Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets

Abstract

Abstract: Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions.These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO,CO2 and O2 vis- à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy
logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws,policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.

Full text

symmetry
S
S
Article
Design of an IoT-Based Fuzzy Approximation
Prediction Model for Early Fire Detection to Aid
Public Safety and Control in the Local Urban Markets
Emmanuel Lule 1, *, Chomora Mikeka 2, Alexander Ngenzi 1and Didacienne Mukanyiligira 1
1African Center of Excellence in Internet of Things (ACEIoT), College of Science & Technology (C.S.T.),
University of Rwanda, Nyarugenge P.O. Box 3900, Kigali, Rwanda; a.ngenzi@ur.ac.rw (A.N.);
d.mukanyirigira@ur.ac.rw (D.M.)
2Chancellor College, Faculty of Science, Department of Physics, University of Malawi (UNIMA),
Zomba P.O. Box 280, Malawi; cmikeka@cc.ac.mw
*Correspondence: lule.emmanuel@cis.mak.ac.ug; Tel.: +250-724801257
Received: 28 June 2020; Accepted: 20 July 2020; Published: 21 August 2020


Abstract:
Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected
for a long time. This has culminated in a severe destruction of life and property worth millions.
These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research
proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient.
Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model
for early fire detection and control as symmetry’s core contribution to fuzzy systems design and
application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the
performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO,
CO
2
and O
2
vis-
à
-vis the Estimated Fire Intensity Prediction (
EFIP
). Results show that, using fuzzy
logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%.
The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal
of minimizing extensive damage and promote intermediate fire suppression and control through true
fire incidences. This solution provides for future public safety monitoring, and control of fire-related
situations among the market community. Hence, fire safety monitoring is significant in providing
future fire safety planning, control and management by putting in place appropriate fire safety laws,
policies, bills and related fire safety practices or guidelines to be applied in public buildings, market
centers and other public places.
Keywords:
Internet of Things (IoT); fuzzy logic; Fuzzy Associative Memory (
FAM
); estimated fire
intensity prediction (EFIP); gas combustion efficiency (GCE)
1. Introduction
With the recent development and advancement in Internet of Things (IoT) technology, it is estimated
that, by the year 2020, approximately 50 billion devices shall be connected to the internet [
1
,
2
]. Therefore,
the development of IoT-based platforms shall subsequently provide the fire area with an opportunity
to develop low-cost, effective and reliable solutions to combat the recurrent fire-related situations in the
local urban markets within the East Africa (EA) region before severe disastrous consequences emerge.
EA’s regional urban markets have suffered severe fire outbreaks within the last 10 years. The major
cause of these fire outbreaks is mainly attributed to electrical short circuits, fuel spillage, carelessly
neglected charcoal stoves and suspected arson, among others [
3
–
5
]. Urban markets, however, lack a
comprehensive contingency plan to manage, contain and safeguard against any fire-related phenomena
despite previous encounters. This has resulted in a severe destruction of both human life and property
Symmetry 2020,12, 1391; doi:10.3390/sym12091391 www.mdpi.com/journal/symmetry

Symmetry 2020,12, 1391 2 of 29
worth millions. The fire and rescue departments still rely on the traditional methods of human patrol
observations in dealing with fire situations making them quite obsolete, inaccurate and inefficient for
fire safety detection [
3
]. In the Figure 1, we show the percentage frequencies of fire occurrences for the
selected local urban markets within the (EA) region, namely, Owino in Uganda, Gikomba in Kenya
and Nyanza or Gisozi in Rwanda for the last 10 years (2009–2019):
Symmetry 2020, 12, x FOR PEER REVIEW 2 of 29
against any fire-related phenomena despite previous encounters. This has resulted in a severe
destruction of both human life and property worth millions. The fire and rescue departments still
rely on the traditional methods of human patrol observations in dealing with fire situations making
them quite obsolete, inaccurate and inefficient for fire safety detection [3]. In the Figure 1, we show
the percentage frequencies of fire occurrences for the selected local urban markets within the (EA)
region, namely, Owino in Uganda, Gikomba in Kenya and Nyanza or Gisozi in Rwanda for the last
10 years (2009–2019):
Figure 1. Percentage (%) frequencies of fire occurrences in selected urban markets within the East
Africa (EA) region between (2009–2019) [4,5].
Embedded computing and micro-electromechanical (MEMs) systems through environmental
science shall provide information through sensors to enhance the existing fire monitoring and
detection applications by gathering sensed data, process and transmit it for purposes of modeling
and analysis for appropriate decision making [6]. Tremendous research efforts in fire detection have
been made. For instance, Sowah at el. in [7], proposed a fuzzy-based multisensor fire detection system
and a web-based notification platform. The authors used both the fuzzy and trained convolutional
neural networks (CNN) for early fire detection as a deep learning technique with the ability to
perform feature extraction and classification. The CNN method enables a broader coverage area of
interest. Results show that using the CNN method significantly improves fire detection and alerting
response, with an accuracy rate of 94%.
Güllüce at el.in [8], propose a smart fire detection system using infrared technology and
mathematical modeling algorithms. The authors use geolocation and behavior to estimate the spatial
resolution by superimposing the detection areas with infrared detectors. Meanwhile, the
mathematical models position the spatial resolution detectors in estimating the coordinates of the
forest fires using the libraries of Google Maps APIs in the cloud. The fire geolocation and behavior
are simulated using software called Fire Analyst. Experimental results show that monitoring fires with
fire analyst using multispectral infrared technology outperformed the other fire monitoring systems.
The method provides a shortened fire detection time frame with an observed high spatial resolution
of up to 4.5 m and geolocation of approximately 3599.56 m2. Previous researchers proposed the use
of conventional fire control alert systems like smoke detectors. However, these alert systems are
highly susceptible to false alarms with limited protection depending on the type of fire [7,9]. Davis,
Hislop at el. in [10,11] propose the use of satellite systems. However, these are quite expensive to
acquire and maintain for developing countries. Therefore, this research approach seeks to propose a
fuzzy-based approximation prediction model for early fire outbreak detection in order to safeguard
the market community from severe destruction of property.
The novel idea is to apply fuzzy logic in early fire detection to significantly improve the accuracy
to 95.83% consisting of true positives. This shall enhance control, by way of minimizing extensive
damage and safeguard the public against potentially prevailing harmful fires. Fuzzy logic control
technique is aimed at providing an accurate real time decision making through early warning signals
Figure 1.
Percentage (%) frequencies of fire occurrences in selected urban markets within the East
Africa (EA) region between (2009–2019) [4,5].
Embedded computing and micro-electromechanical (MEMs) systems through environmental
science shall provide information through sensors to enhance the existing fire monitoring and detection
applications by gathering sensed data, process and transmit it for purposes of modeling and analysis
for appropriate decision making [
6
]. Tremendous research efforts in fire detection have been made.
For instance, Sowah at el. in [
7
], proposed a fuzzy-based multisensor fire detection system and a
web-based notification platform. The authors used both the fuzzy and trained convolutional neural
networks (CNN) for early fire detection as a deep learning technique with the ability to perform
feature extraction and classification. The CNN method enables a broader coverage area of interest.
Results show that using the CNN method significantly improves fire detection and alerting response,
with an accuracy rate of 94%.
Güllüce at el.in [
8
], propose a smart fire detection system using infrared technology and
mathematical modeling algorithms. The authors use geolocation and behavior to estimate the spatial
resolution by superimposing the detection areas with infrared detectors. Meanwhile, the mathematical
models position the spatial resolution detectors in estimating the coordinates of the forest fires using
the libraries of Google Maps APIs in the cloud. The fire geolocation and behavior are simulated
using software called Fire Analyst. Experimental results show that monitoring fires with fire analyst
using multispectral infrared technology outperformed the other fire monitoring systems. The method
provides a shortened fire detection time frame with an observed high spatial resolution of up to 4.5 m
and geolocation of approximately 3599.56 m
2
. Previous researchers proposed the use of conventional
fire control alert systems like smoke detectors. However, these alert systems are highly susceptible to
false alarms with limited protection depending on the type of fire [7,9]. Davis, Hislop at el. in [10,11]
propose the use of satellite systems. However, these are quite expensive to acquire and maintain for
developing countries. Therefore, this research approach seeks to propose a fuzzy-based approximation
prediction model for early fire outbreak detection in order to safeguard the market community from
severe destruction of property.
The novel idea is to apply fuzzy logic in early fire detection to significantly improve the accuracy
to 95.83% consisting of true positives. This shall enhance control, by way of minimizing extensive
damage and safeguard the public against potentially prevailing harmful fires. Fuzzy logic control
technique is aimed at providing an accurate real time decision making through early warning signals

Symmetry 2020,12, 1391 3 of 29
for possible public safety control, suppression and immediate evacuation. Hence, there is a need
to design low-cost, effective, fuzzy-based fire prediction models using the IoT platform to improve
existing firefighting techniques with a major goal of minimizing damage and false alarm rates.
Therefore, in order to ensure appropriate event reporting responses of the related fire calamities,
there is need to develop effective fire detection systems to ensure a prompt responses, proper
management and containment of the ever-recurring fire calamities in local urban markets [
12
].
Thus, the proposed fuzzy model plays a significant contribution to symmetry’s area of computing and
engineering science.
The rest of the paper is structured as follows: Section 2, related works; in Section 3, problem
description and modeling; in Section 4, simulation experiment setup; Section 5, results and discussion,
where obtained results are comprehensively discussed; and, in Section 6, conclusion and future works.
2. Related Works
The state-of-the-art research findings illustrate several related works done in the area pertaining
fire monitoring and detection using the fuzzy logic-based technique. For instance, researchers in [
13
]
proposed GIS-based forest-fire risk mapping in the forested areas of Iran prone to high risk fire
occurrences by combining two techniques, namely analytical network processing (ANP) and fuzzy
logic, to generate a fire risk map. The occurrence of a forest fire is determined using an ANP
ranking procedure yielding a criteria weight, while the fuzzy logic assesses the weight of subcriteria.
Then the GIS-based aggregation module function is applied to generate an appropriate fire risk map.
Results indicate that a high-risk accuracy of 81.9% is obtained using the proposed fuzzy ANP model.
B. Sarwar at el in [
14
] proposed an IoT-based intelligent warning application using the adaptive neural
fuzzy inference systems (ANFIS) to compute the true likelihood of a fire presence and then generate a
fire alert. The novel idea was to use an ANFIS technique in the identification of a true presence of
a fire incident by considering the following parameters: the change rate of smoke, the change rate
of temperature and humidity. Sensors collect vital data from the sensor nodes and the fuzzy logic
technique converts the raw data into linguistic variables trained by the ANFIS to get the probability
of a fire occurrence. The proposed experiment shows satisfactory output results. In addition, in [
15
],
Sarwar et al. proposed the design and application of an intelligent fire monitoring and warning system
(FMWS) using fuzzy logic to predict a fire outbreak. The system sends out alert messages using
the Global System for Mobile Communication (GSM) technology. The authors considered three key
performance parameters, i.e., smoke, flame and temperature. However, only three parameters are not
sufficient for effective fire detection as this may result in false alarms and further is not IoT-related.
The designed FMWS was simulated using MATLAB version 7.1 and results show that the system
was successful.
T. Listyorini at el. in [
16
], proposed an IoT-enabled fire detection tool based on the concept of
fuzzy logic in the peatland area of Riau, Indonesia. The proposed prototype detects the presence of
fire hotspots and analyzes fire intensity using one parameter, temperature, and any noticeable change
of abnormal increase in temperature triggers an early fire warning which then sends a notification
message to the supervisors for immediate action. However, only one parameter cannot be sufficient
for a true identification of a fire presence. M. Samadi at el. in [
17
] proposed a knowledge-based system
called a fire suffocation and burn (FSB) system, which employs a fuzzy decision making, multi criteria
decision making (MCDM) and an RGB model. The system is able to predict the presence of a fire
occurrence, suffocation and burn probabilities based on the sensed data from different clusters of the
network. Sensed data from smoke, temperature and light sensors is ultimately processed in order to
determine an appropriate decision of the prevailing environmental condition. Simulation results show
that the proposed system surpasses the threshold methods in terms of energy efficiency, network time
and financial losses. The system can be used in various areas such as buildings or forests etc.

