forest fire prevention, detection, and fighting based on fuzzy … · 2019. 7. 30. · forest fire...

Research ArticleForest Fire Prevention Detection and Fighting Based onFuzzy Logic and Wireless Sensor Networks

Josueacute Toledo-Castro 1 Pino Caballero-Gil 1

Nayra Rodr-guez-Peacuterez 1 Ivaacuten Santos-Gonzaacutelez 1

Candelaria Hernaacutendez-Goya 1 and Ricardo Aguasca-Colomo 2

1Department of Computer Engineering and Systems University of La Laguna 38206 Tenerife Spain2Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numericas en Ingenierıa University of Las Palmas de Gran Canaria35017 Gran Canaria Spain

Correspondence should be addressed to Josue Toledo-Castro jtoledoculledues

Received 6 August 2018 Accepted 11 November 2018 Published 2 December 2018

Guest Editor Andrew Schumann

Copyright copy 2018 Josue Toledo-Castro et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Huge losses and serious threats to ecosystems are common consequences of forest fires This work describes a forest fire controllerbased on fuzzy logic and decision-making methods aiming at enhancing forest fire prevention detection and fighting systemsIn the proposal the environmental monitoring of several dynamic risk factors is performed with wireless sensor networks andanalysed with the proposed fuzzy-based controller With respect to this meteorological variables polluting gases and the oxygenlevel are measured in real time to estimate the existence of forest fire risks in the short-term and to detect the recent occurrenceof fire outbreaks over different forest areas Besides the Analytic Hierarchy Process method is used to determine the level of firespread and when necessary environmental alerts are sent by aWeb service and received by a mobile application For this purposeintegrity confidentiality and authenticity of environmental information and alerts are protectedwith implementations of Lamportrsquosauthentication scheme Diffie-Lamport signature and AES-CBC block cipher

1 Introduction

Nowadays forest fires often cause serious threats to theenvironment and produce real emergency situations andnatural disasters The response time of emergency corpsgreatly affects the consequences and losses caused by themso the enhancement of forest fire prevention and detectionsystems can be considered a main goal for conserving theenvironment With respect to this the real-time monitoringof certain environmental variables may make the forest fireprevention detection and fighting more efficient

Different types of environmental risk factors can beconsidered for estimating the existence of forest fire risksover different forest areas On the one hand static forestfire risk factors such as vegetation layers topography orthe frequency of forest fires may be useful to perform along-term estimation of forest fire risks because vegetation

affected byweather changes over time and several topographyparameters (such as the existence of elevated slopes) mayhave a direct impact on the probability of fire occurrenceOn the other hand unusual changes of dynamic forest firerisks such as meteorological variables polluting gases orthe oxygen level measured in real time can be analysedaiming at performing a short-term estimation of forest firerisks Likewise uncommon decrease of humidity valuesor oxygen level jointly with increasing temperature valuesor the concentrations of certain polluting gases such ascarbon dioxide and carbon monoxide may involve a highprobability of outbreaks of recent nearby fires Thereforeenvironmental monitoring may make the response time ofemergency corps more efficient Fire spread can be alsoestimated by analysing the values of meteorological variableswind direction changes and the oxygen level over nearbyforest areas because these variables have a direct impact on

HindawiComplexityVolume 2018 Article ID 1639715 17 pageshttpsdoiorg10115520181639715

2 Complexity

relevant fire propagation factors such as dryness of vegetationand organic fuels

A wireless sensor network (WSN) [1] based on Internetof Things (IoT) devices and sensors can be used to performa real-time environmental monitoring of the aforementionedforest fire risk factorsTheir design and distributionhave to beaddressed aiming at covering asmuch forest areas as possibleWith respect to this several challenges must be consideredsuch as the authentication of sensor nodes [2] and the securityof wireless communications among distributed sensor nodestaking into account possible areas out of network coverage

Due to the uncertainty in environmental data under-standing environmental changes to estimate the existence offire risks or to detect the occurrence of a wildfire incidentis not a simple process that can be executed with completeaccuracy Fuzzy logic [3] and decision-making methods suchas the Analytic Hierarchy Process (AHP) [4] can be usedto provide an enhancement in the real-time analysis ofenvironmental data Forest fire prevention and detection maybe more accurate through the interpretation of the forestfire risks involved in every measured environmental variablejointly with unusual environmental changes with respect tothe typical values measured by a WSN

The main goal of the proposal here described is toestimate in short-term the existence of forest fire risks and todetect the recent occurrence of fire outbreaks over differentforest areas For this purpose a forest fire controller basedon fuzzy logic has been implemented aiming at analysingenvironmental information such asmeteorological variablespolluting gases and the oxygen level measured by a dis-tributed WSN To this end a particular prototype of IoTdevice equipped with environmental sensors has been imple-mented When a fire outbreak is detected a decision-makingmethod based on AHP is enabled to determine the neigh-bouring forest area that is more likely to favour fire spread asa result of its current environmental conditions Moreover aWeb service and amobile application have been implementedaiming at activating environmental alerts Besides open datasources have been integrated to provide other relevant envi-ronmental information such as vegetation layers or historicalinformation of recent fires Particular attention has been paidto the application of security mechanisms to protect theintegrity confidentiality and authenticity of measured envi-ronmental information and alerts through implementationsof Lamportrsquos authentication scheme [5] Diffie-Lamportrsquossignature [6] and AES-CBC block cipher [7]

This work is organized as follows Section 2 deals withsome related works Section 3 details the proposed forest firecontroller based on fuzzy logic Section 4 outlines the AHP-based detection method of fire spread Then the proposedsystem is explained in Section 5 and the implemented secu-rity mechanisms are sketched in Section 6 Section 7 includesa description of several experimental results Finally someconclusions and research works in progress are given inSection 8

2 Related Works

In the last years different proposals have been put forwardto improve forest fire prevention detection and extinction

systems Many of those solutions are based on real-timeenvironmental monitoring and IoT devices With respect tothis the work [8] includes the implementation of a smartsystem aiming at measuring carbon dioxide (CO2) emissionsfrom different sources such as forest fires through usingRaspberry Pi In addition to monitoring polluting gasesother proposals analyse the so-called Fire Weather Index fordesigning an efficient fire detection system through wirelesssensor networks and a simple data aggregation scheme[9]

Nowadays the combination of fuzzy logic and decision-makingmethods such asAHP produces innovative solutionsthat may enhance the accuracy in the prevention and detec-tion of wildfire incidents

The work [10] proposes a fuzzy system based on overlapindices to improve forest fire detection through imple-menting a wireless sensor network and analysing differentvariables such as the lightness and the distance to the fireIn that work a particular generalization of the Mamdaniinference system is introduced by using overlap functionsand overlap indices Likewise the work [11] also proposesthe use of WSNs and the incorporation of fuzzy logic insensor nodes but its aim is to estimate the evidence of firethrough analysing the previous temperature and the currenttemperature For that purpose two fuzzy approaches basedon temporal characteristics are proposed to optimize thenumber of rules that have to be checked

Regarding the use of decision-making algorithms thework [12] includes a model of the forest fire risk throughintegrating fuzzy sets with AHP In particular it uses adecision-making method including the Geographic Infor-mation System and the fuzzy AHP method [13] to estimatethe importance related to each considered causative factor inforest fires

The security and the distribution of the WSN requireparticular attention [14 15] Several security challenges andthreats are addressed in [16] with respect to wireless com-municationThe survey [17] includes recent routing protocolsand presents a classification in categories such as data-centrichierarchical and location-based Likewise the functionaldesign and the implementation of a complete WSN platformare presented in [18] aiming at performing a long-termenvironmental monitoring Low cost minimum number ofsensors fast deployment and other requirements are alsoconsidered in the approach of WSN design in differentworks

Differently from the aforementioned works the systemdescribed here proposes the combination of WSN fuzzylogic decision-making methods multihop routing [19] andsecurity mechanisms for performing a secure real-time envi-ronmental monitoring of dynamic forest fire risk factorsThemain aim is to estimate the existing forest fire risks in differentmonitored forest areas and to detect the occurrence offire outbreaks Moreover a decision-method based on AHPintended to determine the fire spread through nearby forestareas has been implemented in a system composed by a Webservice and a mobile application to manage environmentalalerts and provide an enhancement in forest fire preventiondetection and tracking systems

Complexity 3

Input Values

Fuzzy - Forest Fire controller

fuzzifiedoutputs

aggregated

Low

Input VariablesFuzzification

High Extreme

Rules Inference

Outputs

fuzzifiedoutputsvalues

Centroid method

Fire Risk Estimation () Fire Outbreaks Detection ()

