a factor like temperature, humidity and light intensity should be involved to cause a fire. In addition to this interval type-2 fuzzy system is used to make the results accurate and error free so that there would be no uncertainty in decision making. Timely detection allows the prevention units to reach the fire in its initial stage and thus reduce the risk of spreading and the harmful impact on human and animal life. Because of the inadequacy of 2

conventional forest fire detection on real time and monitoring accuracy the Wireless Sensor Network (WSN) is introduced.

1.2 Brief Overview Of The Project Report In Chapter 2, we review the previous work in sensor network to detect forest fire. Chapter 3 addresses the question of useful features and definition of wireless sensor network. We describe experiments with unsupervised cluster to detect forest fire. Chapter 4 presents the proposed solution for forest fire detection. In Chapter 5 presents the software and hardware required for completion of the project. Chapter 6 contains screenshots and illustrations which tracks the stepwise implementation of the project work. Chapter 7 contains the expected output with respect to some values for input parameters. Chapter 8 contains the graphical view of the inputs and outputs. Chapter 9 contains Gantt chart and Gantt table which gives the timeline for the project activities. Chapter 10 presents the future aspects to detect forest fire.

3

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

RELATED WORKS

2

CHAPTER 2 RELATED WORKS Traditionally, forest fires were detected using conventional techniques such as guard towers located to fire high points and Osborne fire Finder that is a tool consisting of a card topographic printed on a disc with edge graduated. Unfortunately these primary techniques are inefficient due to the unreliability of human observation towers and difficult life condition. This has allowed some countries to use forest-fire detection systems based on the satellite imagery. MODIS (Moderate Resolution Imaging Spectro Radiometer) used in CANADA and AVHRR (Advanced Very High Resolution Radiometer) used in CHINA are satellite-based monitoring systems. These approaches have proven to be limited by terrain, time of day, and weather conditions such as clouds, light reflections and smoke from legitimate industrial or social activities. Recently, the technology of wireless sensor networks (WSN) has emerged and has been adopted by several countries. This technology must consider important design goals and features such as: energy efficiency, early detection and accurate localization, forecast capability and adaptive to harsh environment. Many research works from literature related to forest fires by using WSN have been conducted around the world. Other interesting investigations have also been done in this area. Authors in surveyed fire detection studies from three perspectives: residual areas, forest fires and contributions of WSN to early fire detection. South Korean project (FFSS) presented in uses an experimental approach based on a networked motes but no evaluation has been made by authors on the proposed detection approach. In authors have conducted simulation study under Castalia and Farsite fire simulators to detect and localize forest fires using WSN. A theoretical architecture of WSN based on Zigbee Technology has been proposed but neither simulation nor real experiments have been conducted. Research works presented tried to early detect forest fires by means of cluster tree WSN using simulation and test-bed based approaches respectively. To enhance 5

the conventional WSN detection approaches by reducing the number of false alarms, authors proposed an image-based real time fire detection technique. Unfortunately, most of these studies choose simulating their proposed solutions instead of doing experiments in real test-bed environments, since that kind of setup exposes additional difficulties. Even those using test-bed to carry out real experiments; they have not made a serious study on which detection methods could be very suitable to their context. In the context of the above studies, we propose a comparative study between two forest fires detection methods (Canadian and Korean) using a real test-bed based approach to choose the one that fits the context of our country.

2.1 Forest Fire Detection Methods In this section we present the best-known detection systems of forest fires used in practice. We focus mainly on those chosen for the comparative study presented in this project. Canadian approach The Canadian study proposed the calculation of the index fire according to FWI (Fire Weather Index). This eliminates the need to communicate all the sensor data to Sink, and only a few aggregated index are reported for reduce energy consumption. FWI system comprises six standardized index. The three first shows daily variations of water content of three types of fuel forest with different speeds drying. The other three relate to fire behavior and are representative of the propagation speed, the quantity of burned fuel and intensity of the fire. The method is based solely on the determination noon daily weather: temperature, relative humidity, speed wind and rain during the last 24 hours (if there was). The month must also be specified. This method is primarily to solve a set of equations (Van Wagner and Pickett, 1985), which can be calculated with fast computer. Korean approach This approach is implemented on the system FFSS (Forest-fires Surveillance System). The middleware developed in this study receives and 6

processes packets from the transceiver and displays its results. The results contain the level of risk of forest fires. This level is calculated by the formula defined by the following equation: Y= 6.87 + (0.64 *P) + (0.15 *EF) + (1774,94 / CS) (1) Where: EF is effective humidity (%), CS is solar radiation of the day (MJ/m²), P is rain (mm). Then, the software saves the received packets to database server and generates emergency alerts by the Note that other systems for detecting forest fires can be found in practice such as National Fire Danger Rating System (NFDRS) and D-FLER (Distributed Fuzzy Logic Engine Rule-based WSNs).