Symmetry 2020,12, 1391 4 of 29
In [
18
], H. Kaur at el. proposed a three-tier architecture for early detecting and mitigation of wild
forest fires. The architecture consists of i) a data perception layer, responsible for collecting forest
fire phenomena, environmental conditions and location related data. The aggregated data is then
forwarded to the ii) fog computing (FC) layer, responsible for real time analysis and processing of
data collected by the IoT sensors to determine a predict an early forest fire outbreak. iii) The event
classification component is responsible for classification of an event into wild fire oriented datasets to
be detected, i.e., fire detected event or a no fire event, using a k-means clustering and adaptive neuro
fuzzy inference system (ANFIS) for computing the fire vulnerability analysis.
Upon detection, an early warning signal is sent to the fire department for immediate suppression
and control of the wild fires. Experimental results show that, using k-means clustering outperforms
other clustering techniques by 93.41% with a lower classification detection time of wild forest fires,
hence mitigating their adverse effects. Researchers in [
19
] proposed a fire detection and control system
using fuzzy logic technique with feedback over an Arduino micro controller system. The automated
system consists of flame, temperature, smoke sensors and a re-engineered mobile carbon dioxide air
conditioning unit was tested on a medium physical car. Results show, that automobile fire detection
system is devoid of false alarms and able to detect and suppress fire within 20 s.
Hislop et al. in [
11
] demonstrated the use of a satellite data driven approach to monitor and
report fire incidences across boreal-forest-covered areas. Researchers used MODIS and Landsat data to
explore trends in fire disturbances across the boreal forest cover. Research showed that between 2001
and 2018, 9% of the forest was burned as detected by the MODIS satellites. The Google Land Earth
Engine was used to sample thousands of Landsat images to further observe trends and patterns in
fire severity and forest recovery. Results indicated that satellite data together with cloud computing
can be used to harness the survey baselines to reveal trends and patterns so as to improve forest fire
monitoring and reporting at both national and global scale.
L. Salhi et al. in [
20
] proposed a gas and fire detection system in a smart home using a machine
learning technique. The authors applied the following environmental parameters in the experiment:
temperature, humidity, smoke, CO, CO
2
, LPG and flame. The suggested method employs data mining
to detect an abnormal change in pattern of the considered above parameters and then sends a warning
signal to the relevant person. Results show that the proposed solution improved the accuracy of the
model with reduced false positives Nevertheless, the proposed solution is not IoT-related. T. Sahithi
at el. in [
21
], designed a fire rescue system using IoT to safeguard against fire-related accidents in
the lonely houses. When a fire incident is detected by the sensors, immediately data is sent to the
pic18 microcontroller board and intimation is sent to the nearest fire or police station using an android
application. However, the proposed system doesn’t use a fuzzy logic approach and a risk of false
alarms is possible. Roberto at el. in [
22
], proposed a software agent that monitors the status of fire
extinguishers by collecting their history and environmental factors and sends a notification if any
parameters (temperature or humidity) are not within a defined range. Results show that the smart fire
prototype is accurate in computing the pressure changes of a specific data acquisition system (DAS).
Jun Hong at el. in [
23
] proposes a new fire detection system with a multifunctional AI framework and
data transfer minimization for the safety of smart cities using machine learning and fuzzy algorithms
for making appropriate decisions. The developed system achieved an accuracy rate of 95%.
3. Problem Description and Modeling
3.1. Problem Definition
Fire outbreaks in local urban markets located within the East Africa (EA) region have raised
serious concern due to their extensive damage to property and human life. The primary causes of these
fire occurrences arise mainly from electrical short circuits, neglected charcoal stoves and fuel spillage,
as detailed in the police annual reports [
3
,
5
]. These markets, however, act as a source of income to
the small-scale vendor community. Hence, there is lack of comprehensive fire control safety systems

Symmetry 2020,12, 1391 5 of 29
and policies in place to protect these markets against the rampant harmful fires despite previous
encounters. These markets still rely on the traditional methods of human patrol mechanisms for fire
detection, rendering them obsolete and inefficient for proper and accurate fire detection. The single
smoke detectors proposed by S. Chen and et al. in [
9
] are highly susceptible to false alarm rates and
inefficient. Furthermore, satellite-based systems [
11
] are quite expensive for developing countries to
acquire and maintain.
Therefore, this research study seeks to propose an IoT-based fuzzy approximation prediction
model for early fire detection in order to provide public safety against recurrent fire threats, minimize
extensive damage and instigate appropriate risk mitigation measures through emergency control
measures by alerting authorities in time for quick evacuation and as well as establishing immediate
fire suppression mechanisms. This approach, utilizes the fuzzy logic-based technique for determining
true fire incidences [
24
]. Hence, the fuzzy logic method consisting of a set of evaluation inference rules
and its natural linguistic terms to apply approximate reasoning in order to determine the true accuracy
rate of fire, as in Zadeh (1974) (1975) [25,26].
3.2. Materials and Methods
In this paper, we applied a simulated approach using a fuzzy-logic-based technique embedded
within the MATLAB simulation environment. The MATLAB Fuzzy Logic toolbox was then used to
simulate the proposed model with more accuracy, scalability and flexibility. We then modeled and
observed the performance dynamic behavior of the proposed fuzzy prediction model using a set of
six input parameters, namely, rate of change of temperature (
∆
T), rate of change of humidity (
∆
H),
flame presence, rate of change of carbon dioxide levels (
∆
CO
2
), rate of change of carbon monoxide
levels (
∆
CO) and rate of change of oxygen levels (
∆
O
2
). We considered the estimated fire intensity
prediction (EFIP) as the output parameter to determine the percentage probability likelihood of a fire
occurrence. In order to process the fuzzy logic model, we utilized a rule-based Mamdani’s fuzzy
inference system (FIS) and then applied defuzzification processes.
3.3. Fuzzy Approximation Modeling
Fuzzy Logic
Fuzzy logic is defined by multivalued artificial intelligence or soft computing technique that is
based on the principle of the degrees of truth that range between 0 and 1 both inclusive. Also, a fuzzy
set can be referred to as an initial set that defines the uncertain crisp value[x] and a corresponding
membership value[
µ
] in the range of [0,1]. Hence, fuzzy models are based on fuzzy inference rules in
order to model and evaluate nonlinear systems with complex and dynamic engineering problems [
27
].
This research approach employs fuzzy approximation modeling technique in order to determine or
estimate the fire intensity decision status from a set of fuzzy input within a given domain range [28].
Fuzzy logic was originally proposed by Dr. Loft Zadeh of the University of California in the
1960s. The technique has been widely used in solving real life complex soft computing problems.
Fuzzy optimization theory is based on the principle of fuzzy sets that has significantly developed into
fuzzy approximation reasoning. Hence, this sets precedence as one of the theoretical foundations of
fuzzy approximation theory [
29
]. Soft computing is a branch of computational intelligence where
fuzzy logic, genetic algorithms, probability theory and neural networks are collaboratively used to
mimic human reasoning for appropriate decision making [
30
]. Therefore, fuzzy logic is beneficial in
designing nonlinear complex control solutions with multiple parameters because:
•
It models uncertainty of linear and nonlinear systems of arbitrary complexity to solve real-world
complex dynamic computational problems;
•
It covers a range of operating conditions, and is readily customizable in natural language
processing terms;
•
It exhibits the ability to handle dynamic complex problems with imprecise and incomplete datasets;