Prevention module Detection module

MeteorologicalVariables Wind Direction

Criteria

Vegetation

Decision-Making on Fire Propagation

Aggregation of

Defuzzification

Figure 1 Fuzzy-based forest fire controller and AHP-based fire spread estimator

3 Fuzzy-Based Forest Fire Controller

The proposed method is based on environmental measure-ments of dynamic forest fire risk factors such as meteoro-logical variables polluting gases and oxygen level measuredby a distributed WSN in real time The aim is to provide anenhancement in the short-term estimation of forest fire risks(prevention) and in the detection of the beginning of recentwildfire incidents (detection) With respect to this a fuzzy-based forest fire controller has been implemented aimingat calculating the probability of existing forest fire risks(prevention module) and the probability that a fire outbreakhas recently occurred (detection module) in a particularforest area On the one hand the prevention module isintended to analyse measured environmental conditions thatmay favour the occurrence of a wildfire incident (high tem-peratures low relative humidity values vegetation drynessdue to low rainfall etc) On the other hand the detectionmodule is aimed at detecting wildfire incidents that haverecently occurred In addition to meteorological variablesthe oxygen level and the concentrations of polluting gases(useful indicators of fire outbreaks occurrence and biomassburning process) measured by the WSN are also analysedThus unusual environmental changes such as temperatureincrease and decrease in relative humidity and oxygen valuesjointly with very high concentrations of carbon dioxide andcarbon monoxide may involve the recent occurrence of aforest fire

In order to face the difficulty and imprecision of theanalysis of environmental changes related to forest firesa fuzzy logic Mamdani inference system [20] has beenconsidered aiming at developing a forest fire controllerAs Figure 1 shows environmental values measured by thedeployed WSN are initially fuzzified to provide a level ofmembership with different fuzzy sets proposed for each

Table 1 Notation used to describe the fuzzy-based forest firecontroller

Notations DescriptionV Linguist variable120572V Discrete measurement of V119865119863V Fuzzy Domain of V

119865119878V119894i-th Fuzzy Set proposed for

V

120583119865119878V119894 (120572V)Level of membership of 120572V

with respect to 119865119878V119894

considered linguistic variable These fuzzy sets are intendedto express if a particular measurement may be ldquonormalrdquoldquolowrdquo ldquohighrdquo or ldquoextremerdquo depending on the common stateof the corresponding analysed environmental variable Eachfuzzified measure is evaluated on the basis of a knowledgebase or inference rules to analyse the related forest fire risksand the probability of fire outbreaks occurrence Finally theobtained results are aggregated into a same output set anddefuzzified into a discrete percentage

Mamdanirsquos inference steps are described using the nota-tion in Table 1

31 Input Variables Fuzzification Dynamic forest fire riskfactors are considered as input linguistic variables of theproposed fuzzy-based forest fire controller With respect tothis the corresponding environmental measurements regis-tered by the distributed WSN for meteorological variablespolluting gases and the oxygen represent the input values ofthe proposed fuzzy system

Measurements of temperature relative humidity windspeed and rainfall are fuzzified into themembership functionproposed for each one of these monitored meteorological

4 Complexity

0010203040506070809

1

0 25 30 34 37 41 45 100

Temperature

0010203040506070809

1

0 15 25 35 45 575 70 100Humidity percentages

Relative Humidity

0010203040506070809

1

0 10 30 40 60 80 100 240

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

pLe

vel o

f mem

bers

hip

Leve

l of m

embe

rshi

p

km h

Wind speed

lowmedium

highextreme

lowmedium

highextreme

very lowlow

normalhigh

lowmedium

highextreme

0010203040506070809

1

0 65 12 16 30 40 100mm

Rainfall

Celsius degrees C∘

Figure 2 Membership functions of meteorological variables

Table 2 Fuzzy sets and fuzzy domains proposed for every linguistic variable

Variable Fuzzy Set Fuzzy DomainTemperature (119879) 119865119878119879 = low medium high extreme [0 100] ∘CHumidity (119867) 119865119878119867 = very low low normal high [0 - 100] Wind Speed (119882119904119901119890119890119889) 119865119878119882119904119901119890119890119889 = low medium high extreme [0 - 240] kmhRainfall (119877) 119865119878119877 = low medium high extreme [0-100]mmOxygen (1198742) 1198651198781198742 = very low low normal high [0-30] Carbon dioxide (1198621198742) 1198651198781198621198742 = low normal high extreme [0-1000] ppmCarbon monoxide (119862119874) 119865119878119862119874 = normal medium high extreme [0-100] ppm

variables (see Figure 2) The graphical representation of themembership function of each linguistic variable is performedon an ordinate axis that represents the level of membership ofmeasured input values with the different proposed fuzzy setsOn the other hand the abscissa axis represents the domainof the linguistic variable regarding its discourse of universe(Celsius degrees percentages kmh etc) Themain aim is toexpress the fuzzified value attached to every environmentalmeasurement as ldquovery lowrdquo ldquolowrdquo ldquonormalrdquo ldquohighrdquo orldquoextremerdquo

The rule of 30 considered as a relevant preventive modelof forest fire risk has been applied here to design fuzzy setsThis rule considers measurements of temperature and windspeed above 30∘C and 30 kmh respectively jointly withhumidity values below 30 as risk environmental conditionsthat may favour the occurrence of forest fires

Regarding fire outbreak detection provided by the pro-posed fuzzy-based forest fire controller measurements ofoxygen level and polluting gases (carbon dioxide and carbonmonoxide) are also fuzzified into their corresponding mem-bership functions In the case of polluting gases particlesper million (ppm) are used as their discourse of universeTheir fuzzy sets have been proposed on the basis of unusualincreases above their typical environmental concentrations atoutdoor forest areas (see Figure 3) In contrast unexpecteddecreases of the oxygen level below 21 levels (consideredas the current measured oxygen level at the atmosphere)have been considered for their design These uncommonenvironmental changes may involve a high probability thata fire outbreak has recently occurred

For each input linguistic variable Table 2 shows its FuzzySet (119865119878) and Fuzzy Domain (119865119863) According to (1) for every

Complexity 5

0010203040506070809

1

0 150 250 400 500 700 900 1000Particles per million ppm

Carbon dioxide

lownormal

highextreme

0010203040506070809

1

0 10 12 20 40 50 100

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

p

Particles per million ppm

Carbon monoxide

normalmedium

highextreme

0010203040506070809

1

0 12 15 17 20 23 26 30

Leve

l of m

embe

rshi

p

Oxygen percentages

Oxygen

very lowlow

normalhigh

Figure 3 Membership functions of polluting gases and oxygen

Table 3 Average computation of monitored dynamic risk factors

Forest fire risks Fire outbreak occurrence Measurement frequency Average calculationNon-existent Non-existent 5 minutes Last 20 measurementsLow - 2 minutes Last 15 measurementsHigh - Without measurement delay Last 10 measurementsExtreme Low High Extreme Without measurement delay Last 5 measurements

V an environmental measurement 120572V of the linguistic variableV is measured within the considered thresholds [119886 119887] of itsfuzzy domain 119865119863V This value is fuzzified into the corre-sponding membership function so its level of membership120583119865119878V119894 (120572V) is calculated for the i-th fuzzy set proposed for theenvironmental variable V (ldquovery lowrdquo ldquolowrdquo etc) Calculatedlevels of membership with respect to all four fuzzy setsproposed for every linguistic variable are added up to obtaina final value of 100

forallV isin (119879119867119882119904119901119890119890119889 119877 1198742 1198621198742 119862119874) exist120572V

isin [119886 119887] |4

sum119894=1

120583119865119878V119894 (120572V) = 100(1)

Everymonitored linguistic variable is analysed by fuzzify-ing the value of the last measured environmental value and itsaverage Previous measurements of every dynamic risk factorare used to calculate the average which is also fuzzified intothe membership function of every input linguistic variableaiming at expressing if the average is ldquonormalrdquo ldquolowrdquo ldquohighrdquo

etc The number of measurements used to calculate theaverage depends on the analysed environmental conditionswith regard to every variable (see Table 3)

32 Inference-Rule Evaluation A knowledge base intendedto evaluate unusual environmental changes between the lastenvironmental measurement and the average of each inputlinguistic variable (previously fuzzified) is here proposedUnexpected increases of the last fuzzified values of tem-perature wind speed or concentrations of polluting gaseswith respect to their corresponding fuzzified averages ina particular forest area are analysed Likewise unexpecteddecreases in fuzzified values of relative humidity precipita-tion or oxygen produce the same effectThese environmentalevents are detected when there is a difference with regard tothe fuzzy set with which the last environmental measurementand the average present a greater level of membership Forthis purpose we have used the first neural network model forimplementing fuzzy systems the so-called Fuzzy AssociativeMemory (FAM) [21] One FAM has been proposed for eachconsidered linguistic variable (temperature humidity carbon

6 Complexity

Table 4 Inference-rule evaluation for carbon monoxide variable

Last COmeasurement CO average normal medium high very highNormal NFO LFO LFO LFOMedium LFO LFO HFO HFOHigh HFO HFO HFO EFOExtreme EFO EFO EFO EFO

0010203040506070809

1

0 33334 66666 100percentages ()

