ISeCureThe ISC Int'l Journal ofInformation Security

August 2019, Volume 11, Number 3 (pp. 51–58)

http://www.isecure-journal.org

Selected Paper at the ICCMIT’19 in Vienna, Austria

Evaluation of Planet Factors of Smart City through Multi-layerFuzzy Logic (MFL)I

Areej Fatima 1, Muhammad Adnan Khan 1, Sagheer Abbas 1,∗, Muhammad Waqas 1,Leena Anum 1, and Muhammad Asif 11Department of Computer Science, National College of Business Administration & Economics, Lahore, Pakistan

A R T I C L E I N F O.

Keywords:IoT, Smart City, PFSC, MultilayerFuzzy logic.

Abstract

Internet of Things (IoT) approach is empowering smart city creativities all overthe world. There is no specific tool or criteria for the evaluation of the servicesoffered by the smart city. In this paper, a new Multilayer Fuzzy InferenceSystem (MFIS) is proposed for the assessment of the Planet Factors of smartcity (PFSC). The PFSC system is categorized into two levels. The proposedMFIS based expert system can categories the evaluation level of planet factorsof the smart city into low, satisfied, or good.

c© 2019 ISC. All rights reserved.

1 Introduction

I nternet of Things (IoT) is the most frequently dis-cussed worldview that imagines in the future in

which all of the things that are used in the daily rou-tine of life must be embedded with microcontrollers,sensors and transceivers for digital transmission andcommunication, and appropriate protocol nodes. Thatkind of protocol can be used to make them able tocreate a great opportunity to speak with each otherand with the clients, becoming a vital portion of theInternet [1].

However, such a varied field of the utility of IoTfor the smart city makes not only the recognition ofbest solutions but also a necessity of all related util-

I The ICCMIT’19 program committee effort is highly acknowl-edged for reviewing this paper.∗ Corresponding author.Email addresses: areejfatima@lgu.edu.pk,madnankhan@ncbae.edu.pk, dr.sagheer@ncbae.edu.pk,waqas.ger@gmail.com, leenarehman@ncbae.edu.pk,muhammadasif@ncbae.edu.pkISSN: 2008-2045 c© 2019 ISC. All rights reserved.

ity situations an alarming challenge. [2]. The mostimportant thing is IoT platform design and develop-ment which requires a perfect solution that is calledmiddleware-level solution to enable the seamless in-teroperability between machine-to-machine based ap-plication and existing internet-based service [3].

All the more, by and large, we could state thata smart city procedure goes for utilizing the tech-nology to expand the personal satisfaction in ur-ban space, both enhancing the environmental qual-ity and conveying better administrations to the res-idents [4].Transmission and information technologyare among the most important aspects used to sup-port and change urban city to smart city [5]; there-fore, a digital city is frequently utilized synonymousand identical to a smart city. The application andarchitecture of the IoT paradigm to the smart city isprincipally alluring to nearby and regional organiza-tions that may turn into the early adopters of suchadvances. Accordingly, going about as catalyzers forthe appropriation of the IoT worldview on a moreextensive scale [6].

The purpose of this explanation is to talk about

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52 Evaluation of Planet Factors of Smart City through Multi-layer Fuzzy Logic (MFL)I — Areej Fatima et al.

a general reference structure for the plan of an ur-ban IoT. Smart city defines the exact features andproperties of the urban city.

2 Literature ReviewThe Internet of Things (IoT) is the interconnectionof various physical gadgets, vehicles, mobile, and dif-ferent things implanted with hardware, programming,sensors, actuators, and availability which empowersthese items to interface and trade information [7].

The first important factor for the smart city is sus-tainable energy integration and consumption for anessential multi-axial complex of smart city design andarchitecture [8]. The aggressive growth of urban citycauses an increase in pollution and waste manage-ment matters in most of the countries all over theworld [9]. For instance, Pakistan has similarity issuessince last few decades even in developed cities in-cluding Karachi, Islamabad, and Lahore. Smart cityinitiatives are aimed to create digital innovation tomake the lives of citizens batter and improve urbanliving sustainably by excelling in environment andproperties, transportation and people amenities, suchas education, healthcare, and living [10]. To calculatethe evaluation of PFSC, we need to follow a specificmodel similar to swarm intelligence that is advanceresearch in which we have to analyse and identify cor-respondence algorithms that represent the behaviorsmodeling techniques. By using swarm Intelligence, wecan define many algorithms that represent the behav-iors of the swarm of flies and other organisms suchas fishes, bees or insects. When we have many inputsand outputs, we have to use MIMO (Multiple-Inputand Multiple Outputs) technologies to improve ourquality of the system in IoT and smart city [11].