Symmetry 2020,12, 1391 6 of 29
•It exhibits a great sense of flexibility and simplicity in modeling real life complex problems.
Therefore, the research study seeks to utilize the concept of fuzzy logic technique to assist in an
early detection of fire-related hazards with a view of minimizing possible errors by sending an alarm
notification message comprised of true positives during an event detection [
15
,
21
]. Fuzzy approximation
utilizes the IF
. . .
THEN implication reference rules with appropriate linguistic description rules.
For instance, IF (the “antecedent” or “premises” is satisfied) THEN (the “consequent” is inferred) as
cited in Liu et al. [
29
]. Hence, the designed concrete set of rules can be inferred with respect to the
FIS knowledge base to generate a generic fuzzy based algorithm. Then, the resultant control model
represents the logic function of the fuzzy approximation algorithm aiding early fire detection and
safety control for the local urban market community.
The proposed prediction model is based on fuzzy approximation reasoning to estimate the
Fire Intensity (FI) as a percentage of a given fire event status. Simulations were carried out in the
MATLAB 2018 an environment, specifically using the Fuzzy Toolbox to design the fuzzy logic controller.
Simulations were carried out to understand the dynamic performance behavioral nature of the proposed
fuzzy control model. The process of design of the fuzzy approximation model is clearly discussed in
the next section of this paper.
3.4. Fuzzy Approximation Control Model
In the design of a fuzzy based approximation control model, we employ the following principle
stages, namely, model fuzzification,model fuzzy rules,model inference engine, and model defuzzification
and evaluation.
3.4.1. The Fuzzy Control Model
The fuzzy control model is designed to approximate the fire intensity and subsequently determine
the appropriate fire probability by making an informed decision about a fire occurrence based on a set
of predefined parameters: smoke intensity, gases, flame, temperature and humidity. Hence, the fuzzy
logic system is therefore built based on following principle steps as detailed in the Figure 2, below:
Symmetry 2020, 12, x FOR PEER REVIEW 6 of 29
Therefore, the research study seeks to utilize the concept of fuzzy logic technique to assist in an
early detection of fire-related hazards with a view of minimizing possible errors by sending an alarm
notification message comprised of true positives during an event detection [15,21]. Fuzzy
approximation utilizes the IF…THEN implication reference rules with appropriate linguistic
description rules. For instance, IF (the “antecedent” or “premises” is satisfied) THEN (the
“consequent” is inferred) as cited in Liu et al. [29]. Hence, the designed concrete set of rules can be
inferred with respect to the FIS knowledge base to generate a generic fuzzy based algorithm. Then,
the resultant control model represents the logic function of the fuzzy approximation algorithm aiding
early fire detection and safety control for the local urban market community.
The proposed prediction model is based on fuzzy approximation reasoning to estimate the Fire
Intensity (FI) as a percentage of a given fire event status. Simulations were carried out in the MATLAB
2018 an environment, specifically using the Fuzzy Toolbox to design the fuzzy logic controller.
Simulations were carried out to understand the dynamic performance behavioral nature of the
proposed fuzzy control model. The process of design of the fuzzy approximation model is clearly
discussed in the next section of this paper.
3.4. Fuzzy Approximation Control Model
In the design of a fuzzy based approximation control model, we employ the following principle
stages, namely, model fuzzification, model fuzzy rules, model inference engine, and model defuzzification and
evaluation.
3.4.1. The Fuzzy Control Model
The fuzzy control model is designed to approximate the fire intensity and subsequently
determine the appropriate fire probability by making an informed decision about a fire occurrence
based on a set of predefined parameters: smoke intensity, gases, flame, temperature and humidity.
Hence, the fuzzy logic system is therefore built based on following principle steps as detailed in the
Figure 2, below:
Figure 2. Principle steps in the design of the fuzzy approximation control model.
3.4.2. Model Fuzzification Values
In the design of the fuzzy control model for early fire detection, we defined six multisensory
input crisp parameters and their corresponding fuzzy membership values, namely, i) the rate of
change in temperature (ΔT) = {very low, low, medium, high, very high}; ii) the rate of change in
humidity (ΔH) = dry, optimal, moist}; iii) the rate of change of smoke intensity or carbon monoxide
(ΔCO) = {low, medium, high}; iv) the rate of change of carbon dioxide (ΔCO2) = {low, medium, high};
v) the rate of change of oxygen (ΔO2) = {low, medium, high}; vi) the flame presence = {false, true}.
Figure 2. Principle steps in the design of the fuzzy approximation control model.
3.4.2. Model Fuzzification Values
In the design of the fuzzy control model for early fire detection, we defined six multisensory
input crisp parameters and their corresponding fuzzy membership values, namely, (i) the rate
of change in temperature (
∆
T) ={very low, low, medium, high, very high}; (ii) the rate of
change in humidity (
∆
H) =dry, optimal, moist}; (iii) the rate of change of smoke intensity

Symmetry 2020,12, 1391 7 of 29
or carbon monoxide
(∆CO) ={low, medium, high};
(iv) the rate of change of carbon dioxide
(∆CO2)={low, medium, high};
(v) the rate of change of oxygen (
∆
O
2
)={low, medium, high}; (vi) the
flame presence ={false, true}. This study, considers a membership value for flame presence equivalent
to “true”. This is so because, we tend to minimize the potential likelihood of false alarm rate that may
be generated. Note that in the proposed experiment, three types of combustion gases are considered
for efficient fire detection, namely, carbon dioxide (CO
2
), carbon monoxide (CO) and oxygen (O
2
).
Mainly because, most burning materials contains carbon materials as one of the primary elements.
The carbon element combines with the atmospheric oxygen to support extensive fire combustion.
This gives rise to carbon monoxide (CO) and carbon dioxide (CO
2
). Thus, a continued fire combustion
leads to a drop in O
2
levels. This subsequently results to a drop in the Fire Intensity for a particular
burning flame.
The fire intensity (FI) is realized as the output function. Hence, the estimated fire intensity
prediction (EFIP) as the fuzzy output is defined by the following membership values: {very low, low,
moderate, high, very high}.
3.4.3. Used Membership Functions
The membership function (MF) is referred to as a curve defined within the MATLAB environment
and is used to map the input space to the membership value to the output function.
In this research approach, we mainly apply three types of Mamdani membership functions in the
design of the proposed fuzzy based approximation model.
These include the triangular MF or (trimf), the Gaussian MF or (gauss) and, the trapezoidal
(trapmf) MF. The applied MF in the model design are extensively discussed in [26,27].
3.5. Applied Model Fuzzy Rules
3.5.1. Fuzzy Associative Memory (FAM) Method
Fuzzy associative memory (FAM) is a content addressable memory in which the recall occurs
correctly if the input data falls within a specified window consisting of the upper and lower bound limit
of a given fuzzy domain. Also, FAMs are associative transformations that map input fuzzy values to
corresponding output fuzzy sets in order to generate an appropriate FAM matrix tool consisting of a set
of evaluation inference rules. The FAM method helps achieve storing and completing the recall realized
by the fuzzy logic associative memory pattern [
31
]. This research approach, utilizes a combination of
both the square and cube FAM methods to generate the required inference rules as detailed in Tables 1
and 2. The FAM method is widely accepted in modeling and optimization of fuzzy control systems.
This improves the performance of the captured contents and their associations efficiently [
32
–
34
].
A total of forty-two inference rules, i.e., {42} ={27 +15}: {27 =3
×
3
×
3
×
1; 15 =3
×
5
×
1},
were generated for the fuzzy based model. Hence, the fuzzy evaluation inference rules as detailed in
Tables 1and 2, for the fuzzy model shall subsequently aid in early fire detection for urban markets
with a major purpose of public safety and control.

Symmetry 2020,12, 1391 8 of 29
Table 1.
Fuzzy inference system (FIS) evaluation rules for the initial environmental parameters i.e.,
Temperature, Humidity vs Estimated Fire Intensity Prediction (EFIP) (%).
Rule No. Temperature (◦C) (∆T) Humidity (%) (∆H) Estimated Fire Intensity
Prediction (EFIP) (%)
1. Very Low Dry L
2. Very Low Optimal L
3. Very Low Moist L
4. Low Dry L
5. Low Optimal L
6. Low Moist L
7. Medium Dry M
8. Medium Optimal M
9. Medium Moist L
10. High Dry H
11. High Optimal H
12. High Moist L
13. Very High Dry H
14. Very High Optimal H
15. Very High Moist L
KEY: L: low, M: moderate, H: high. For all evaluation rules, flame presence ={true} or (0.5).
Table 2. FIS evaluation rules for gas combustion i.e., ∆CO, ∆CO2,∆O2vs EFIP (%).
Rule No. Smoke Intensity
(∆CO) (ppmv)
Carbon Dioxide
(∆CO2) (ppmv)
Oxygen Level
(∆O2) (ppmv)
Estimated Fire Intensity
Prediction (EFIP) (%)
1. Low Low Low L
2. Low Medium Low M
3. Low High Low H
4. Medium Low Low H
5. Medium Medium Low VH
6. Medium High Low VH
7. High Low Low VH
8. High Medium Low VH
9. High High Low VH
10. Low Low Medium VL
11. Low Medium Medium M
12. Low High Medium M
13. Medium Low Medium M
14. Medium Medium Medium H
15. Medium High Medium L
16. High Low Medium M
17. High Medium Medium H
18. High High Medium L
19. Low Low High VL
20. Low Medium High VL
21. Low High High H
22. Medium Low High VL
23. Medium Medium High L
24. Medium High High L
25. High Low High L
26. High Medium High L
27. High High High L
KEY: VL: very low, L: low, M: moderate, H: high, VH: very high. For all rules, flame presence ={true} or value =0.5.

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3.5.2. Fuzzy Inference Evaluation Rules for the Control Experiment Design
In Table 1, of the research experiment, we simulate and model the initial environmental changes
in the surrounding environment effect when a fire outbreak occurs. The following shall be realized
i.e., the rate of change in temperature (
∆
T) and the rate of change in humidity (
∆
H) and the flame
presence ={True} is considered constant for all evaluation inference rules. The expected fuzzy output is
called the estimated fire intensity prediction (EFIP), determined as a percentage (%) of the relationship
between temperature and humidity variation. The fire intensity is the rate of energy released by the
fire or the energy released per unit area of actively burning fire (KW/m2) [35,36].
In Table 2, we simulate and model the dynamic behavior of byproducts of the gases dissipated
during a fire outbreak and clearly understand their effect on the surrounding environmental. The input
parameters to be considered include; the of the rate of change in smoke intensity or carbon monoxide
(
∆
CO), rate of change of carbon dioxide (
∆
CO
2
) and rate of oxygen (
∆
O
2
) that is depleted in the
surrounding atmosphere due to the oxidation with the carbon burning elements. All the gases
dissipated are measured in standard units of parts per million per volume (ppmv). Gas combustion:
the high temperature exothermic redox chemical reaction between fuel (reactant) and an oxidant
(usually oxygen) that produces oxidized gaseous products in a mixture termed as smoke [
37
,
38
].
Note that, combustion doesn’t always result in a fire, but when it does, the flame is the characteristic
indicator of the reaction.
3.6. Model Fuzzy Inference System
The fuzzy inference system (FIS) is the key unit in the fuzzy logic system, having decision making
as the primary goal. It comprises of the IF
. . .
THEN rules along with specified connectors i.e., “OR” and
“AND”. Mathematically, the AND, OR operators are defined using the equations represented by
Equations (1) and (2) respectively;
µ(AηB) =min [µA(x), µB(x)] (1)
µ(AυB) =max [µA(x), µB(x)] (2)
where µA; denotes the membership function in Class A, µB; the membership function in Class B.
In this paper, we utilized the “AND” logic operator in order to determine the minimum probability
of a fire outbreak occurrence [
39
]. There are two types of FIS, namely, the Mamdani type and the TSK
(Takagi, Sugeno, Tangi). In the Mamdani FIS, the resulting consequent memberships are quite fuzzy
in nature. It is widely accepted because it provides reasonably good results with simple structure.
The TSK FIS is not fuzzy (either linear or constant), the consequent membership function has many
parameters per rule translating into more degrees of freedom. This provides more flexibility in design of
membership functions although it lacks interpretability compared to the Mamdani FIS [
40
,
41
]. So, in the
design of the proposed fuzzy prediction model, the FIS module shall be applied in determining the
appropriate decision making of a prevailing fire status. Using the Mamdani method, we were able to
generated corresponding linguistic fuzzy control rules as applied to the proposed model.
From Equations (4) and (5), we compute the strength of the fuzzy evaluation inference rule by
joining the fuzzified inputs of say, temperature, humidity, smoke and gas. Using the clipping method,
the membership functions then evaluates the weighted strength of the rule. Hence, the outcome
represents the fuzzy distribution rule in the application domain [
42
,
43
]. The Mamdani’s Max-Min fuzzy
inference system (FIS) engine is utilized in evaluating the output of the proposed model, because of its
ability to provide accurate and precise approximation results as 6mentioned in [
26
,
44
,
45
]. Therefore,
the Mamdani FIS method, utilizes the rule sets defined in Tables 1and 2, as input membership values
with a corresponding weigh factor to determine the approximate fuzzy output.