Forest fire risks

nonexistentlow

highextreme

0010203040506070809

1

0 33334 66666 100

Leve

l of m

embe

rshi

p

percentages ()

Fire outbreaks occurrence

nonexistentlow

highextreme

Leve

l of m

embe

rshi

p

Figure 4 Membership functions of output variables

dioxide etc) All of them compose the knowledge base of thisfuzzy-based forest fire controller According to the consultedexpert knowledge the triggers of the rules on the linguisticvariables are previously set through appropriate overlapsof the fuzzy sets of input variables These proposed FAMsevaluate fuzzified input values on the basis of two differentobjectives

(1) Fire risk prevention module Fuzzified values ofthe last measurement and average correspondingto temperature relative humidity wind speed andrainfall (meteorological variables) are compared withthe aim of evaluating the existence and severity offorest fire risks (nonexistent low high and extreme)in every forest area The objective is to evaluate theprobability of considering this forest area as a riskzone to be affected by the beginning of a forest fireTherefore the considered output linguistic variable isthe existence of forest fire risks

(2) Fire outbreak detection module In addition tofuzzified values of meteorological variables pro-posed FAMs compare the fuzzified concentrationsof carbon dioxide carbon monoxide and oxygenin order to evaluate the probability that a fire out-break may have recently occurred in that forestarea (nonexistent low high or extreme) Thereforethe related output linguistic variable correspondsto the probability of detecting a recent fire out-break Table 4 shows the proposed FAM for car-bon monoxide that analyse the probability of fireoutbreak occurrence obtained by comparing theirfuzzified values (average and last measurement)For simplicity the following notation has beenused to denote the probability of Fire Outbreak

nonexistent (NFO) low (LFO) high (HFO) andextreme (EFO)

33 Aggregation of Outputs and Defuzzification Once infer-ence rules have been used to evaluate fuzzified values for bothmodules (prevention and detection) the results obtainedwith respect to evaluating every input linguistic variableare aggregated into two different global output sets andfuzzified into the proposed output membership functionsOne of the two output sets includes all the results of theinference-rule evaluation corresponding to the existence offire risks (prevention module) The second one is composedof the results of the inference-rule evaluation with respectto the probability that a recent fire outbreak has occurred(detection module) The percentage highlights the discourseof universe of both output linguistic variables Thus allfuzzified outputs obtained from the inference-rule evaluationstep are represented in the range of 0-100

Figure 4 shows the fuzzy sets proposed for both out-put linguistic variables ldquononexistentrdquo ldquolowrdquo ldquohighrdquo andldquoextremerdquo The inference rules relate input fuzzified variableswith those fuzzy sets through FAMs

Both obtained output sets are defuzzified through apply-ing the centroid method [22] whose aim is to obtain thegravity center of each output set On the one hand a nonfuzzydiscrete percentage of the forest fire risks existing in thecorresponding forest area is obtained that represents theresult required by the prevention module On the other handthe probability that a fire outbreak has recently occurredis obtained by applying the aforementioned defuzzificationmethod in the other output set Finally the Web service isresponsible for activating environmental alerts and notifyingemergency corps depending on the estimated forest fire risks

Complexity 7

Selecting the neighboring node with thegreatest risk of fire propagation

Temperature Rainfall Oxygen VegetationWind directionHumidity Wind speed

Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n

Figure 5 Proposed criteria

Table 5 Notation used for analysing fire spread

Notations Description119873119900119889119890119891119894119903119890 IoT device located in the forest area where fire was detected119908119894119899119889 119889119894119903119890119888119905119894119900119899 Linguistic variable of wind direction119871119900119888(119899) Function that calculates the location of a WSN node 119899119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889(119909) Fuzzification of location 119909 of a node into fire spread membership function

() and the obtained evidence of fire outbreaks occurrence()

4 AHP-Based Fire Spread Estimator

If the fuzzy-based forest fire controller detects evidencesthat a fire outbreak has recently occurred in a particularforest area a decision-making method for analysing thefire propagation is activated For this purpose AHP hasbeen used with the aim of evaluating and selecting whichneighbouring forest areas are more likely to favour fire spreadand to be affected by nearby fire outbreaks as consequenceof their environmental conditions With respect to thisseven criteria have been defined in order to select the bestalternative (nearby forest area) as Figure 5 shows

The values of meteorological variables (such as tem-perature relative humidity rainfall and wind speed) andthe oxygen level have been considered among the sevencriteria For this purpose fuzzified input values of thesemeasured environmental variables for sensor nodes locatedin a nearby forest area from where the fire outbreak wasrecently detected are considered These sensor nodes areconsidered neighbours of the affected area One of them andin particular theWSN sensor node located in a neighbouringforest area that ismore likely to be affected by the fire outbreakrecently detected is selected as the best alternative Thesemeteorological criteria are relevant because they have a directimpact on the state of existing vegetation or organic fuelthus favouring fire spread Required fuzzified environmentalvalues are returned by the Input Variables Fuzzificationstep of Mamdanirsquos inference when new environmental datapackages (measured by every nearby forest area) are analysedby the proposed fuzzy-forest fire controller

The wind direction measured in the forest area where thefire outbreak was detected is considered as a main criterionEvery WSN node is capable of measuring this environmentalvariable in degrees with respect to the North On the one

hand each IoT device knows the location in degrees ofevery neighbouring WSN node with respect to the NorthThrough comparing their locations and the wind directionit is determined whether every neighbouring WSN nodemaybe ldquoextremely nearrdquo ldquovery nearrdquo ldquonearrdquo ldquomoderately nearrdquoor ldquofarrdquo with regard to the direction of fire spread that isaffected by the current state of wind in that forest area Table 5shows the notation used to describe this process

According to (2) the difference between the location ofevery neighbouring WSN node and the current wind direc-tion bothmeasured in degrees to the North is calculated andfuzzified into the membership function that Figure 6 showsThis membership function has been implemented aimingat calculating the proximity of the node to the fire spreaddirection In this example the difference between the locationof the neighbouring node 1 and the last measurement ofwind direction registered by the sensor node located in theforest area recently affected by fire outbreaks is fuzzified intothis membership function A value of 100 is obtained withrespect to the fuzzy set ldquoextremely nearrdquo and 0 for the restof fuzzy sets This result involves that the fire spread directionmay be extremely near the location of the neighbouringnode 1 Fuzzy sets for the wind direction variable have beenproposed according to the features of the used sensor

forall119899 isin 119899119890119894119892ℎ119887119900119906119903119894119899119892 119882119878119873 119899119900119889119890119904 119900119891 119873119900119889119890119891119894119903119890 exist119909

isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)

In addition to the hierarchical structure of the proposedcriteria a comparison scale has been implemented to providedifferent pairwise comparison levels ldquoequally importantrdquo

8 Complexity

North

a b

Fire outbreaks detection

a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘

Neighboring nodeb = Loc degrees-N (N2)

Fire spread direction - WSN node location Fuzzifier

Fuzzy Set Level ofmembership

Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately

Extremely near Very nearNear

NearModerately near Far

Far

Neighboring node 1a = Loc degrees-N (N1)

0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)

Figure 6 Membership function of fire spread direction WSN node location

Table 6 Matrix pairwise criteria comparison

Criteria Temperature Humidity Rainfall O2 119882119894119899119889119904119901119890119890119889 119882119894119899119889119889119894119903119890119888119905119894119900119899 VegetationTemperature 11 11 11 11 13 15 15Humidity 11 11 11 11 13 15 15Rainfall 11 11 11 11 13 15 15O2 11 11 11 11 13 15 15119882119894119899119889119904119901119890119890119889 31 31 31 31 11 13 13119882119894119899119889119889119894119903119890119888119905119894119900119899 51 51 51 51 31 11 11Vegetation 51 51 51 51 31 11 11

(referenced as comparison number 1) ldquomoderately moreimportantrdquo (number 3) and ldquostrongly more importantrdquo(number 5) Regarding paired-wise comparisons amongalternatives according to each criterion the importance levelassigned to every alternative with respect to the othersdepends on the forest fire risks associated with their fuzzifiedvalues

When two alternatives present the same fuzzy value(such as ldquohighrdquo or ldquolowrdquo) for a given criterion (temperaturehumidity etc) a comparison level of 1 ldquoequally importantrdquois used However when they are not equal each fuzzy setof difference between both fuzzy values involves one higherlevel of importance that will be assigned to the sensornode whose fuzzy value may cause more forest fire risksFor example regarding the membership function and fuzzysets proposed for temperature (ldquonormalrdquo ldquomediumrdquo ldquohighrdquoand ldquoextremerdquo) ldquonormalrdquo and ldquohighrdquo fuzzy values of twoalternatives or WSN nodes are considered For this criterionthe second alternative highlights with respect to the first onethrough a comparison level of ldquostrongly more importantrdquo asconsequence of existing two fuzzy sets of difference betweenfuzzy values (medium and high) With respect to this thesecond alternative is more likely to favour fire spread asresult of its fuzzy temperature value Thus the differenceswith respect to fuzzy sets have a direct impact on theimportance level or weight difference assigned to every WSNnode