7

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

PROBLEM DEFINITION & OBJECTIVES

3

CHAPTER 3 PROBLEM DEFINITION & OBJECTIVES

A Wireless Sensor Network (WSN) of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. The WSN is built of "nodes" – from a few to several hundreds or even thousands, where each node is connected to one (or sometimes several) sensors. Each such sensor network node has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a microcontroller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery or an embedded form of energy harvesting. A sensor node might vary in size from that of a shoebox down to the size of a grain of dust, although functioning "motes" of genuine microscopic dimensions have yet to be created. The cost of sensor nodes is similarly variable, ranging from a few to hundreds of dollars, depending on the complexity of the individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.

9

In computer science and telecommunications, wireless sensor networks are an active research area with numerous workshops and conferences arranged each year, for example IPSN, SenSys, and EWSN.

Fig 3.1: Sensor Network We can point out 3 main parts in the system:   

the Wireless Sensor Network the Communications Network the Reception Center

Below is given a general diagram of the whole system:

Fig 3.2: Circuit diagram of the Sensor Network 10

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

PROPOSED SOLUTION FOR FOREST FIRE DETECTION

4

CHAPTER 4 PROPOSED SOLUTION FOR FOREST FIRE DETECTION 4.1 Fuzzification Fuzzy techniques for treating tentative qualitative information including fuzzy arithmetic and mathematics, fuzzy set theory, fuzzy logic, fuzzy decision making and fuzzy control. Rule based fuzzy operators are a new class of operators exclusively considered in order to relate the principles of estimated interpretation as shown in the figure below.

Fig 4.1: Fuzzification process

Fuzzy Logic was introduced in 1965 by Lotfi A. Zadeh, who was professor in computer science at the University of California in Berkeley. Fuzzy Logic is a multi-valued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. 12

Fuzzy Logic has emerged as a profitable tool for the controlling and steering

of

systems,

complex

industrial

processes,

household,

entertainment electronics, as well as for other expert systems and applications. The aim is to use fuzzy sets in order to make computers more ’intelligent’,

therefore.

Fuzziness

describes

event

ambiguity

and

impreciseness of linguistic terms. Fuzzy logic fits best in applications where the variables are continuous and/or mathematical models do not exist or traditional system models become overly difficult. WSN is typically used to supervise some parameters of an environment process. The atmospheric events are multifaceted, confusing and imprecision embedded in their nature. Consequently, a fuzzy based approach is a feasible option. The model of fuzzy logic system consists of fuzzification, fuzzy rules, fuzzy inference system and de-fuzzification process. The Fuzzification is the first step in the fuzzy inferencing process. This involves a domain transformation where crisp inputs are transformed into fuzzy inputs. Crisp inputs are the exact inputs measured by sensors and passed into the control system for processing, such as temperature, position, pressure, rpm's, etc. Each crisp input that is to be processed by the Fuzzy Inferencing Unit has its own group of membership functions or sets to which they are transformed. The group of membership functions exists within a universe of discourse that holds all relevant values that the crisp input can possess. In our fire detection algorithm temperature, relative humidity, CO2 density and time act as input fuzzy variables. The Probability of Fire is the output variable. The membership functions LOW, MEDIUM and HIGH are defined on temperature, light intensity, humidity and CO density whereas BEFORE NOON, NOON, AFTER NOON for. VERY LOW, LOW, MEDIUM, HIGH and VERY HIGH are defined on Probability of fire as shown in the table on the next page.

13

Membership

Variable

Range

Temperature

0 oC to 600 oC

VL, L, M, H, VH

Relative Humidity

0 to 100 %

VL, L, O, H, VH

Time

0 to 24 Hours

EM, M, F, AF, E, N

CO2 Density

500 to 5000 ppm

L, N, H

Functions

Table 4.1: Input Fuzzy Variable

Variable

Range

Membership Functions

Fire Probability

0 to 100

VL, L, M, H, VH

Table 4.2: Output Fuzzy Variable

Legend: VL: Very Low, L: Low, N: Normal, M: Medium, O: Optimum, H: High, VH: Very High, EM: Early Morning, M: Morning, F: Forenoon, AF: Afternoon, E: Evening, N: Night

14

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

SOFTWARE AND HARDWARE REQUIREMENT SPECIFICATIONS

5

CHAPTER 5 SOFTWARE AND HARDWARE REQUIREMENT SPECIFICATIONS

Software Requirements: 

Operating System: Windows XP or, Windows 7



Programming IDE: MATLAB 2013

Hardware Components: 

Processor: Dual core processor. Preferably, Intel i3.



Hard Drive: A minimum of 10 GB space is required for the software to run without problems.



Memory: A minimum of 4 GB RAM is required for faster processing.



A sufficiently fast and reliable internet connection.

 A wireless network of connected sensors for data accumulation.