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3.7. Model Defuzzification and Evaluation
Defuzzification is the inverse transformation process that maps the fuzzy outputs from the fuzzy
domain back into the crisp output domain [
46
]. In this approach, we utilize the centroid defuzzification
technique or the center of gravity (CoG). The center of gravity is a widely accepted technique because
the output defuzzification values tend to move smoothly in the fuzzy region, giving a more accurate and
precise representation of the fuzzy set region of any shape. Mathematically, the CoG is fundamentally
defined as follows;
Crisp Output : µ(υ)={ΣµA(υ)·υ
SHµA(υ)}(3)
This evaluates to a single crisp value µA(υ), and υis the center of the membership function.
3.8. Architectural Design of Fire Detection Model
The Figure 3, shows a typical block diagram of a fuzzy based architectural model with its
corresponding interacted components. The sensor components include DHT22 for recording the
temperature and humidity changes, UR/IR which detects the presence of flame, and MQ5 which
detects the presence of a gas. They are used to detect the surrounding environmental changes due to a
fire outbreak.
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3.8. Architectural Design of Fire Detection Model
The Figure 3, shows a typical block diagram of a fuzzy based architectural model with its
corresponding interacted components. The sensor components include DHT22 for recording the
temperature and humidity changes, UR/IR which detects the presence of flame, and MQ5 which
detects the presence of a gas. They are used to detect the surrounding environmental changes due to
a fire outbreak.
Sensor information is collected and transmitted to the intermediate component called the micro
controller unit (MCU) for intermediate processing of the collected data.
Figure 3. Block diagram of the proposed fuzzy-based control system.
The experimental collected data is further transmitted and stored in the Cloud API. The stored
data can then be segmented into appropriate fuzzy membership values as illustrated in the Figure 3,
above. Using appropriate fuzzy-based detection algorithms, the stored data is acted upon and
analyzed to determine an output by making an informed decision of the prevailing fire status, i.e., as
a percentage of the estimated fire intensity prediction (EFIP). The fuzzy algorithm may be
implemented using the Arduino integrated development platform (IDE). The Arduino IDE is an
open-source cross-platform application for hardware and software solutions in embedded
computing systems code modules [47]. In order to simulate the model, we utilize MATLAB version
2018 a, in the simulation and modeling of the dynamic behavior of the proposed fuzzy based
approximation model. MATLAB is a high-performance language tool for modeling technical
computing solutions with an integrated environment to support computational, visualization and
programming in a user-friendly manner. Using the MATLAB Fuzzy Logic toolbox, the proposed
fuzzy control model behavioral dynamics are then designed, simulated, modeled and analyzed, with
different performance parameters as discussed herein.
Simulation Input and Output Fuzzy Parameters Considered
In the Tables 3 and 4, we show the various simulation input/output parameters considered in
our simulation experiment, together with their corresponding fuzzy domain ranges defined in a
MATLAB Fuzzy Logic toolbox environment.
Figure 3. Block diagram of the proposed fuzzy-based control system.
Sensor information is collected and transmitted to the intermediate component called the micro
controller unit (MCU) for intermediate processing of the collected data.
The experimental collected data is further transmitted and stored in the Cloud API. The stored
data can then be segmented into appropriate fuzzy membership values as illustrated in the Figure 3,
above. Using appropriate fuzzy-based detection algorithms, the stored data is acted upon and analyzed
to determine an output by making an informed decision of the prevailing fire status, i.e., as a percentage
of the estimated fire intensity prediction (EFIP). The fuzzy algorithm may be implemented using the
Arduino integrated development platform (IDE). The Arduino IDE is an open-source cross-platform
application for hardware and software solutions in embedded computing systems code modules [
47
].
In order to simulate the model, we utilize MATLAB version 2018 a, in the simulation and modeling of the
dynamic behavior of the proposed fuzzy based approximation model. MATLAB is a high-performance
language tool for modeling technical computing solutions with an integrated environment to support
computational, visualization and programming in a user-friendly manner. Using the MATLAB Fuzzy

Symmetry 2020,12, 1391 11 of 29
Logic toolbox, the proposed fuzzy control model behavioral dynamics are then designed, simulated,
modeled and analyzed, with different performance parameters as discussed herein.
Simulation Input and Output Fuzzy Parameters Considered
In the Tables 3and 4, we show the various simulation input/output parameters considered in our
simulation experiment, together with their corresponding fuzzy domain ranges defined in a MATLAB
Fuzzy Logic toolbox environment.
Table 3.
The crisp and fuzzy-based input parameters, domain ranges, and universe of discourse
membership function.
Crisp Input Variable. Fuzzy Input Parameters. Fuzzy Domain Range. Universe of Discourse for MFs
Temperature(∆T) Very Low, Low, Medium,
High, Very High. [0–100] 0–20, 20–40, 40–60, 60–80 and
80–100 respectively.
Humidity (∆H) Dry, Optimal, Moist. [0–100]- (%) 0–40, 40–80, 80–100
Smoke (CO) Low, Medium, High. [0–100] 0–40, 40–80, 80–100
Carbon dioxide (CO2) Low, Medium, High. [0–100] 0–40, 40–80, 80–100
Oxygen Level (O2) Low, Medium, High. [0–100] 0–40, 40–80, 80–100
Flame Presence Boolean: False, True. [False, True] 0, 1
Table 4.
The Crisp, Fuzzy Based Output Parameters, Domain, Universe of DiscourseMembership Function.
Crisp Output Variable. Fuzzy Output Parameters Fuzzy Domain Range. Universe of Discourse for MFs
Estimated Fire Intensity
Prediction(EFIP)%
Very Low, Low Moderate,
High, Very High. [0–100]- (%) 0–20, 20–40, 40–60, 60–80 and
80–100 respectively.
4. Simulation Experimental Setup
4.1. The Fuzzy Control System (FCS) Design
In this section, we illustrate the design of the fuzzy-based fire detection model designed using
the fuzzy toolbox and the Mamdani FIS, integrated within MATLAB environment, as observed in
Figures 4and 5. Figure 4represents the initial environment changes in temperature and humidity due
to a fire flame vis-
à
-vis the estimated fire intensity prediction (EFIP), whereas Figure 5represents the
various gases involved during combustion and their effect of the EFIP observed.
Symmetry 2020, 12, x FOR PEER REVIEW 11 of 29
Table 3. The crisp and fuzzy-based input parameters, domain ranges, and universe of discourse
membership function.
Crisp Input
Variable. Fuzzy Input Parameters.
Fuzzy
Domain
Range.
Universe of Discourse for
MFs
Temperature(ΔT) Very Low, Low, Medium,
High, Very High. [0–100] 0–20, 20–40, 40–60, 60–80 and
80–100 respectively.
Humidity (ΔH) Dry, Optimal, Moist. [0–100]- (%) 0–40, 40–80, 80–100
Smoke (CO) Low, Medium, High. [0–100] 0–40, 40–80, 80–100
Carbon dioxide
(CO2) Low, Medium, High. [0–100] 0–40, 40–80, 80–100
Oxygen Level (O2) Low, Medium, High. [0–100] 0–40, 40–80, 80–100
Flame Presence Boolean: False, True. [False, True] 0, 1
Table 4. The Crisp, Fuzzy Based Output Parameters, Domain, Universe of Discourse Membership
Function.
Crisp Output Variable. Fuzzy Output
Parameters
Fuzzy
Domain
Range.
Universe of Discourse
for MFs
Estimated Fire Intensity
Prediction(EFIP)%
Very Low, Low
Moderate, High, Very
High.
[0–100]- (%) 0–20, 20–40, 40–60, 60–80
and 80–100 respectively.
4. Simulation Experimental Setup
4.1 The Fuzzy Control System (FCS) Design
In this section, we illustrate the design of the fuzzy-based fire detection model designed using
the fuzzy toolbox and the Mamdani FIS, integrated within MATLAB environment, as observed in
Figures 4 and 5. Figure 4 represents the initial environment changes in temperature and humidity
due to a fire flame vis-à-vis the estimated fire intensity prediction (EFIP), whereas Figure 5 represents
the various gases involved during combustion and their effect of the EFIP observed.
Figure 4. Design of the fuzzy control system (FCS) model for temperature, humidity and flame vs
EFIP (%).
Figure 4.
Design of the fuzzy control system (FCS) model for temperature, humidity and flame vs
EFIP (%).