Regarding criteria comparison Table 6 shows the weightcomparison matrix for the seven criteria

5 Proposed System

Theproposed system is based on aWSN aWeb service and amobile application TheWSN is in charge of performing real-time environmental monitoring The Web service integratesthe fuzzy-based fire controller and the AHP-based fire spreadestimator aiming at analysing the existence of forest firerisks in every monitored forest area detecting recent fireoutbreaks and estimating fire propagation With respect tothis the activation of environmental alerts depending onthe results obtained by the proposed fuzzy-based forest firerisk controller and decision-making method is implementedThrough the proposed mobile application members of theemergency corps are notifiedTherefore the proposed systemis responsible for the following

(1) Analysing the states and unusual variations of themonitored environmental variables through the pro-posed distributed WSN

(2) Coordinating active and deployed members of emer-gency corps in areas at risk of forest fires ensuringtheir safety and tracking their location at any time

(3) Managing efficiently the state and energy of thesystem resources deployed in the environment suchas the battery level of WSN nodes

For the coordination of emergency corps the imple-mented mobile application allows establishing a real-timecommunication service with the Web service and the emer-gency corps headquarters

Complexity 9

51 Wireless Sensor Network The proposed WSN is aimedat implementing an environmental monitoring interfacecapable of measuring meteorological variables (such as tem-perature humidity wind and rainfall) polluting gases (suchas carbon dioxide and carbon monoxide) and oxygen levelEvery WSN node is based on a particular prototype ofIoT device that is distributed through different forest areascomposing a distributed WSN

Regarding the proposed prototype of IoT device it isbased on Arduino platform and mainly composed of amainboard seven environmental sensors and a supportboard for allowing their integration Two particular mod-ules are also assembled in order to provide 4G and Wificommunications On the one hand the 4G module allowssending the environmental information measured by sensorsto the Web service It also provides a GPS service capableof accessing the location of every IoT device On the otherhand the Wifi module is aimed at providing Wifi-Directcommunications [23] among IoT nodes The 4G and Wifimodules do not transmit information simultaneously Wifi-Direct communications are only enabled when a particularsensor node is not able to transmit wirelessly through 4Gthe recent measured environmental information to the Webservice as a consequence of being out of network coveragein that moment Thus these communications are intendedto provide a multihop-routing protocol among nearby IoTdevices aiming at reaching a sensor node with 4G networkcoverage

Temperature and humidity aremeasured by a samedigitalsensor capable of providing operational ranges between -40∘C and +85∘C and 0 - 100 respectively Wind parameters(speed and direction) are measured by an anemometer (withmeasurement range between 0 and 240 kmh) and a windvane In addition a pluviometer composed of a small bucketfor measuring rainfall is assembled A maximum bucketcapacity of 028 mm of water is allowed Pollutant gases aremeasured by two different sensors On the one hand thecarbon dioxide measuring range allows the measurement ofconcentrations up to 10000 ppm with a response time of 60seconds On the other hand the carbon monoxide sensoris able to perform environmental measurements below 1000ppm (with response time of 1 second) Finally the oxygenlevel can bemeasured between 0 and 30 (with response timeof 15 seconds)

The power supply of the IoT device prototype is based onan external rigid solar panel of 7 volts (V) that can provide amaximum charging current of 300 mA aiming at recharginga connected rechargeable lithium-ion battery This batteryprovides 6600 mA x h and a continuous nominal voltage of37V To reduce the energy consumption below 33120583A severalsleepmodesmay be enabled when forest fire risks do not existin the corresponding forest area In addition Web servicemonitors in real time the current battery level of every sensornode through the last sent environmental measurement

Once environmental variables are measured environ-mental measurements and other device parameters (such asthe battery level) are formatted to obtain a new environmen-tal data package Every dynamic risk factor (temperaturehumidity etc) is referenced by an alias of a few characters

to decrease the size of the package that will be sent Theproposed environmental data package format is as shown inthe following

119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)

Time frequency of environmental measuring can beupdated depending on the previously estimation of forestfire risks detection of recent fire outbreaks or activation ofexternal forest fire alerts by the emergency corps Insteadof measuring the considered dynamic risk factors every 5minutes the sensor nodes located near the affected forestarea will measure without any time delay Likewise WSNnodes that are neighbours of an IoT device located in aforest area at risk of fire will also increase the frequencyof environmental measuring The Web service is in chargeof adjusting the environmental measurement cycle of everyWSN node depending on the continuous forest fire risksanalysis (shown in Table 3)

52 Web Service Environmental information measured bythe WSN is continuously sent to the Web service which ismainly composed of a server that integrates the proposedfuzzy-based forest fire controller TheWeb service is in chargeof maintaining an environmental dataset history for everymonitored forest area including

(1) Every environmental measurement registered by theWSN

(2) Average of monitored dynamic risk factors and corre-sponding coefficient of variation (aimed at analysingits variability and detecting possible errors in valuesmeasured by the WSN)

(3) Results given by the fuzzy-based forest fire con-troller for each received environmental data packageincluding short-term forest fire risk estimation andprobability that a fire outbreak has recently occurred

Interactive elements such as linear and bar graphs visualgauges and maps are used to represent environmental infor-mation The Web service is also responsible for the activa-tion of environmental alerts depending on results obtainedby the fuzzy-based forest fire controller According to theproposed fuzzy sets of output variables a colour code hasbeen integrated into every proposed visualization elementldquoNonexistentrdquo results provided by the fuzzy-based forestfire controller are displayed with green and ldquoLowrdquo ldquoHighrdquoand ldquoExtremerdquo results with yellow orange and red coloursrespectively The aim is to improve the visual interpretationof the severity of estimated forest fire risks and detected fireoutbreaks

The forest fire risks and the probability that a wildfireincident has recently occurred are immediately sent to theinvolved emergency corps For this purpose notificationssent by the Web service are received by the proposed mobile

10 Complexity

Fuzzy - based forestrisk controller

AHP - basedfire spread estimator AEMET API

WSN sensornodes

Dynamic forestfire risks

Forest firerisks

Real time environmental data

Open data

Vegetationmap

Landscapedescription

Forest Tracks

Waterresources

Static forest data

Mobile appWeb service

Environmentalalerts

Emergencycorps

Location

Emergency corpsdata

Figure 7 Structure of information designed for the system

application aiming at providing an improvement of theresponse time of emergency corps If a fire outbreak in aparticular forest area is detected results given by decision-making method based on AHP are also sent to the involvedemergency corps via the mobile application With respect tothis nearby forest areas with the most propitious environ-mental conditions to favour fire spread are notified Finally areal-time coordination module has been integrated into theWeb service and the mobile application to enhance forestfire prevention and fighting operations among the membersof emergency corps Besides their locations and movementsaround the affected forest areas are tracked and representedthrough an interactive map displayed in both the mobileapplication and the Web service

Open data sources like the Spanish Agencia Estatal deMeteorologıa (AEMET) have also been used to extend theenvironmental information managed by the Web service andto access certain forest resources thatmay be relevant to forestfire prevention detection and monitoring systems aimingat designing the structure of information of the proposedsystem (see Figure 7)

6 System Security

The proposal includes different security mechanisms aimedat providing secure communications among WSN nodesthe Web service and the mobile application In particularrelevant security requirements for IoT deployment suchas data privacy confidentiality and integrity together withauthenticity have been considered in the implementation

61 Insecurity in WSN Used for Environmental MonitoringWSN nodes are susceptible to different hazards capable ofcompromising their integrity confidentiality and availability

When used for environmental monitoring if WSN nodesare compromised the fuzzy-based forest fire controller isnot able to estimate risks and fire outbreak occurrences sothe response time of emergency corps losses and damagecaused by forest fires to the ecosystems may be significantlyincreased

Communication channels between nodes or betweennode and Web service may be attacked to get unauthorizedaccess to the environmental information measured by theWSN or to interrupt the transmission of environmentaldata packages In addition environmental data may bemanipulated to activate false forest fire alerts so involvingthreats to the integrity and confidentiality of data measuredby sensor nodes Once activated these alerts would reachthe implemented mobile application (wrongly notifying theemergency corps) Other manipulation attacks may aim athiding the existence of fire risks or of the beginning of a forestfire Besides data may be also duplicated through forwardingan environmental data package that was previously sent by aWSN node successfully authenticated

62 Implemented Authentication Signature and EncryptionAn authentication scheme for environmental data packagesmeasured by IoT devices has been implemented throughthe combination of Lamportrsquos authentication scheme andLamport-Diffie signature In particular a privatepublic keygeneration mechanism necessary for the signature of everyenvironmental data package and for the authentication of IoTdevices has been implemented following the Lamportrsquos One-Time Password Authentication Scheme

The procedure based on the Lamportrsquos authenticationprocess is performed as follows Firstly every IoT devicechooses a secret value 119908 and applies 119899 times a hash cryp-tography function 119867(119908) on it The result is a list of 119899