16

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

PROJECT IMPLEMENTATION

6

CHAPTER 6 PROJECT IMPLEMENTATION 6.1 Implementation The project required implementation of only the software component. No hardware component was created as per the requirements of the project. MATLAB by MathWorks was used for the implementation of the software. MATLAB was chosen specifically due to the availability of its Fuzzy Logic Toolbox. The Fuzzy Logic Toolbox provides functions, applications, and a Simulink block for analyzing, designing, and simulating systems based on fuzzy logic. The product guides the user through the steps of designing fuzzy inference systems. Functions are provided for many common methods, including fuzzy clustering and adaptive neurofuzzy learning. The toolbox lets us model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. We can use it as a stand-alone fuzzy inference engine. Alternatively, we can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system. Step-wise implementation of the project: 

Analysis

of

following

environmental

conditions

in

forests:

Temperature, Relative Humidity, CO2 density. Analysis was done based on data available on the internet owing to previous works on forest fire detection. 

Designing of rules based on data analysed.



Creating rules in MATLAB.



Testing of rules using available real world data.



Analysis of output values.



Documentation creation.

On the following pages are rules as implemented in MATLAB. 18

6.2 Rules for Evaluation

Fig 6.1: Screenshot of Rules 1

19

Fig 6.2: Screenshot of Rules 2

20

6.3 Input and Output Parameters

Fig 6.3: Input and Output parameters In the above image, the left side indicates the input parameters and the right side indicates the output parameter. Various values of Temperature, Relative Humidity, CO2 and Time are input on the left side. The middle portion of the image indicates the Fuzzy Logic Toolbox which evaluates the input to give the output on the right side, which is Fire Probability.

21

6.4 Range Of Values for Input Parameters

Fig 6.4: Temperature range graph

Fig 6.5: Relative Humidity range graph

22

Fig 6.6: CO2 density range graph

Fig 6.7: Time range graph

23

6.5 Range Of Values for Output Parameter

Fig 6.8: Fire Probability range graph

24

6.6 Testing Of Rules for Output

Fig 6.9: Different fire probabilities for different input values

25

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

USER INPUT AND OUTPUT

7

CHAPTER 7 USER INPUT AND OUTPUT

Fire probability is the output with respect to input parameters: Temperature, Relative Humidity, CO2 Density and Time. Rule No.

Temperature

Relative Humidity

CO2 Density

Time

Fire Probability

1

L

H

L

F

VL

2

L

O

L

F

VL

3

L

L

L

F

VL

4

L

L

N

F

VL

5

M

VL

H

AF

M

6

M

H

L

F

VL

7

M

H

N

AF

L

8

M

O

H

F

M

9

M

L

H

AF

M

10

M

VL

H

AF

M

11

H

H

N

F

M

12

H

O

N

F

M

13

H

L

N

F

H

14

H

VL

H

F

H

15

H

VL

H

AF

VH

16

H

H

N

F

M

17

H

L

N

AF

VH

18

VH

VL

H

F

VH

19

VH

VL

H

AF

VH

20

VH

VL

H

AF

VH

21

VH

O

H

AF

VH

22

VH

L

H

F

H

23

VH

L

H

AF

VH

24

VH

O

H

F

H

25

VH

O

H

AF

VH

Table 7.1: Expected Input & Output values Legend: VL: Very Low, L: Low, M: Medium, O: Optimum, H: High, VH: Very High, N: Normal, F: Forenoon, AF: Afternoon

27

RESULT Test Case

Relative Temperature Humidity (oC) (%)

CO2 Density (ppm)

(in 24 hour format)

Time

Fire Probability

1

18

70

1000

22

10

2

18

50

1000

22

10

3

18

30

1000

22

10

4

18

30

2000

22

10

5

30

7

4000

14

50

6

30

60

1600

10

10.00

7

30

60

3000

14

28.43

8

30

50

4800

10

58.31

9

30

30

4800

14

68.31

10

30

14

4800

14

68.31

11

100

60

3000

10

48.43

12

100

40

3000

10

48.43

13

100

30

3000

10

78.43

14

100

10

4500

10

88.43

15

100

10

4500

13

88.43

16

100

60

3000

10

48.43

17

100

20

3000

13

78.43

18

500

10

4000

10

88.42

19

500

10

4000

10

88.42

20

500

10

4000

13

88.42

21

500

40

4000

13

88.42

22

500

20

4000

10

88.42

23

500

20

4000

13

88.42

24

500

40

4000

10

88.42

25

500

40

4000

13

88.42

Table 7.2: Input & Output values based on real world test values. Based on the input values: Temperature, Relative Humidity, CO2 Density and Time, the output received is the Fire Probability. Example: Temperature (oC)

Relative Humidity (%)

CO2 Density (ppm)

Time (in 24 hour format)