Whereas in PFSC, many other elements are in-volved, but there is a great role of RFID technologyof novelty for traffic blocking and controlling. Sensorsplay an important role for this purpose that identifiesevery action by using an RFID reader. At this stage,we have to use an intelligent system to maintain thedynamic changes of time whenever a single actionoccurs [12].In the current era, new cities have issuesabout the urbanization, and other improve life stan-dard. The latest research says that social networkingrequires a specific and precise framework that canhelp to analyze the data set of social networking sitesand sensor devices in the smart city [13].

We need a specific method in which we have totransmit a path that identifies in different devices.A multicarrier method that is Multi-Carrier CodeDivision Multiple Access (MC-CDMA) provide thesolution to this problem [14]. Another important issueis how much Multiple Input and Multiple Output

Figure 1. DFD of Proposed Methodology of PFSC

(MIMO) systems are helpful in PFSC. This issue ismuch highlighted in wireless technologies. MC-CDMAwith Alamouti’s Space-Time Block Codes (STBC)is the paramount solution of this issue [15]. FuzzyLogical Controller (FLC) that can be implemented forprototyping panel and mathematical results of PFSCcalculations are assessed for altered configuration andinput parameters [16].

3 Proposed Methodology MultilayerFuzzy Logic of PFSC System

New processing strategies give fluffy rationale can beutilized as a part of the improvement of insightfulframeworks for basic leadership, ID, design acknowl-edgment, streamlining, and control [17]. Fuzzy sur-mising guidelines will be an aid for giving a roundcheck to all components in urban areas. The plan isrequired to encourage wellbeing, economic framework,biological system, and energy and water utilizationother parental figures to make choices wisely. In thismanner, the IoT based framework can be utilized allthe more proficiently without trading off with the na-ture of the administration of the framework [18]. Fig-ure 1 demonstrates DFD of proposed methodology ofPFSC and Figure 2 demonstrates proposed method-ology multilayer fuzzy interface system of PFSC.

3.1 Inputs Variables

The membership function of this system gives curveoutput values between 0 and 1 and also provides amathematical function that offers statistical valuesof input and output variables. The only first layer(energy and mitigation factors) considered as level 1,the rest of the layers is correspondence.

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August 2019, Volume 11, Number 3 (pp. 51–58) 53

Figure 2. Proposed Methodology Multilayer Fuzzy InterfaceSystem of PFSC

3.2 Outputs Variables

The output variables for multiple-layer of proposedPFSC needs to be set up. First of all, we have toevaluate the results from layer one; if the resultsare satisfying the condition, then layer two will beactivated.

3.3 Member Function

Membership function represents the curve valuesranged between 0 and 1. It is a mathematical nota-tion of input and output variables. FIS input/outputvariables of EMF for layer one and layer two witha graphical and mathematical representation of theproposed methodology PFSC system is demonstratedin Figure 3; where first five rows represent the inputmember function while row six represents the outputmember function.

3.4 Fuzzy Set Operations

The most important set operations in fuzzy are anintersection, union and additive compliment. Theymanage the essence of fuzzy logic. If there are twofuzzy sets A and B defined on the universe X, x∈XThen the fuzzy set operation can be written as:

Figure 3. EMF for Layer One and Layer Two Input/OutputVariables Membership Functions Proposed Methodology PFSCSystem

Intersection(AND) = µA∩B(x) =min(µA(x), µB(x))

Union(OR) = µA∪B(x) = max(µA(x), µB(x))AdditiveComplement(NOT ) = µA(x) = 1− µB(x)

3.5 Fuzzy Propositions

A proposition is a statement that is either true orfalse. There is multi-layered architecture proposed toevaluate PFSC in two levels of layers.

3.5.1 Layer Level One

Here, layer one contains 5-factor layers correspon-dence with EMF, MWL, CR, PW, and ES. Everylayer has its member function represented by vari-ables.

EMF = t : A×R× C =⇒ T1

All input and output variable values are mappedfrom real range to probability ranges because fuzzy

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54 Evaluation of Planet Factors of Smart City through Multi-layer Fuzzy Logic (MFL)I — Areej Fatima et al.

expert system works on probability (range 0-1). T-norm function of layer level one can be written as:

EMF = t : [0, 1]× [0, 1]× [0, 1] =⇒ T1

Equation 1 and 2 convert the membership functionsof fuzzy sets of simulation, Energy and Mitigation(EM), Material, Water and Land (MWL), ClimateResilience (CR), Pollution and Wastage (PW), EchoSystem (ES) as layer level one.t[µA(a), µR(r)µC(c)] = min(µA(a), µR(r), µC(c))

(1)

µA∩R∩C(a, b, c) = t[µA(a), µR(r), µC(c)] (2)

µH∩T∩E(h, t, e) = t[µH(h), µT (t), µE(e)] (3)

µA∩R∩C(a, r, c) = min(µA(a), µR(r), µC(c)) (4)

µM∩W∩P∩L(m,w, p, l) =min(µM (m), µW (w), µP (p), µL(l))

(5)

Equation 4 and 5 represent the minimum of the in-tersection of all sets.