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Figure 5. Design of the FCS model for gas combustion, i.e., CO, CO2, O2 and flame, vs EFIP (%).
4.2. Input/Output Fuzzy Membership Functions Designs
With reference to the Figure 6a–e, we illustrate the sample input/output designs for the proposed
fuzzy inference systems (FIS) variables and their corresponding membership function plots. For
instance, Figure 6a or Figure 6e illustrates the FIS input variables for carbon monoxide (CO), {low,
medium, high}, and humidity, {dry, optimal, moist}, as their respective membership function plots.
Likewise, the estimated fire intensity prediction (EFIP) is an FIS output variable that is represented
by: {very low, low, moderate, high, very high}.
(a) CO input variable
(b) EFIP (%) output variable
Figure 5. Design of the FCS model for gas combustion, i.e., CO, CO2, O2and flame, vs EFIP (%).
4.2. Input/Output Fuzzy Membership Functions Designs
With reference to the Figure 6a–e, we illustrate the sample input/output designs for the
proposed fuzzy inference systems (FIS) variables and their corresponding membership function
plots. For instance, Figure 6a or Figure 6e illustrates the FIS input variables for carbon monoxide (CO),
{low, medium, high}, and humidity, {dry, optimal, moist}, as their respective membership function plots.
Likewise, the estimated fire intensity prediction (EFIP) is an FIS output variable that is represented by:
{very low, low, moderate, high, very high}.
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Figure 5. Design of the FCS model for gas combustion, i.e., CO, CO2, O2 and flame, vs EFIP (%).
4.2. Input/Output Fuzzy Membership Functions Designs
With reference to the Figure 6a–e, we illustrate the sample input/output designs for the proposed
fuzzy inference systems (FIS) variables and their corresponding membership function plots. For
instance, Figure 6a or Figure 6e illustrates the FIS input variables for carbon monoxide (CO), {low,
medium, high}, and humidity, {dry, optimal, moist}, as their respective membership function plots.
Likewise, the estimated fire intensity prediction (EFIP) is an FIS output variable that is represented
by: {very low, low, moderate, high, very high}.
(a) CO input variable
(b) EFIP (%) output variable
Figure 6. Cont.

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(c) Flame presence
(d) Temperature
(e) Humidity
Figure 6. Membership function design plots for the different FIS input/output variables.
4.3. MATLAB Evaluation Rules Editor Proposed Fuzzy Based Model
In this section, we show the proposed fuzzy inference rules, as detailed in Figure 7a,b, that were
used in the output evaluation of the proposed fuzzy approximation model. In order to design the
inference rules, we applied the “AND “connector. The “AND” connection is significant in
determining the minimum probability of the estimated fire intensity prediction (EFIP) status, given
a set of fuzzy input parameters discussed above.
In this research approach, we assumed that all the fuzzy inference rules considered in the model
evaluation have an equal weighted priority function (W = I). This implies that the proposed
evaluation rules are having equal priority during model evaluation. It is also important to note that
we considered the flame presence to be “true” for all evaluation rules. The significant of this is to
minimize the potential possibility of false alarm rate.
Figure 6. Membership function design plots for the different FIS input/output variables.
4.3. MATLAB Evaluation Rules Editor Proposed Fuzzy Based Model
In this section, we show the proposed fuzzy inference rules, as detailed in Figure 7a,b, that were
used in the output evaluation of the proposed fuzzy approximation model. In order to design the
inference rules, we applied the “AND “connector. The “AND” connection is significant in determining
the minimum probability of the estimated fire intensity prediction (EFIP) status, given a set of fuzzy
input parameters discussed above.

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(a) Initial environmental parameters temperature, humidity and
f
lame presence vs EFIP.
(b) Gas combustion, i.e., CO, CO2, O2, and flame presence vs EFIP.
Figure 7. MATLAB evaluation inference rules editor view design for various parameters.
4.4. MATLAB Evaluation Rules Viewer
In Figure 8a, we illustrate the rule view evaluation insights of the resultant effect on the EFIP
due to gas combustion and then temperature and humidity. For instance, for Figure 8a, we
demonstrate a resultant fire gas combustion and then its corresponding effect on the fire intensity,
i.e., if CO = 57.7 ppmv, CO2 = 69.8 ppmv, O2 = 62.1 ppmv, flame presence = 0.5, then estimated fire
intensity prediction (EFIP) = 60.9% for the above conditions. Likewise, in Figure 8b, we show that the
resultant rule viewer for a given experimentation yielded the following results: temperature = 70.6 °C,
humidity = 26.6%, flame = 0.5 vs EFIP = 67.5%, as below:
Figure 7. MATLAB evaluation inference rules editor view design for various parameters.
In this research approach, we assumed that all the fuzzy inference rules considered in the model
evaluation have an equal weighted priority function (W =I). This implies that the proposed evaluation
rules are having equal priority during model evaluation. It is also important to note that we considered
the flame presence to be “true” for all evaluation rules. The significant of this is to minimize the
potential possibility of false alarm rate.

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4.4. MATLAB Evaluation Rules Viewer
In Figure 8a, we illustrate the rule view evaluation insights of the resultant effect on the
EFIP due to gas combustion and then temperature and humidity. For instance, for Figure 8a,
we demonstrate a resultant fire gas combustion and then its corresponding effect on the fire intensity,
i.e.,
if CO =57.7 ppmv,
CO
2
=69.8 ppmv, O
2
=62.1 ppmv, flame presence =0.5, then estimated fire
intensity prediction (EFIP) =60.9% for the above conditions. Likewise, in Figure 8b, we show that the
resultant rule viewer for a given experimentation yielded the following results: temperature =70.6
◦
C,
humidity =26.6%, flame =0.5 vs EFIP =67.5%, as below:
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(a) CO, CO2, O2, flame vs EFIP.
(b) Temperature, humidity, flame vs EFIP
Figure 8. Determination of the probability of (EFIP) using MATLAB rule view.
4.5. Proposed IoT-based Fuzzy Approximation Prediction Model
The proposed IoT fuzzy-based approximation prediction model consists of five major
components, namely, i) fuzzy input values, ii) fuzzy inference engine (FIS), iii) fuzzy output values,
iv) decision evaluation criteria and v) safety operations analyzer.
i) Fuzzy Input Values:
This component comprises crisp input parameters. It consists of sensors that take the different data
readings. Each crisp input parameter is then represented in several fuzzy input parameters as shown
in the diagram in Figure 9. Environmental sensor readings (data) are taken from the sensors, namely,
rate of change in temperature (ΔT) = {very low, low, medium, high, very high}, humidity (ΔH) = {dry,
optimal, moist}, carbon monoxide (ΔCO) ={low, medium, high}, carbon dioxide (ΔCO2) = {low,
medium, high}, oxygen (ΔO2) = {low, medium, high} and flame presence = {false, true}. The data
Figure 8. Determination of the probability of (EFIP) using MATLAB rule view.

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4.5. Proposed IoT-Based Fuzzy Approximation Prediction Model
The proposed IoT fuzzy-based approximation prediction model consists of five major components,
namely, (i) fuzzy input values, (ii) fuzzy inference engine (FIS), (iii) fuzzy output values, (iv) decision
evaluation criteria and (v) safety operations analyzer.
(i) Fuzzy Input Values:
This component comprises crisp input parameters. It consists of sensors that take the different
data readings. Each crisp input parameter is then represented in several fuzzy input parameters
as shown in the diagram in Figure 9. Environmental sensor readings (data) are taken from the
sensors, namely, rate of change in temperature (
∆
T) ={very low, low, medium, high, very high},
humidity
(∆H) ={dry, optimal, moist},
carbon monoxide (
∆
CO) ={low, medium, high}, carbon dioxide
(∆CO2)={low, medium, high}
, oxygen (
∆
O
2
)={low, medium, high} and flame presence ={false, true}.
The data readings from the different sensors are fuzzified into the different input fuzzy values as
defined in Figure 9.
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readings from the different sensors are fuzzified into the different input fuzzy values as defined in
Figure 9.
ii) The Fuzzy Inference Engine (FIS):
After the process of fuzzification. The fuzzy values are processed with the help of a micro
controller unit (MCU). This component consists of a rules inference engine. That comprises all
inference evaluation rules to be applied to the proposed fuzzy based model. The database engine acts
as a fuzzy associative matrix (FAM) store for all inference rules to be utilized by the proposed model
thereby forming a knowledge base. Using the “AND” operator and the IF…THEN structure, we then
define the minimum probability of a fire outbreak using the defined fuzzy domain. The weighted
function (W = 1), means all evaluation inference rules are equal and therefore, have equal priority in
the evaluation of the fuzzy inference system.
iii) The Fuzzy Output Values:
The fuzzy output value criteria denote the implication of the proposed fuzzy model design.
Through the message queue telemetry transport (MQTT) protocol, data is sent to the cloud application
programming interface (API) environment. The sensor data are then acted upon by an intelligent
fuzzy based algorithm to obtain an output referred to as the estimated fire intensity prediction (EFIP).
EFIP represents the probability of a fire occurrence determined by the input fuzzy parameters shown
in Figure 9, above. Consequently, an appropriate decision is made and evaluated against the fire
status.
iv) The Decision Evaluation Criteria:
The fire intensity (EFIP) then determines the appropriate decision-making based on the
percentage (%) of fire status using predefined classification criterion ranges, namely, very low (VL):
[0–20], low (L): [20–40], moderate (M): [40–60], high (H): [60–80], and very high (VH):[80–100]. The
proposed fuzzy based algorithm shall be able to act on the data stored in a cloud API such as
Thingspeak or Firebase to make an appropriate decision based on the laid-out criteria defined in the
model above.
v) Safety and Operations Analyzer:
This component of the model is activated in case of any predetermined early warning detected
a signal shall be initiated or a message sent to the authorities for an appropriate and immediate action.
The safety and operation analyzer is therefore, responsible for signaling an alarm, initiating a water
sprinkler, or sending a warning message for possible evacuation of persons.
Figure 9. Proposed IoT-based fuzzy approximation prediction model.
Figure 9. Proposed IoT-based fuzzy approximation prediction model.
(ii) The Fuzzy Inference Engine (FIS):
After the process of fuzzification. The fuzzy values are processed with the help of a micro controller
unit (MCU). This component consists of a rules inference engine. That comprises all inference evaluation
rules to be applied to the proposed fuzzy based model. The database engine acts as a fuzzy associative
matrix (FAM) store for all inference rules to be utilized by the proposed model thereby forming a
knowledge base. Using the “AND” operator and the IF
. . .
THEN structure, we then define the minimum
probability of a fire outbreak using the defined fuzzy domain. The weighted function (
W=1
), means all
evaluation inference rules are equal and therefore, have equal priority in the evaluation of the fuzzy
inference system.
(iii) The Fuzzy Output Values:
The fuzzy output value criteria denote the implication of the proposed fuzzy model design.
Through the message queue telemetry transport (MQTT) protocol, data is sent to the cloud application
programming interface (API) environment. The sensor data are then acted upon by an intelligent
fuzzy based algorithm to obtain an output referred to as the estimated fire intensity prediction (EFIP).