Complexity 11

loT device Web service

n = 100Signatureverification

Hash function(original message)

Selecting comparingelements from keyBinary sequence

AES 256Decryption Authentication

verification

Database update

New public key

Data package

AES 256 CBCHash function(original message)

Selecting elementsfrom private key in useBinary sequence

435263 3536236 73635

New public key forauthenticating next message

T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)

Hash function(SHA256)

H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1

Figure 8 Authentication and signature methods proposed for WSN sensor nodes

one-time privatepublic key pairs The last generated key119867119899(119908) is sent from the corresponding IoT device to the Webservice This key is used as public key for authenticating thefirst environmental data package sent by the IoT device Foran initial value of 119899 = 100 given as an example the first keysent to the server would be 119867100(119908)

Once the value of 119899 is defined the IoT device selectsthe key 119867119899minus119894(119908) for the 119894-th environmental data pack-age from the list of keys in order to perform its corre-sponding signature At this moment the selected key isconsidered as a private key and is associated with thekey 119867119899minus119894+1(119908) previously stored in the server databaseThrough the application of the hash function to this pri-vate key the second one (public key) is obtained so thatboth compose an authentication key pair according to theLamportrsquos authentication scheme For an example with n= 100 the key 119867119899minus119894(119908) 997888rarr 119867100minus1(119908) 997888rarr 11986799(119908) isselected from the aforementioned key list to sign the contentof the first environmental data package Therefore keys119867119899minus119894(119908) (private key) and 119867119899minus119894+1(119908) (public key) composean authentication key pair for the 119894-th environmental datapackage

After selecting the private key 119867119899minus119894(119908) the IoT deviceapplies the hash function on the content of the new environ-mental data package to be signed according to the Lamport-Diffie one-time signature scheme (see Figure 8)This packageis mainly composed of environmental measurements andother device data Then the obtained result is expressed ina binary sequence so the bits 0 and 1 are used aiming atselecting the corresponding elements of the private key in use119867119899minus119894(119908) for the 119894-th message Then the IoT device sends tothe server

(1) Signature It is composed of the original message(involving the set of registered environmental mea-surements for each monitored dynamic risk factorand other parameters such as the battery level) andthe elements selected from the current private key inuse 119867119899minus119894(119908)

(2) Private Key in Use The key 119867119899minus119894(119908) is used to verifythe signature of the package and to authenticate theIoT sensor node in the Web service If this signature

verification is successful this key is stored in theserver database as the new public key to be usedto authenticate the next environmental data packagethat reaches the Web service

When the Web service receives a signed environmentaldata package from an authenticated IoT device it verifies theattached signature In order to do it according to Lamportrsquosauthentication scheme the server checks if the key 119867119899minus119894(119908)obtained in this package is associated with the last public keystored in the database for the 119894-thmessage119867119899minus119894+1(119908) For thispurpose the cryptographic hash function is applied to thefirst one and the obtained result is compared to the secondone aiming at verifying their match Regarding an initialvalue of 119899 = 100 and the second environmental packagesent to the Web service (119894 = 2) authentication is performedfollowing

119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)

The hash function used in the implementation is SHA-3(Secure Hash Algorithm 3) which is the latest member of theSecure Hash Algorithm family of standards [24]

Every key available in the list initially generated by theIoT device through the secret chosen value 119908 is consideredas a private key or public key depending on its current useOn the one hand every key is used as a private key to signan environmental data package On the other hand the samekey is considered as a public key when it is stored in thedatabase aiming at authenticating the sensor node that hasrecently sent a new environmental data package to the Webservice

Table 7 shows an example of the signature process andthe keys used for the first three environmental data packagesregistered and sent by the IoT device to the Web service

Regarding signature verification the server applies thehash function to the content of the environmental packagereceived from the IoT device The Web service can deducewhich elements of the private key in use should have been

12 Complexity

Table 7 Authentication for the first three packages

119894-th message Private key 119867119899minus119894(119908) Public key 119867119899minus119894+1(119908) Authentication1 119867100minus1(119908) = 11986799(119908) 119867100minus1+1(119908) = 119867100(119908) 119867(119867100minus1(119908)) = 119867100minus1+1(119908)2 119867100minus2(119908) = 11986798(119908) 119867100minus2+1(119908) = 11986799(119908) 119867(119867100minus2(119908)) = 119867100minus2+1(119908)3 119867100minus3(119908) = 11986797(119908) 119867100minus3+1(119908) = 11986798(119908) 119867(119867100minus3(119908)) = 119867100minus3+1(119908)

Table 8 Environmental dataset defined for estimating forest fire risks

119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252

Table 9 Oxygen level and polluting gases for experimental results

119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147

Figure 9 Generation of proposed fuzzy-based forest fire controller in fuzzyTECH app

selected by the IoT device in the signature process If thesignature verification of the 119894-th message and the nodeauthentication are completed successfully the server replacesthe current public key stored in the database 119867119899minus119894+1(119908) by thenew public key 119867119899minus119894(119908) contained in the last environmentaldata package This last one will be used during the signatureverification process of the next (119894+1)-th message and the IoTdevice will select the private key 119867119899minus119894minus1(119908) for signing a newenvironmental message

In the implementation of the proposal before the trans-mission of a new environmental package to the Web serviceevery IoT device encrypts the content of each environmentalpackage through AES block cipher in Cipher Block Chaining(CBC) mode with keys of 256 bits and zero padding [25]The use of this algorithm does not involve a significantadditional cost of time in the environmental measurementsmanagement by the IoT devices For this purpose a keypredistribution process has been implemented to provide thenecessary secret keys based on Lamportrsquos scheme using thehash function

7 Experimental Results

We have performed an environmental simulation to analysethe results obtained by the proposed fuzzy-based forest firecontroller for a particular dataset of environmental measure-ments FuzzyTECH application has been used to simulate theproposed fuzzy-based forest fire controller (see Figure 9)

As Table 8 shows an environmental dataset has beendefined aiming at providing the content of three differentenvironmental data packages sent by an IoT device Thesepackages are referenced as 1198751 1198752 and 1198753 and composed ofthe last measurement of temperature (119879) humidity (119867) windspeed (119882119904119901119890119890119889) and rainfall (119877) In addition averages (Av) ofevery linguistic variable are included to be analysed Thesevalues are used by the fuzzy-based controller to estimate theexistence of forest fire risks () in that forest area

Fire outbreak detection only requires measures of tem-perature and humidity as meteorological variables

For every environmental data package the oxygen leveland the concentrations of polluting gases are also measuredand included in the dataset as Table 9 shows

Complexity 13

Figure 10 Fuzzified humidity values

Figure 11 Fuzzified carbon dioxide values

Environmental data package 1198751 presents high values oftemperature and polluting gasesThese environmental condi-tions may involve a nearby burning process of biomass wherethe IoT device is located In fact both last carbon dioxideand carbon monoxide concentrations indicate a significantincrease with respect of their typical average concentrationsIn contrast the last environmental measurement of relativehumidity (39) has decreased with respect to the humidityaverage (46) Oxygen level has also decreased from 18 (average) to 165 (last measurement) so increasing theprobability that a recent wildfire incident may be consumingthe oxygen in that forest area On the other hand packages1198752 and 1198753 do not present significant changes between the lastmeasurement and the average of meteorological variables Inaddition measurements of temperature humidity and windspeed do not involve ldquohighrdquo forest fire risks due to complyingwith thresholds proposed by the rule of 30 aforementionedHowever package 1198753 presents polluting gases concentrationshigher than package 2 Therefore the result of fire outbreaksoccurrence () provided by the fuzzy system should behigher with respect to package 1198752

Discrete values of every environmental package arefuzzified into the corresponding membership function Forexample Figure 10 shows the last measurement of humidity(package 1198751) fuzzified into the proposed humidity member-ship function ldquoLowrdquo (59) and ldquonormalrdquo (40) fuzzy valuesare obtained for a humidity discrete value of 39

Figure 11 shows the last measurement of carbon dioxide(package 1198753) fuzzified into the membership function pro-posed for CO2

Thus the input fuzzification step is applied on every valueof the proposed dataset Table 10 shows all the obtained fuzzyvalues and their levels of membership On the left the lastmeasurements of every variable are fuzzified On the rightthe same process is applied to the averages

The fuzzified values are evaluated on the basis of theproposed knowledge base and FAMs for every linguisticvariable The aim is to analyse the existence of forest firerisks and fire outbreaks depending on existing unusualchanges between the last measurements and the averagesthat compose the dataset For every proposed environmentalpackage Figure 12 shows the result of aggregating all theobtained outputs into the membership function proposed forthe output variable related to the existence of forest fire risks

Outputs related to the probability that a wildfire incidenthas recently occurred are also aggregated into another outputset (see Figure 13) These aggregated outputs are defuzzifiedby the Centroid method Regarding the environmental data1198751 56458 has been obtained as the result of the defuzzifi-cation process applied to the aggregated output set associatedwith fire outbreaks occurrence