Fire Probability

18

70

1000

22

10

28

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

GRAPHICAL VIEW OF RULES WITH INPUT AND OUTPUT

8

CHAPTER 8 GRAPHICAL VIEW OF RULES WITH INPUT AND OUTPUT

Fig 8.1: Screenshot of Rule Viewer 1 30

Fig 8.2: Screenshot of Rule Viewer 2

31

Fig 8.3: Screenshot of Rule Viewer 3

32

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

PROJECT PLANNING AND SCHEDULING

9

CHAPTER 9 PROJECT PLANNING AND SCHEDULING Gantt Chart is a project control technique used for several purposes including scheduling and planning. Gantt chart is also known as bar chart each box is used to represent an activity. Gantt Chart allows US to see at a glance the following information: (i)

What the various activities are.

(ii)

When each activities begins and ends.

(iii)

How long each activity is scheduled to last.

(iv)

Where activities overlap with another activities and how much overlap is present.

Fig 9.1: Screenshot of Gantt Table

34

Fig 9.2: Screenshot of Gantt Chart

35

FUZZY LOGIC BASED IMPLEMENTATION FOR FOREST FIRE DETECTION USING WIRELESS SENSOR NETWORK

FUTURE SCOPE AND FURTHER ENHANCEMENT

10

CHAPTER 10 FUTURE SCOPE AND FURTHER ENHANCEMENT

This project only required the implementation of the software component. We did not implement any hardware component, nor was this software package tested on any available hardware devices. The project was only tested on the MATLAB IDE. This project has immense future scope because of its use of Fuzzy Logic, which is more suited for evaluating variable input parameters to give a concrete output. One area which can see lots of improvement is data gathering and collection. Hardware that makes use of this program for forest fire detection can be designed to operate based on natural triggers. Forest fires, man-made or natural, are such catastrophes which boggle the mind! Controlling, or even predicting forest fires is an immensely difficult task. This is mostly due to presence of such a large number of parameters that may speed up the fire, change the direction of the fire, etc. The temperatures of the local area during a forest fire are so high that there is a good chance of multiple sensors being damaged or being burnt out! Therefore, one area of improvement can be the development of fire resistant and robust hardware. The software program can also be so implemented that it can take more number of variables as input, with much vague values, and give very accurate results. Energy efficient and versatile hardware can also be very useful for proper implementation of this project.

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CONCLUSION In this project, we projected an event detection mechanism for detection of fire and fuzzy approach for calculating probability of fire using multisensors. Our proposed forest fire detection handles the vagueness present in the statistics successfully and gives the finest results with very low false alarm rate. The decision based on this approach is more precise as it gives accurate results with variation of time and other physical parameters. The membership functions and the parameters can be changed and modified as required. Rules also could be altered and adjusted according to parameters for further work on this model. In this project, we have investigated the effect of fuzzy logic in determining the probability of forest fire using multiple sensors. Some vagueness related to the different environmental conditions can easily be handled by the proposed forest fire detection method. It gives accurate and robust result with variation of temperature, humidity, etc as all the input variables are defined by real time data.

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BIBLIOGRAPHY 1. Bayo, D. Antolín, N. Medrano, B. Calvo, S. Celma,” Early detection and monitoring of forest fire with a wireless sensor network system”, Procedia Engineering, Volume 5, 2010, Pages 248-251, ISSN 1877-7058. 2. YunusEmreAslan, IbrahimKorpeoglu, and OzguUluso, “A framework for use of wireless sensor networks in forest fire detection and monitoring,” Science direct, vol. 36 pp.1-12, Mar 2012. 3. Al-Abbass Y. Al-Habashneh, Mohamed H. Ahmed, and Taher Husain, “Adaptive MAC Protocols for Forest Fire Detection Using Wireless Sensor Networks ,” in proceeding of IEEE electrical and communication system engineering conference’, 2009,pp.329-333. 4. ÇağdaşDöner*, GökhanŞimşek, Kasım Sienna Yıldırım, and AylinKantarc, “Forest Fire Detection with Wireless Sensor Networks,” in academia Computer Engineering Department, Ege University’, 2010, pp.107-109. 5. ArnoldoDíaz-Ramíreza,*, Luis A. Tafoyaa, Jorge A. Atempa, and PedroMejía-Alvarezb, “Wireless Sensor Networks and Fusion Information Methods for Forest Fire Detection,” in Science direct on Electronics Engineering and Computer Science ’,2012, pp.69-79. 6. A.K. Singh, and Harshit Singh, “Forest Fire Detection through Wireless Sensor Network using Type-2 Fuzzy System”, International Journal of Computer Applications,” vol. 52– No.9, pp. 19-23,August 2012. 7. M. Ganesh, Introduction to fuzzy sets and fuzzy logic, Prentice Hall of India Private Limited, New Delhi, 2006.

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