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EFIP represents the probability of a fire occurrence determined by the input fuzzy parameters shown in
Figure 9, above. Consequently, an appropriate decision is made and evaluated against the fire status.
(iv) The Decision Evaluation Criteria:
The fire intensity (EFIP) then determines the appropriate decision-making based on the percentage
(%) of fire status using predefined classification criterion ranges, namely, very low (VL): [0–20],
low (L): [20–40],
moderate (M): [40–60], high (H): [60–80], and very high (VH):[80–100]. The proposed
fuzzy based algorithm shall be able to act on the data stored in a cloud API such as Thingspeak or
Firebase to make an appropriate decision based on the laid-out criteria defined in the model above.
(v) Safety and Operations Analyzer:
This component of the model is activated in case of any predetermined early warning detected a
signal shall be initiated or a message sent to the authorities for an appropriate and immediate action.
The safety and operation analyzer is therefore, responsible for signaling an alarm, initiating a water
sprinkler, or sending a warning message for possible evacuation of persons.
5. Results and Discussions
After successful simulation and modeling of the proposed fuzzy control approximation model,
the following insights of results were obtained as discussed herein. Results graphically the comparative
performance due to a fire outbreak on the surrounding environmental conditions vis-
à
-vis the estimated
fire intensity prediction (EFIP) in order to obtain an informed decision of a prevailing fire status.
5.1. Initial Environmental Parameters, i.e., Temperature, Humidity and Flame vs EFIP
In Figure 10a–e, we show the performance comparison due to a fire outbreak as per the proposed
environmental parameters, namely, rate of change of temperature and rate of change of humidity
vis-
à
-vis the output parameter, estimated fire intensity prediction (EFIP). The EFIP is determined
as a percentage probability of true fire incidences with respect to the input variables. Figure 10b,d
represent a 2D view of Figure 10a. We observe that, during the initial stages of a fire outbreak, there are
lower temperatures experienced amidst high humidity conditions, translating into lower fire intensity
(EFIP). Then, the temperatures significantly increase with increased EFIP. We also observe that lower
temperatures are due to a high moisture content contained in the atmosphere; this subsequently
translates into a lower fire intensity or (EFIP). Furthermore, we noted that a steady increase in
temperature decreases the humidity conditions till the dryness conditions. This significantly lowers
the estimated fire intensity prediction (EFIP), as illustrated in the Figure 10d.

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(a) Temperature, Humidity Vs EFIP (%)
(b) Temperature vs EFIP (%)
(c) Humidity vs temperature
(d) Humidity vs EFIP (%)
Figure 10. Cont.

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(e) Temperature, flame presence vs EFIP (%)
Figure 10. Performance comparison of 3D, 2D surface plot view of various parameter pairs.
5.2. Gas Combustion i.e., CO, CO2, O2 and Flame Vs EFIP
In the Figure 10a–f below, we discuss several comparative insights due to gas combustion, i.e.,
ΔCO, ΔCO2 and ΔO2, in relationship with fire intensity (EFIP) due to a fire outbreak. Several scenarios
are considered for the model, the performance behavior of the gases dissipated in the proposed fuzzy
control system; results are discussed herein.
In the Figure 11a, we observe an initially lower gas concentration levels for CO and CO2, which
further rises with subsequent increase in the fire intensity (EFIP). In the Figure 11b, at CO2 = 17 ppmv,
EFIP = 50%, we observe a gradual increase in CO2 levels due to increased fire intensity (EFIP), until
75%, when the fire intensity then becomes constant till when CO2 = 67 ppmv. However, beyond 67
ppmv, we suddenly observe a drop in the fire intensity (EFIP) noticed due to decreased levels of O2
necessary for supporting fire combustion. Hence, this subsequently translates into a decrease in the
fire intensity (EFIP).
In Figure 11c, we observe that an increase in smoke intensity or carbon monoxide (CO)
dissipated lowers the volume of O2 levels in the surrounding and hence, subsequent increases in fire
intensity or (EFIP). In the Figure 11f, we observe that an increased consumption of O2, lowers the fire
intensity, meaning that more O2 is being consumed. This initially increases the fire intensity.
However, O2 levels gradually reduce with time. This eventually translates into lower fire intensity
(EFIP).
In addition, note that, as more O2 levels are reduced by the burning flame, this subsequently
increases the level of CO2 dissipated into the atmosphere. Hence, there is a drop in the fire intensity
(EFIP) because CO2 does not support the combustion as noted in Figure 11b. Also, decreased levels
of oxygen translate into an increased support for fire combustion. This further leads to a high
dissipation of both carbon dioxide (CO2) or CO hence, higher fire intensity prediction (EFIP) is
realized, as observed in the Figure 11a. Note that, in the Figure 11e, we observe that increased flame
intensity subsequently increases the fire intensity as more O2 is consumed by the burning flame. Also,
in the Figure 11f, we observe that, at lower levels of oxygen (O2), there is significantly higher EFIP
and vice versa: higher O2 concentrations yield lower EFIP, as per the designed rule based fuzzy
model.
Figure 10. Performance comparison of 3D, 2D surface plot view of various parameter pairs.
In Figure 10c, we observe that a lower risk level is experienced in the “
blue
” region. Thus, when the
temperatures increase the risk factors are migrated to the moderate level and subsequently to high
risk level (“
yellow
” region), as per the Figure 10c. We also note that the averagely low temperatures
that are experienced with an increase in moisture content during a fire outbreak subsequently lower
the probability of the fire intensity and vice versa. In Figure 10b, it is noted that, during the initial
stages of a fire outbreak event, for, say, EFIP <
20%
and temperature =20
◦
C, a high moisture content
is experienced and this lowers EFIP because high humidity conditions are still experienced at that
point. However, when the temperatures significantly rise beyond 20
◦
C, we observe an increase in the
EFIP, which further increases with a rise in temperature as per Figure 10b. Overall, it is observed that,
high temperatures translate into significantly lower humidity and, therefore, lead to an increased fire
intensity (EFIP). Lower temperatures, on the other hand, result in a high humidity, hence decreasing
the fire intensity (EFIP), as per the rules.
In Figure 10b, at EFIP =
88%
, and Temperature =72
◦
C, we observe that the EFIP remains constant
(in steady state) with an increased temperature change for a given period of time due to oxygen
being depleted. It should be noted that the effect of increased temperatures translates into lower
dry humidity conditions for a given environmental setting. From Figure 10d above, we observe that
a lower humidity condition experienced due to high temperatures ultimately translates into high
fire intensity (EFIP). However, beyond the value of humidity of approx.
68%
, we realize a gradual
decrease in the fire intensity. This is because, increased humidity conditions within the atmosphere
subsequently lowers the temperatures and then fire intensity (EFIP), hence affecting the fire status.
In Figure 10e, it is noted that lower temperature conditions significantly translate into lower flame and
subsequently yielding a lower fire intensity. Likewise, higher temperatures may significantly result
into higher fire intensity (EFIP), as observed in the Figure 10e.
5.2. Gas Combustion i.e., CO, CO2, O2and Flame vs EFIP
In the Figure 10a–e below, we discuss several comparative insights due to gas combustion,
i.e.,
∆
CO,
∆
CO
2
and
∆
O
2
, in relationship with fire intensity (EFIP) due to a fire outbreak. Several
scenarios are considered for the model, the performance behavior of the gases dissipated in the
proposed fuzzy control system; results are discussed herein.
In the Figure 11a, we observe an initially lower gas concentration levels for CO and CO
2
,
which further rises with subsequent increase in the fire intensity (EFIP). In the Figure 11b,
at
CO2=17 ppmv
, EFIP =
50%
, we observe a gradual increase in CO
2
levels due to increased
fire intensity (EFIP), until
75%
, when the fire intensity then becomes constant till when CO
2
=67 ppmv.
However, beyond 67 ppmv, we suddenly observe a drop in the fire intensity (EFIP) noticed due to

Symmetry 2020,12, 1391 20 of 29
decreased levels of O
2
necessary for supporting fire combustion. Hence, this subsequently translates
into a decrease in the fire intensity (EFIP).
Symmetry 2020, 12, x FOR PEER REVIEW 20 of 29
(a) CO2, CO vs EFIP (%)
(b) CO2 vs EFIP (%)
(c) CO, O2 vs EFIP (%)
(d) CO vs EFIP (%)
Figure 11. Cont.

Symmetry 2020,12, 1391 21 of 29
Symmetry 2020, 12, x FOR PEER REVIEW 21 of 29
(e) O2
,
Flame Presence vs EFIP (%)
(f) O2 vs EFIP (%)
Figure 11. Performance comparison of 3D and 2D surface plots view of various parameter pairs.
5.3. Simulation Experiment Data Results
In Table 5, we study and observe the performance comparison of rate of change of temperature
(ΔT) and the rate of change of humidity (ΔH) vis-à-vis the estimated fire intensity prediction (EFIP)
for 12 experimental evaluation rule values. Figure 12 illustrates a resultant graphical illustration of
the data output generated from Table 5 above. It is observed that, at lower temperatures, there exists
high humidity conditions, which subsequently translate into lower estimated fire intensity prediction
(EFIP), as noted by the dotted line in the Figure 12. However, an increase in temperature conditions
tends to significantly lower the humidity conditions towards dryness.
Table 5. Expt. data of temperature and humidity vs EFIP.
Rule No. ΔT(0 C) ΔH (%) EFIP (%)
1. 9.0 17.5 17.6
2. 15.1 27.1 18.3
3. 38.0 58.4 43.6
4. 50.0 50.0 52.1
5. 28.3 38.0 22.9
6. 40.4 44.0 46.2
7. 46.4 48.8 52.1
8. 52.4 65.7 51.9
9. 62.9 71.7 54.5
10. 66.9 80.1 37.9
11. 68.1 52.4 71.2
12. 68.1 22.3 69.7
Figure 11. Performance comparison of 3D and 2D surface plots view of various parameter pairs.
In Figure 11c, we observe that an increase in smoke intensity or carbon monoxide (CO) dissipated
lowers the volume of O
2
levels in the surrounding and hence, subsequent increases in fire intensity or
(EFIP). In the Figure 11f, we observe that an increased consumption of O
2,
lowers the fire intensity,
meaning that more O
2
is being consumed. This initially increases the fire intensity. However, O
2
levels
gradually reduce with time. This eventually translates into lower fire intensity (EFIP).
In addition, note that, as more O
2
levels are reduced by the burning flame, this subsequently
increases the level of CO
2
dissipated into the atmosphere. Hence, there is a drop in the fire intensity
(EFIP) because CO
2
does not support the combustion as noted in Figure 11b. Also, decreased levels of
oxygen translate into an increased support for fire combustion. This further leads to a high dissipation
of both carbon dioxide (CO
2
) or CO hence, higher fire intensity prediction (EFIP) is realized, as observed
in the Figure 11a. Note that, in the Figure 11e, we observe that increased flame intensity subsequently
increases the fire intensity as more O
2
is consumed by the burning flame. Also, in the Figure 11f,
we observe that, at lower levels of oxygen (O
2
), there is significantly higher EFIP and vice versa: higher
O2concentrations yield lower EFIP, as per the designed rule based fuzzy model.
5.3. Simulation Experiment Data Results
In Table 5, we study and observe the performance comparison of rate of change of temperature
(
∆
T) and the rate of change of humidity (
∆
H) vis-
à
-vis the estimated fire intensity prediction (EFIP)
for 12 experimental evaluation rule values. Figure 12 illustrates a resultant graphical illustration of
the data output generated from Table 5above. It is observed that, at lower temperatures, there exists
high humidity conditions, which subsequently translate into lower estimated fire intensity prediction