Before defuzzification the output set involved 87ldquoextremerdquo 84 ldquohighrdquo 59 ldquolowrdquo and 40 ldquononexistentrdquoof probabilities that a wildfire incident may have recentlyoccurred

Therefore an unusual increase in polluting gas con-centrations and temperature together with the decrease inhumidity and oxygen levels show a probability higher than50 of fire outbreak occurrence In contrast 24074 valuehas been obtained for 1198752 after the defuzzification process

14 Complexity

Table 10 Fuzzy values obtained by the fuzzifier

119875 Variable Value Fuzzy values Variable Value Fuzzy value

1

119879 416 High(84)Extreme (15) Av 119879 37 High(100)119867 39 Low(59)Normal(40) Av 119867 46 Normal(100)

119882119904119901119890119890119889 55 High(74) Medium(25) Av 119882119904119901119890119890119889 30 Medium(100)119877 10 Medium(100) Av 119877 95 Medium(100)1198742 165 Low (100) Av 1198742 18 Normal(33)Low(66)1198621198742 876 High(12)Extreme(87) Av 1198621198742 592 High(100)119862119874 478 Extreme(77) High(22) Av 119862119874 136 Medium(79)High(20)

2

119879 28 Low(40) Medium(60) Av 119879 25 Low(100)119867 57 Normal(100) Av 119867 54 Normal(100)

119882119904119901119890119890119889 23 Low(35)Medium(64) Av 119882119904119901119890119890119889 25 Low(24)Medium(75)119877 147 Medium(32)High(67) Av 119877 12 Medium(100)1198742 21 Normal(100) Av 1198742 20 Normal(100)1198621198742 395 Normal(100) Av 1198621198742 350 Normal(100)119862119874 10 Medium(100) Av 119862119874 7 Medium(69)Normal(30)

3

119879 22 Low(100) Av 119879 222 Low(100)119867 6319 Normal(54)High(45) Av 119867 6442 Normal(55)High(44)

119882119904119901119890119890119889 75 Low(100) Av 119882119904119901119890119890119889 95 Low(100)119877 2883 High(100) Av 119877 3252 High(74) Extreme(25)1198742 191 Normal(69)Low(30) Av 1198742 2135 Normal(100)1198621198742 546 High(100) Av 1198621198742 454 Normal(46) High(53)119862119874 1761 Medium(29)High(70) Av 119862119874 147 Normal(85)Medium(14)

Figure 12 Forest fire risks () for 1198751 (top left) 1198752 (top right) and 1198753 (bottom middle)

With respect to this the aggregated output set of 1198752 involved100 ldquononexistentrdquo and 69 ldquolowrdquo of probabilities withregard to recent wildfire incidents occurrence Regarding1198753 100 ldquononexistentrdquo 29 ldquolowrdquo and 70 ldquohighrdquo ofprobabilities were aggregated for its output set Although

meteorological variables did not involve significant forestfire risks in the same way as 1198752 an unusual increase ofpolluting gases jointly with decreasing oxygen level involvea higher percentage of fire outbreaks occurrence with regardto 1198752

Complexity 15

Figure 13 Fire outbreak occurrence () for 1198751 (top left) 1198752 (top right) and 1198753 (bottom middle)

Table 11 Comparison of results provided by fuzzy-based forest fire controller

Forest Fire risks() Fire outbreaks occurrence()P Defuzzified result Aggregated output set Defuzzified result Aggregated output set

1 50964

Non-existent(40)

56458

Non-existent (40)Low(59) Low (59)High(100) High (84)Extreme(15) Extreme (87)

2 33148

Non-existent(100)

24074

Non-existent(100)Low(67) Low(69)High(32) High(0)Extreme(0) Extreme(0)

3 1111

Non-existent(100)

4151

Non-existent(100)Low(0) Low(29)High(0) High(70)

Extreme(0) Extreme(0)

Finally Table 11 shows an overview of the results providedby the fuzzy-based forest fire controller corresponding to theproposed input environmental dataset Results of preventionmodule (forest fire risks) and those of the detection module(evidence of fire outbreak occurrence) may be related in somecases but not in othersWith respect to this awildfire incidentmay be caused by malicious attackers even when there are noforest fire risks due to the existence of stable environmentalconditions in that forest area In this case the result of thedetection module may indicate ldquohighrdquo or ldquoextremerdquo proba-bilities that a forest fire has begun although the preventionmodule may indicate ldquolowrdquo or ldquononexistentrdquo forest fire risksThis case corresponds to the analysed results of 1198753 and is theconsequence of detecting unusual concentrations of pollutinggases

8 Conclusions and Future Works

This work describes a proposal aimed at performing ashort-term estimation of forest fire risks to enhance theresponse time of emergency corps and existing forest fireprevention detection and monitoring systems In order todo it real-time environmental monitoring of dynamic forestfire risk factors is carried out through WSNs and novel IoTtechnologies

A fuzzy-based forest fire controller has been proposedto analyse measured environmental information aiming atestimating the existence of forest fire risks detecting recentwildfire incidents and activating environmental alertsBesides a decision-making method based on AHP hasbeen used to determine nearby forest areas with favourable

16 Complexity

environmental conditions to be affected by close fireoutbreaks and to favour fire spread

Those elements have been integrated into a Web serviceand a mobile application to improve the coordination ofemergency corps Moreover open data sources have beenintegrated to provide an additional support and externalenvironmental information of interest such as weather datavegetation layers and other forest resources

Special attention has been paid to the implementationof security mechanisms to ensure integrity confidentialityand authenticity of communications between WSN nodesand between any WSN node and the Web service Forthis purpose an authentication method based on Lamportrsquosauthentication scheme and Lamport-Diffie signature hasbeen implemented using SHA-3 hash function and environ-mental information has been encrypted through AES 256 inCBCmode

The proposal described here is part of work in progressSeveral open research lines are the introduction of machinelearning in the WSN in order to provide an enhancement inthe detection of unusual environmental events in every mon-itored forest area For this purpose training environmentaldatasets generated in controlled environments might favoura dynamic configuration process of the fuzzy sets proposedfor every monitored environmental variable and each forestarea Besides the fuzzy-based forest fire risk controller mightbe also improved if new sensors are assembled into theproposed IoT devices so that new variables such as theexistence of smoke or the distance to the fire could bemeasured Finally blockchain is currently highlighting as anovel technology that might be used to favour the planning ofWSN distribution in order to propose decentralized schemesfor authenticating new nodes

Data Availability

No data were used to support this study

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research is supported by the Centre for the Developmentof Industrial Technology the Spanish Ministry of Econ-omy and Competitiveness the Government of the CanaryIslands the CajaCanarias Foundation and the University ofLa Laguna under Grants IDI-20160465 TEC2014-54110-RTESIS- 2015010106 and DIG02-INSITU

References

[1] V Kumar A Jain and P Barwal ldquoWireless sensor networkssecurity issues challenges and solutionsrdquo International Journalof Information and Computation Technology (IJICT) vol 4 no8 pp 859ndash868 2014

[2] C Karlof and D Wagner ldquoSecure routing in wireless sensornetworks attacks and countermeasuresrdquo inProceedings of the 1st

IEEE International Workshop on Sensor Network Protocols andApplications pp 113ndash127 IEEE 2003

[3] L A Zadeh ldquoFuzzy logicmdasha personal perspectiverdquo Fuzzy Setsand Systems vol 281 pp 4ndash20 2015

[4] S H Zanakis A Solomon N Wishart and S Dublish ldquoMulti-attribute decision making a simulation comparison of selectmethodsrdquo European Journal of Operational Research vol 107no 3 pp 507ndash529 1998

[5] L Lamport ldquoPassword authentication with insecure communi-cationrdquo Communications of the ACM vol 24 no 11 pp 770ndash772 1981

[6] M Naor and M Yung ldquoUniversal one-way hash functions andtheir cryptographic applicationsrdquo in Proceedings of the Twenty-First Annual ACM Symposium onTheory of Computing pp 33ndash43 ACM 1989

[7] J Daemen and V Rijmen The Design of Rijndael AES-TheAdvanced Encryption Standard Springer Science amp BusinessMedia 2013

[8] D M N Rajkumar M Sruthi and D V V Kumar ldquoIotbased smart system for controlling co2 emissionrdquo InternationalJournal of Scientific Research in Computer Science Engineeringand Information Technology vol 2 no 2 p 284 2017

[9] MHefeeda andM Bagheri ldquoWireless sensor networks for earlydetection of forest firesrdquo in Proceedings of the IEEE InternatonalConference on Mobile Adhoc and Sensor Systems pp 1ndash6 IEEE2007

[10] S Garcia-Jimenez A Jurio M Pagola L De Miguel E Bar-renechea andHBustince ldquoForest fire detectionA fuzzy systemapproach based on overlap indicesrdquo Applied Soft Computingvol 52 pp 834ndash842 2017