Symmetry 2020,12, 1391 22 of 29
(EFIP), as noted by the dotted line in the Figure 12. However, an increase in temperature conditions
tends to significantly lower the humidity conditions towards dryness.
Table 5. Expt. data of temperature and humidity vs EFIP.
Rule No. ∆T(◦C) ∆H (%) EFIP (%)
1. 9.0 17.5 17.6
2. 15.1 27.1 18.3
3. 38.0 58.4 43.6
4. 50.0 50.0 52.1
5. 28.3 38.0 22.9
6. 40.4 44.0 46.2
7. 46.4 48.8 52.1
8. 52.4 65.7 51.9
9. 62.9 71.7 54.5
10. 66.9 80.1 37.9
11. 68.1 52.4 71.2
12. 68.1 22.3 69.7
Symmetry 2020, 12, x FOR PEER REVIEW 22 of 29
Figure 12. Performance comparison of temperature and Humidity vs EFIP for 12 sampled data points.
In addition, excessively high temperatures tend to vaporize the humidity conditions, and this
subsequently increases the fire intensity (EFIP). This phenomenon can be observed at sampled points
11–12, of the graphical illustration in Figure 12. Therefore, the latter figure suitably illustrates the
EFIP from very low at point 1, to very high EFIP when temperatures are very high, and the humidity
conditions decreases to dryness, as observed at point 12. Moderate EFIP is observed at sample points
6–7, where the temperatures and humidity conditions are approximately in equilibrium with the
prevailing conditions.
In the Table 6, we study and observe the performance comparison of the gases dissipated i.e.,
rate of change of carbon monoxide (CO), rate of change of carbon dioxide (CO2), due to a fire outbreak
combustion in support of the rate of change in oxygen (ΔO2) levels. In Figure 13, it is observed that
lower levels of CO2 or CO concentration in the atmosphere translates into lower estimated fire
intensity prediction (EFIP). Lower levels of CO2 in the atmosphere also significantly translates into
lower temperatures or high humidity conditions experienced during a fire outbreak. However, at
sample data point 2–5, we observe that there is a relative increase in both CO2 and CO concentration
levels due to increased temperatures. This significantly translated into higher EFIP expected, coupled
with a lower humidity condition.
Table 6. Expt. data of CO, CO2 and O2 vs EFIP.
Rule No. ΔCO ΔCO2 ΔO2 EFIP (%)
1. 23.6 21.4 23.6 47.8
2. 32.4 29.1 28.0 63.2
3. 36.8 30.2 31.1 69.1
4. 44.5 32.4 39.0 71.3
5. 50.0 37.9 44.5 75.0
6. 19.2 48.9 51.1 56.5
7. 13.7 8.2 13.7 25.0
8. 64.3 50.0 17.0 86.5
9. 70.9 24.7 32.4 62.8
10. 32.4 46.7 65.4 70.3
11. 48.9 75.3 86.3 25.0
12. 57.7 69.8 62.1 60.9
Figure 12.
Performance comparison of temperature and Humidity vs EFIP for 12 sampled data points.
In addition, excessively high temperatures tend to vaporize the humidity conditions,
and this subsequently increases the fire intensity (EFIP). This phenomenon can be observed at
sampled points 11–12, of the graphical illustration in Figure 12. Therefore, the latter figure suitably
illustrates the EFIP from very low at point 1, to very high EFIP when temperatures are very high, and the
humidity conditions decreases to dryness, as observed at point 12. Moderate EFIP is observed at
sample points 6–7, where the temperatures and humidity conditions are approximately in equilibrium
with the prevailing conditions.
In the Table 6, we study and observe the performance comparison of the gases dissipated i.e., rate of
change of carbon monoxide (CO), rate of change of carbon dioxide (CO
2
), due to a fire outbreak
combustion in support of the rate of change in oxygen (
∆
O
2
) levels. In Figure 13, it is observed that
lower levels of CO
2
or CO concentration in the atmosphere translates into lower estimated fire intensity
prediction (EFIP). Lower levels of CO
2
in the atmosphere also significantly translates into lower
temperatures or high humidity conditions experienced during a fire outbreak. However, at sample
data point 2–5, we observe that there is a relative increase in both CO
2
and CO concentration levels
due to increased temperatures. This significantly translated into higher EFIP expected, coupled with a
lower humidity condition.

Symmetry 2020,12, 1391 23 of 29
Table 6. Expt. data of CO, CO2and O2vs EFIP.
Rule No. ∆CO ∆CO2∆O2EFIP (%)
1. 23.6 21.4 23.6 47.8
2. 32.4 29.1 28.0 63.2
3. 36.8 30.2 31.1 69.1
4. 44.5 32.4 39.0 71.3
5. 50.0 37.9 44.5 75.0
6. 19.2 48.9 51.1 56.5
7. 13.7 8.2 13.7 25.0
8. 64.3 50.0 17.0 86.5
9. 70.9 24.7 32.4 62.8
10. 32.4 46.7 65.4 70.3
11. 48.9 75.3 86.3 25.0
12. 57.7 69.8 62.1 60.9
Symmetry 2020, 12, x FOR PEER REVIEW 23 of 29
Figure 13. Performance comparison of CO, CO2 and O2 vs EFIP for 12 data sample points.
At sample data point 7 in the dataset, we observe a sudden drop in CO2, CO levels. This is due
to reduced relative temperature and high humidity conditions. Hence, this ultimately translates into
a decrease in the fire intensity or (EFIP). Also, at point 8, we observe an increase in the levels of CO2
resulting into a drop in O2 levels and a higher fire intensity of approx. EFIP = 87 ppmv. It should be
noted that increased levels of CO and CO2 significantly reduce the O2 levels, which are used in
supporting fire combustion. At point 11, we observe a lower EFIP = 22%, higher O2 = 87 ppmv and
subsequently lower CO and CO2 concentrations. This is because of high humidity translates into
lower temperature therefore affection the fire intensity or EFIP.
5.4. Gas Combustion Efficiency (GCE)
This is defined as the measure of how effectively the heat content generated of the burning fuel
is transferred into usable heat as a result of gas combustion [35]. Note that temperature, oxygen and
carbon dioxide are the primary indicators of combustion efficiency. For all presence of flame = {true}
inputs to the fuzzy model and combustion of gases dissipated in the atmosphere are measured in
parts per million per volume (ppmv). Hence, the gas combustion efficiency (GCE) is defined by the
formula:
GCE = Ʃ𝜟𝑪𝑶𝟐
Ʃ(𝜟𝑪𝑶𝜟𝑪𝑶𝟐) × 100% (4)
where GCE represents the gas combustion efficiency of the burning fire flame with respect to ΔCO2,
ΔCO and O2.
From the data experimental results in Table 6, we can determine the GCE of the gases dissipated
as per the simulated case study. Using the formula, defined in Equation (7), the rate of change of the
gases dissipated to the atmosphere i.e., ΔCO, ΔCO2, the GCE can be computed.
From extracted data we observe that, the summation of ΔCO, ΔCO2 can be determined: Ʃ(ΔCO)
= 494.4 ppmv, Ʃ(ΔCO2) = 474.6 ppmv. From the dataset in Table 6, the resultant GCE can be
approximately computed from the equation (7).
The gas combustion efficiency (GCE) = (.
.  .) x 100% = 48.96%, Hence, the efficiency of
the gases dissipated into the atmosphere due to the burning fuel is equivalent to 49%. Meaning that,
approximately ~50% of the gases, i.e., CO, CO2, are dissipated into the atmosphere through the carbon
element burning in the fuel. With reference to the simulation data in Table 6, we observe that an
increase or decrease in the amount of gas dissipated has a subsequent increase or decrease on the fire
intensity. This translates into an increased efficiency of the burning fuel. This can also be clearly
indicated by the dotted line curve in the graph of Figure 12, representing the estimated fire intensity
prediction (EFIP).
5.5. Model Performance Evaluation
Figure 13. Performance comparison of CO, CO2and O2vs EFIP for 12 data sample points.
At sample data point 7 in the dataset, we observe a sudden drop in CO
2
, CO levels. This is due to
reduced relative temperature and high humidity conditions. Hence, this ultimately translates into a
decrease in the fire intensity or (EFIP). Also, at point 8, we observe an increase in the levels of CO
2
resulting into a drop in O
2
levels and a higher fire intensity of approx. EFIP =87 ppmv. It should
be noted that increased levels of CO and CO
2
significantly reduce the O
2
levels, which are used in
supporting fire combustion. At point 11, we observe a lower EFIP =22%, higher O
2
=87 ppmv and
subsequently lower CO and CO
2
concentrations. This is because of high humidity translates into lower
temperature therefore affection the fire intensity or EFIP.
5.4. Gas Combustion Efficiency (GCE)
This is defined as the measure of how effectively the heat content generated of the burning fuel is
transferred into usable heat as a result of gas combustion [
35
]. Note that temperature, oxygen and
carbon dioxide are the primary indicators of combustion efficiency. For all presence of flame ={true}
inputs to the fuzzy model and combustion of gases dissipated in the atmosphere are measured in parts
per million per volume (ppmv). Hence, the gas combustion efficiency (GCE) is defined by the formula:
GCE =P∆CO2
P(∆CO+∆CO2)×100% (4)
where GCE represents the gas combustion efficiency of the burning fire flame with respect to
∆
CO
2
,
∆CO and O2.