[11] M Maksimovic V Vujovic B Perisic and V MilosevicldquoDeveloping a fuzzy logic based system for monitoring andearly detection of residential fire based on thermistor sensorsrdquoComputer Science and Information Systems vol 12 no 1 pp 63ndash89 2015

[12] S Eskandari ldquoA new approach for forest fire risk modelingusing fuzzy AHP andGIS in Hyrcanian forests of IranrdquoArabianJournal of Geosciences vol 10 no 8 p 190 2017

[13] CKahramanUCebeci andZUlukan ldquoMulti-criteria supplierselection using fuzzy AHPrdquo Logistics Information Managementvol 16 no 6 pp 382ndash394 2003

[14] M A Khan and K Salah ldquoIoT security Review blockchainsolutions and open challengesrdquo Future Generation ComputerSystems vol 82 pp 395ndash411 2018

[15] S D C di Vimercati A Genovese G Livraga V Piuri andF Scotti ldquoPrivacy and security in environmental monitoringsystems issues and solutionsrdquo in Computer and InformationSecurity Handbook pp 835ndash853 Elsevier 2nd edition 2013

[16] A K Pathan H-W Lee and C S Hong ldquoSecurity in wirelesssensor networks issues and challengesrdquo inProceedings of the 8thInternational Conference Advanced Communication Technology(ICACT rsquo06) vol 2 pp 1043ndash1048 IEEE 2006

[17] K Akkaya and M Younis ldquoA survey on routing protocols forwireless sensor networksrdquo Ad Hoc Networks vol 3 no 3 pp325ndash349 2005

[18] M T Lazarescu ldquoDesign of a WSN platform for long-termenvironmental monitoring for IoT applicationsrdquo IEEE Journalon Emerging and Selected Topics in Circuits and Systems vol 3no 1 pp 45ndash54 2013

[19] S Rani and S H AhmedMulti-Hop Routing in Wireless SensorNetworks An Overview Taxonomy and Research ChallengesSpringer 2015

Complexity 17

[20] E H Mamdani ldquoApplication of fuzzy logic to approximatereasoning using linguistic synthesisrdquo in Proceedings of the SixthInternational Symposium on Multiple-Valued Logic vol 26 pp196ndash202 IEEE Computer Society Press 1976

[21] B Kosko ldquoFuzzy associative memoriesrdquo in Fuzzy Expert Sys-tems 1987

[22] T A Runkler ldquoSelection of appropriate defuzzificationmethodsusing application specific propertiesrdquo IEEE Transactions onFuzzy Systems vol 5 no 1 pp 72ndash79 1997

[23] C Funai C Tapparello and W Heinzelman ldquoEnabling multi-hop ad hoc networks through WiFi Direct multi-group net-workingrdquo in Proceedings of the 2017 International Conferenceon Computing Networking and Communications (ICNC rsquo17) pp491ndash497 IEEE 2017

[24] M J Dworkin ldquoSHA-3 standard permutation-based hash andextendable-output functionsrdquo Tech Rep Federal Inf ProcessStds (NIST FIPS)-202 National Institute of Standards andTechnology 2015

[25] M J Dworkin ldquoRecommendation for block cipher modes ofoperation methods and techniquesrdquo Tech Rep NIST SP 800-38a National Institute of Standards and Technology Gaithers-burg MD Computer Security Division 2001

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AnalysisInternational Journal of


Stochastic AnalysisInternational Journal of

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2 Complexity

relevant fire propagation factors such as dryness of vegetationand organic fuels

A wireless sensor network (WSN) [1] based on Internetof Things (IoT) devices and sensors can be used to performa real-time environmental monitoring of the aforementionedforest fire risk factorsTheir design and distributionhave to beaddressed aiming at covering asmuch forest areas as possibleWith respect to this several challenges must be consideredsuch as the authentication of sensor nodes [2] and the securityof wireless communications among distributed sensor nodestaking into account possible areas out of network coverage

Due to the uncertainty in environmental data under-standing environmental changes to estimate the existence offire risks or to detect the occurrence of a wildfire incidentis not a simple process that can be executed with completeaccuracy Fuzzy logic [3] and decision-making methods suchas the Analytic Hierarchy Process (AHP) [4] can be usedto provide an enhancement in the real-time analysis ofenvironmental data Forest fire prevention and detection maybe more accurate through the interpretation of the forestfire risks involved in every measured environmental variablejointly with unusual environmental changes with respect tothe typical values measured by a WSN

The main goal of the proposal here described is toestimate in short-term the existence of forest fire risks and todetect the recent occurrence of fire outbreaks over differentforest areas For this purpose a forest fire controller basedon fuzzy logic has been implemented aiming at analysingenvironmental information such asmeteorological variablespolluting gases and the oxygen level measured by a dis-tributed WSN To this end a particular prototype of IoTdevice equipped with environmental sensors has been imple-mented When a fire outbreak is detected a decision-makingmethod based on AHP is enabled to determine the neigh-bouring forest area that is more likely to favour fire spread asa result of its current environmental conditions Moreover aWeb service and amobile application have been implementedaiming at activating environmental alerts Besides open datasources have been integrated to provide other relevant envi-ronmental information such as vegetation layers or historicalinformation of recent fires Particular attention has been paidto the application of security mechanisms to protect theintegrity confidentiality and authenticity of measured envi-ronmental information and alerts through implementationsof Lamportrsquos authentication scheme [5] Diffie-Lamportrsquossignature [6] and AES-CBC block cipher [7]

This work is organized as follows Section 2 deals withsome related works Section 3 details the proposed forest firecontroller based on fuzzy logic Section 4 outlines the AHP-based detection method of fire spread Then the proposedsystem is explained in Section 5 and the implemented secu-rity mechanisms are sketched in Section 6 Section 7 includesa description of several experimental results Finally someconclusions and research works in progress are given inSection 8

2 Related Works

In the last years different proposals have been put forwardto improve forest fire prevention detection and extinction

systems Many of those solutions are based on real-timeenvironmental monitoring and IoT devices With respect tothis the work [8] includes the implementation of a smartsystem aiming at measuring carbon dioxide (CO2) emissionsfrom different sources such as forest fires through usingRaspberry Pi In addition to monitoring polluting gasesother proposals analyse the so-called Fire Weather Index fordesigning an efficient fire detection system through wirelesssensor networks and a simple data aggregation scheme[9]

Nowadays the combination of fuzzy logic and decision-makingmethods such asAHP produces innovative solutionsthat may enhance the accuracy in the prevention and detec-tion of wildfire incidents

The work [10] proposes a fuzzy system based on overlapindices to improve forest fire detection through imple-menting a wireless sensor network and analysing differentvariables such as the lightness and the distance to the fireIn that work a particular generalization of the Mamdaniinference system is introduced by using overlap functionsand overlap indices Likewise the work [11] also proposesthe use of WSNs and the incorporation of fuzzy logic insensor nodes but its aim is to estimate the evidence of firethrough analysing the previous temperature and the currenttemperature For that purpose two fuzzy approaches basedon temporal characteristics are proposed to optimize thenumber of rules that have to be checked

Regarding the use of decision-making algorithms thework [12] includes a model of the forest fire risk throughintegrating fuzzy sets with AHP In particular it uses adecision-making method including the Geographic Infor-mation System and the fuzzy AHP method [13] to estimatethe importance related to each considered causative factor inforest fires

The security and the distribution of the WSN requireparticular attention [14 15] Several security challenges andthreats are addressed in [16] with respect to wireless com-municationThe survey [17] includes recent routing protocolsand presents a classification in categories such as data-centrichierarchical and location-based Likewise the functionaldesign and the implementation of a complete WSN platformare presented in [18] aiming at performing a long-termenvironmental monitoring Low cost minimum number ofsensors fast deployment and other requirements are alsoconsidered in the approach of WSN design in differentworks

Differently from the aforementioned works the systemdescribed here proposes the combination of WSN fuzzylogic decision-making methods multihop routing [19] andsecurity mechanisms for performing a secure real-time envi-ronmental monitoring of dynamic forest fire risk factorsThemain aim is to estimate the existing forest fire risks in differentmonitored forest areas and to detect the occurrence offire outbreaks Moreover a decision-method based on AHPintended to determine the fire spread through nearby forestareas has been implemented in a system composed by a Webservice and a mobile application to manage environmentalalerts and provide an enhancement in forest fire preventiondetection and tracking systems

Complexity 3

Input Values


fuzzifiedoutputs

aggregated

Low


High Extreme

Rules Inference

Outputs


Centroid method




Criteria

Vegetation


Aggregation of

Defuzzification








V







4 Complexity

0010203040506070809

1

0 25 30 34 37 41 45 100

Temperature

0010203040506070809

1


Relative Humidity

0010203040506070809

1

0 10 30 40 60 80 100 240

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

pLe

vel o

f mem

bers

hip

Leve

l of m

embe

rshi

p

km h

Wind speed

lowmedium

highextreme

lowmedium

highextreme

very lowlow

normalhigh

lowmedium

highextreme

0010203040506070809

1

0 65 12 16 30 40 100mm

Rainfall









Complexity 5

0010203040506070809

1


Carbon dioxide

lownormal

highextreme

0010203040506070809

1

0 10 12 20 40 50 100

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

p


Carbon monoxide

normalmedium

highextreme

0010203040506070809

1

0 12 15 17 20 23 26 30

Leve

l of m

embe

rshi

p

Oxygen percentages

Oxygen

very lowlow

normalhigh






isin [119886 119887] |4

sum119894=1

120583119865119878V119894 (120572V) = 100(1)




6 Complexity



0010203040506070809

1

0 33334 66666 100percentages ()

Forest fire risks

nonexistentlow

highextreme

0010203040506070809

1

0 33334 66666 100

Leve

l of m

embe

rshi

p

percentages ()


nonexistentlow

highextreme

Leve

l of m

embe

rshi

p









Complexity 7



Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n












isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)


8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 3

Input Values


fuzzifiedoutputs

aggregated

Low


High Extreme

Rules Inference

Outputs


Centroid method




Criteria

Vegetation


Aggregation of

Defuzzification








V







4 Complexity

0010203040506070809

1

0 25 30 34 37 41 45 100

Temperature

0010203040506070809

1


Relative Humidity

0010203040506070809

1

0 10 30 40 60 80 100 240

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

pLe

vel o

f mem

bers

hip

Leve

l of m

embe

rshi

p

km h

Wind speed

lowmedium

highextreme

lowmedium

highextreme

very lowlow

normalhigh

lowmedium

highextreme

0010203040506070809

1

0 65 12 16 30 40 100mm

Rainfall









Complexity 5

0010203040506070809

1


Carbon dioxide

lownormal

highextreme

0010203040506070809

1

0 10 12 20 40 50 100

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

p


Carbon monoxide

normalmedium

highextreme

0010203040506070809

1

0 12 15 17 20 23 26 30

Leve

l of m

embe

rshi

p

Oxygen percentages

Oxygen

very lowlow

normalhigh






isin [119886 119887] |4

sum119894=1

120583119865119878V119894 (120572V) = 100(1)




6 Complexity



0010203040506070809

1

0 33334 66666 100percentages ()

Forest fire risks

nonexistentlow

highextreme

0010203040506070809

1

0 33334 66666 100

Leve

l of m

embe

rshi

p

percentages ()


nonexistentlow

highextreme

Leve

l of m

embe

rshi

p









Complexity 7



Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n












isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)


8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








4 Complexity

0010203040506070809

1

0 25 30 34 37 41 45 100

Temperature

0010203040506070809

1


Relative Humidity

0010203040506070809

1

0 10 30 40 60 80 100 240

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

pLe

vel o

f mem

bers

hip

Leve

l of m

embe

rshi

p

km h

Wind speed

lowmedium

highextreme

lowmedium

highextreme

very lowlow

normalhigh

lowmedium

highextreme

0010203040506070809

1

0 65 12 16 30 40 100mm

Rainfall









Complexity 5

0010203040506070809

1


Carbon dioxide

lownormal

highextreme

0010203040506070809

1

0 10 12 20 40 50 100

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

p


Carbon monoxide

normalmedium

highextreme

0010203040506070809

1

0 12 15 17 20 23 26 30

Leve

l of m

embe

rshi

p

Oxygen percentages

Oxygen

very lowlow

normalhigh






isin [119886 119887] |4

sum119894=1

120583119865119878V119894 (120572V) = 100(1)




6 Complexity



0010203040506070809

1

0 33334 66666 100percentages ()

Forest fire risks

nonexistentlow

highextreme

0010203040506070809

1

0 33334 66666 100

Leve

l of m

embe

rshi

p

percentages ()


nonexistentlow

highextreme

Leve

l of m

embe

rshi

p









Complexity 7



Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n












isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)


8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 5

0010203040506070809

1


Carbon dioxide

lownormal

highextreme

0010203040506070809

1

0 10 12 20 40 50 100

Leve

l of m

embe

rshi

p

Leve

l of m

embe

rshi

p


Carbon monoxide

normalmedium

highextreme

0010203040506070809

1

0 12 15 17 20 23 26 30

Leve

l of m

embe

rshi

p

Oxygen percentages

Oxygen

very lowlow

normalhigh






isin [119886 119887] |4

sum119894=1

120583119865119878V119894 (120572V) = 100(1)




6 Complexity



0010203040506070809

1

0 33334 66666 100percentages ()

Forest fire risks

nonexistentlow

highextreme

0010203040506070809

1

0 33334 66666 100

Leve

l of m

embe

rshi

p

percentages ()


nonexistentlow

highextreme

Leve

l of m

embe

rshi

p









Complexity 7



Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n












isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)


8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








6 Complexity



0010203040506070809

1

0 33334 66666 100percentages ()

Forest fire risks

nonexistentlow

highextreme

0010203040506070809

1

0 33334 66666 100

Leve

l of m

embe

rshi

p

percentages ()


nonexistentlow

highextreme

Leve

l of m

embe

rshi

p









Complexity 7



Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n












isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)


8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 7



Neighboringnode 1

Neighboring node 2

Neighboring node 3

Neighboring node n












isin [0∘ 360∘] 119904119906119888ℎ 119905ℎ119886119905

119909 = min (1003816100381610038161003816119871119900119888 (119899) minus 120572119908119894119899119889 1198891198941199031198901198881199051198941199001198991003816100381610038161003816) 997888rarr

exist119910 isin 119890119909119905119903119890119898119890119897119910 119899119890119886119903 V119890119903119910 119899119890119886119903 119899119890119886119903

119898119900119889119890119903119886119905119890119897119910 119899119890119886119903 119891119886119903 | 119910 = 119865119906119911119911119910119891119894119903119890 119904119901119903119890119886119889 (119909)

(2)


8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








8 Complexity

North

a b


a = 40 ∘ b = 93 ∘ wind_dir = 18 ∘rArr | - 18| = 22∘

|＜ - 18| = 75∘




Extremely 100 Near

Very Near 0

0

0

0

Near

Moderately



Far


0

01

02

03

04

05

06

07

08

09

1

0 1125 225 3375 45 5625 675 7875 90 10125 1125 12375 135 14625 1575 180

Leve

l of m

embe

rshi

p

degrees (∘)







5 Proposed System






Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 9







119879 ⟨V119886119897119906119890⟩ 119867 ⟨V119886119897119906119890⟩ 119882119904119901119890119890119889 ⟨V119886119897119906119890⟩ 119882119889119894119903119890119888119905119894119900119899 ⟨V119886119897119906119890⟩

119877 ⟨V119886119897119906119890⟩

1198742 ⟨V119886119897119906119890⟩ 1198621198742 ⟨V119886119897119906119890⟩ 119862119874 ⟨V119886119897119906119890⟩

119861119886119905119905119890119903119910119871119890V119890119897 ⟨V119886119897119906119890⟩ 119871119886119905 ⟨V119886119897119906119890⟩ 119871119899119892 ⟨V119886119897119906119890⟩

(3)








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








10 Complexity



WSN sensornodes


Forest firerisks


Open data

Vegetationmap


Forest Tracks

Waterresources

Static forest data


Environmentalalerts

Emergencycorps

Location

Emergency corpsdata




6 System Security







Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 11






verification

Database update

New public key

Data package



435263 3536236 73635


T 12∘C H 46 CO2 370

（Ｈ - Ｃ ( Ｑ )

H n - i( w )

Original message

Secret value (w)


H (w)

Private publickeys

- i ( )

( )

H ( H Ｈ - i (w) ) (w)= H Ｈ - i + 1









119867(119867119899minus119894 (119908)) = 119867119899minus119894+1 (119908) 997888rarr

119867(119867100minus2 (119908)) = 119867100minus2+1 (119908) 997888rarr

119867(11986798 (119908)) = 11986799 (119908)

(4)





12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








12 Complexity




119875 119879 119867 119882119904119901119890119890119889 119877 Av 119879 Av 119867 Av 119882 Av 1198771 416 39 55 10 37 46 30 952 28 57 23 147 25 54 25 123 22 6319 75 2883 222 6442 95 3252


119875 1198742 1198621198742 119862119874 Av 1198742 Av 1198621198742 Av 1198621198741 165 876 478 18 592 1362 21 395 10 20 350 73 191 546 1761 2135 454 147









Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 13











14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








14 Complexity



1



2



3






Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 15




1 50964

Non-existent(40)

56458


2 33148

Non-existent(100)

24074


3 1111

Non-existent(100)

4151







16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








16 Complexity





Data Availability




Acknowledgments


References





















Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018








Complexity 17














Journal of












Journal of







Volume 2018






Volume 2018















Journal of












Journal of







Volume 2018






Volume 2018








forest fire prevention, detection, and fighting based on fuzzy … · 2019. 7. 30. · forest fire...

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