Symmetry 2020,12, 1391 24 of 29
From the data experimental results in Table 6, we can determine the GCE of the gases dissipated
as per the simulated case study. Using the formula, defined in Equation (4), the rate of change of the
gases dissipated to the atmosphere i.e., ∆CO, ∆CO2, the GCE can be computed.
From extracted data we observe that, the summation of
∆
CO,
∆
CO
2
can be determined:
Σ(∆CO)=494.4 ppmv, Σ
(
∆CO2
)=474.6 ppmv. From the dataset in Table 6, the resultant GCE
can be approximately computed from the Equation (4).
The gas combustion efficiency (GCE) =
474.6
494.7+474.6 ×
100% =48.96%, Hence, the efficiency of
the gases dissipated into the atmosphere due to the burning fuel is equivalent to 49%. Meaning that,
approximately ~50% of the gases, i.e., CO, CO
2,
are dissipated into the atmosphere through the carbon
element burning in the fuel. With reference to the simulation data in Table 6, we observe that an
increase or decrease in the amount of gas dissipated has a subsequent increase or decrease on the
fire intensity. This translates into an increased efficiency of the burning fuel. This can also be clearly
indicated by the dotted line curve in the graph of Figure 12, representing the estimated fire intensity
prediction (EFIP).
5.5. Model Performance Evaluation
In this paper, we evaluate the performance of the model and subsequently determine the
percentage accuracy, using accuracy rate as one of the standard evaluation parameters. The model is
evaluated using the two-factor decision authentication, to determine the test and the actual model,
then a percentage accuracy is determined depending on the inference rules, as shown in Tables 7and 8
as detailed below.
Table 7.
Model results evaluation for the initial environmental parameters, namely, temperature (
∆
T),
and humidity (∆H) for 12 sampled control experiments.
Expt. No. ∆T(◦C) ∆H (%) Flame
Presence EFIP (%) Test Model Actual Model
Determined
Accuracy
Rate (%)
1. 9.0 17.5 True 17.6 L VL 50%
2. 15.1 27.1 True 18.3 L VL 50%
3. 38.0 58.4 True 43.6 M M 100%
4. 50.0 50.0 True 52.1 M M 100%
5. 28.3 38.0 True 22.9 L L 100%
6. 40.4 44.0 True 46.2 M M 100%
7. 46.4 48.8 True 52.1 M M 100%
8. 52.4 65.7 True 51.9 M M 100%
9. 62.9 71.7 True 54.5 M M 100%
10. 66.9 80.1 True 37.9 L L 100%
11. 68.1 52.4 True 71.2 H H 100%
12. 68.1 22.3 True 69.7 H H 100%
Estimated fire intensity prediction (EFIP): 0–20; VL, 20–40; L, 40–60; M, 60–80; H, 80–100; VH. Flame presence =“true”.
Table 8.
Model results evaluation for gas combustion, namely, carbon monoxide (
∆
CO), carbon dioxide
(∆CO2) and oxygen (∆O2) for 12 sampled control experiments.
Expt. No. ∆CO(ppm) ∆CO2(ppm) ∆O2(ppm) Flame
Presence EFIP (%) Test
Model
Actual
Model
Determined
Accuracy
Rate (%)
1. 23.6 21.4 23.6 True 47.8 M M 100%
2. 32.4 29.1 28.0 True 63.2 H H 100%
3. 36.8 30.2 31.1 True 69.1 H H 100%
4. 44.5 32.4 39.0 True 71.3 H H 100%
5. 50.0 37.9 44.5 True 75.0 H H 100%
6. 19.2 48.9 51.1 True 56.5 M M 100%
7. 13.7 8.2 13.7 True 25.0 L L 100%
8. 64.3 50.0 17.0 True 86.5 VH VH 100%
9. 70.9 24.7 32.4 True 62.8 H H 100%
10. 32.4 46.7 65.4 True 70.3 H H 100%
11. 48.9 75.3 86.3 True 25.0 L L 100%
12. 57.7 69.8 62.1 True 60.9 H H 100%

Symmetry 2020,12, 1391 25 of 29
For instance, in Table 7, Experiment 1, we observe that the rate of temperature (
∆
T) is 9.0
◦
C, rate of
change in humidity (
∆
H) is 17.5%, and the estimated fire intensity prediction (EFIP) is 17.6%, which is
L and the actual case is VL, which means an accuracy rate of 50% achieved. In Expt. 2,
∆
T is 15.1
◦
C,
∆H is 27.3% and the EFIP is 18.3%, meaning there is 18.3% probability of a fire occurrence.
The test case is L and, actual case is VL, gives an accuracy rate of 50%. In Expt. 3, we show that
∆
T
is 38.0
◦
C,
∆
H is 58.4% and the EFIP is 43.6%. Hence, 43.6% there is a probability of a fire occurrence.
The test case is M and the actual case is M giving an accuracy rate of 100%. From Experiment
4–12, we observed that the accuracy rate is 100%, meaning the tested model is working according
to the defined fuzzy inference rules. Then, the above method is subsequently applied to the second
experiment shown in the Table 8. Then, the overall accuracy rate of the proposed model can be
calculated below as:
Test Model Accuracy Rate (%) = Xµ(ai)
n(5)
In Equation (5), we compute the accuracy rate of the proposed model, where
µ
(ai) represents the
accuracy percentage of each experiment and, n the total number of simulated experiments. According
to the experiments, we achieved an average overall accuracy rate of 95.83%. Hence, using the fuzzy
logic approach significantly improves fire detection with an overall accuracy of 95.83%.
The original novelty of the research works demonstrates that using Mamdani’s FIS method
exhibits a significant improvement in terms of rate accuracy for the proposed fuzzy control model.
This subsequently translates into an effective design for early fire detection and safety control for
the local urban markets. In this research approach, we achieved an average overall accuracy rate of
95.83%, as compared to the performance of previous related works as illustrated in Table 9of [
7
,
13
,
18
].
Hence, the proposed solution can effectively detect early fires, making it advantageous to single
smoke sensor based systems. This solution is cheaper and more affordable to the developing countries
compared to the satellite-based systems and a likely substitute for human patrol mechanisms that
are being used today in detecting fires in the local urban markets of East Africa (EA). In the above
experiment, we realize that application of a multisensory based fire detection system solutions,
significantly improves performance and subsequently minimizes the rate of false alarms induced.
Hence, the application of fuzzy logic method successfully improved the overall accuracy rate to 95.83%,
using the defined inference rules.
Table 9. Performance comparison with previous related works.
Features. Kaur et al.
(2019) [18]
Sawar et al.
(2018) [15]
Abedi et al.
(2019) [13]
Sowah et al.
(2020) [7]Proposed Solution
Multisensor
parameters
Four inputs:
temperature,
humidity, smoke
and flame
Yes. Four input
parameters:
temperature,
humidity, time
and flame.
Yes. Three input
parameters: smoke,
temperature and
humidity.
Yes. Four input
parameters, smoke,
temperature,
humidity and flame.
Yes. Six input
parameters:
temperature,
humidity, CO, CO
2
,
O2and flame.
Method or
technique
K-means
clustering,
adaptive ANFIS
Single simulated
expt. model
using fuzzy
logic method
Analytical network
processing (ANP),
fuzzy logic
Fuzzy logic method,
trained CNN, a deep
learning technique
Two separate
integrated
simulated expt.
models using fuzzy
logic method
Accuracy rate (%)
93.12% 95.8% 81.9% 94.0% 95.83%
False alarm
detection
Sends early
warning signals
Yes. Notification
warnings
Generates a forest
fire risk map
Yes. Web notification
platform
Yes. Determination
of fire intensity
status notifications
followed with
appropriate action.
Decision on two
authentications No Yes No No Yes

Symmetry 2020,12, 1391 26 of 29
6. Conclusion and Future Works
In this paper, we present an IoT-based fuzzy approximation prediction model using the Mamdani
inference system to aid effective fire safety management and control for vendor community in
the local urban markets. The major significance of the model is aimed at an early detection by
minimizing extensive damage, through notifying responsible authorities for quick action and allow
for safe evacuation of occupants. Previous research works propose different methods of detecting
fire incidences. In this paper, we employ a multisensory fuzzy logic application to achieve accurate
results and minimize false alarm rates. In this approach, six input parameters are applied to the model,
namely, rate of temperature (
∆
T), humidity (
∆
H), carbon dioxide (
∆
CO
2
), carbon monoxide (
∆
CO),
oxygen (
∆
O
2
) and flame presence, under different operating conditions vis-
à
-vis the estimated fire
intensity prediction (EFIP). The system shall alert responsible authorities if an abnormal environmental
situation is detected. The EFIP represents the percentage probability of true fire incidences realized and
then an appropriate action is instigated, such as sending alert message, or initiating water sprinklers to
suppress the fire immediately.
Simulation experiments were performed using the MATLAB Fuzzy Logic toolbox. Results obtained
achieved an overall average accuracy rate of 95.83%, as discussed above. In future works, we intend to
develop an intelligent fuzzy-based fire detection algorithm using the proposed design model.
Author Contributions:
The following authors fully participated in the drafting and conceptualization of the
research; C.M. and E.L.; methodology, C.M.; software, E.L.; validation, E.L., A.N. and D.M.; formal analysis, E.L.;
investigation, E.L.; writing—original draft preparation, A.N.; writing—review and editing, E.L.; visualization,
C.M.; supervision, D.M.; project administration. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research study is being funded by the World Bank Group, through the African Center of Excellence
in Internet of Things (
ACEIoT
), under the Department of Computer & Software Engineering, College of Science
and Technology (CST), University of Rwanda.
Acknowledgments:
E.L. wishes to thank C.M. for leading the research study. E.L. wishes to put on record,
an endless support and appreciation provided by A.N. and D.M. for their continued reviews and advice rendered
in carrying out this research study. Finally, E.L. wishes to thank all the anonymous reviewers and editors for their
continued support by reading through the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
API Application Programming Interface
ANP Analytical Network Processing
ANFIS Adaptive Neural Fuzzy Inference System
CNN Convolutional Neural Networks
CO Carbon monoxide
CO2Carbon dioxide
EA East Africa
EFIP Estimated Fire Intensity Prediction
FAM Fuzzy Associative Memory
FCS Fuzzy Control System
FIS Fuzzy Inference System
FMWS Fire Monitoring Warning System
GCE Gas Combustion Efficiency
GIS Geographical Information System
GSM Global System for Mobile Communication
IDE Integrated Development Environment
IoT Internet of Things
O2Oxygen
MCU Micro Controller Unit
MEM Micro Electro Mechanical
MF Membership Function
MQTT Message Queue Telemetry Transport
TSK Takagi, Sugeno, Tangi

Symmetry 2020,12, 1391 27 of 29
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