3.5.2 Layer Level Two

Layer two contains five member functions respectivelyEM, MWL, CR, PW, and ES. Every layer has theirmember function represented by variables.t : EM ×MWL× CR× PW × ES =⇒ Lt

t : [0, 1]× [0, 1]× [0, 1]× [0, 1]× [0, 1] =⇒ Lt

t[µEM (em), µMWL(mwl),µCR(cr), µPW (pw), µES(es)]

= min(µEM (em), µMWL(mwl),µCR(cr), µPW (pw), µES(es))]µEM∩MWL∩CR∩PW∩ES

(em,mwl, cr, pw, es) =min(µEM (em), µMWL(mwl),µCR(cr), µPW (pw), µES(es))

(6)

Equation 6 specifies the minimum of the intersectionof all sets.

3.6 Lookup Table

The fuzzy rule base is the important element of FuzzyInference System (FIS) because other components ofFIS like rules surface and rules viewer are dependentupon fuzzy rule base. Fuzzy rule base of proposedPFSC contains 27 rules at layer one (Only for EMF)and denoted by Rαn , where 1 ≤ n ≤ 27.

Rα3 = IF ES is Poor AND PW is Poor AND CRis Satisfied AND MWL is Poor AND EM is Poorthat represents weightage of PFSC is very poor.

µp1(em,mwl, cr, pw, es) = µPoor(ES),µPoor(PW ), µSat(CR), µGood(MWL), µPoor(EM)Rα300 = IF CR is Satisfaction AND MWL is GoodAND EM is Good that represents weightage of

PFSC is very Good.

µG1(em,mwl, cr, pw, es) =µn/a(ES), µn/a(PW ), µSat(CR),µGood(MWL), µGood(EM)

µSCPF (p1, s1, g1) =

max(min(1, 0.09− 00.1 ), 0)|

max(min(0− 0.140.1 ,

0.06− 00.1 ), 0)|

max(min(0− 0.490.1 , 1), 0)

Fuzzy rule PFSC System (Layer Two) contains 300rules denoted by Rαn , where 1 ≤ n ≤ 300.

3.7 Mamdani Implications

The fuzzy IF-THEN rules Rα27, Rβ144,Rγ27, Rπ120,and Rφ27 are with membership functions of the firstlevel layer as follow:

Rαn = µEMF (a,r,c)

A1 = if use of a,r,c are poor conditionThen the fuzzy output must be

A2 = use of Energy and Mitigation Factors (EMF)must be poor for first level layer

µA1(a, r, c) = µpoor(a)µpoor(r)µpoor(c)

or we can write it as:

µA1(a, r, c) = {( 1000−a

0.1 )( 900−r0.1 )( 09−c

0.1 )

0}

or

{(1000−a)(900−r)(09−c)

(0.1)3

0}

The output of fuzzy are as follow:

µA2(t) = µPoor(t)

µA2(t) = {(0.15−t)

(0.1)

0}

ift =⇒ [0, 1]

Similarly, we can get rest of layer Rα140 , Rβ27 , Rγ120 ,and Rπ27 .

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August 2019, Volume 11, Number 3 (pp. 51–58) 55

Rβ1300 for the second level layer with membershipfunctions are written as

Rβ1300 = µSCPF (em,mwl, cr, pw, es))

Suppose:

B1 = IF ES is Poor AND PW is Good, ANDCR is Poor AND MWL is Poor AND EM is Poor,

Then fuzzy output must beB2 = weight of PFSC is very poor

Then we may calculate the fuzzy product ofsimulation:

µSCPF (em,mwl, cr, pw, es) = µpoor(ES),µpoor(MWL), µpoor(CR), µpoor(PW ), µpoor(EM)

or we can write it asµB1(em,mwl, cr, pw, es) =

{( (0.15−es)

(0.1) )( (0.4−mwl)(0.1) )( (0.3−cr)

(0.1) )( (0.38−pw)(0.1) )( (0.36−em)

(0.1) )

0}

= {( (0.15−es)(0.4−mwl)(0.3−cr)(0.38−pw)(0.36−em)

(0.1)5 )

0}

ifes,mwl, cr, pw, em =⇒ [0, 1]

The output of fuzzy are:

µB2(t) = {( (0.09−t)

(0.1) )

0}

ift =⇒ [0, 1]

3.8 Fuzzy Interface Engine

The process of combining the fuzzy IF-THEN rulesfrom the fuzzy rule base into a mapping from a fuzzyinput set to fuzzy output based fuzzy logic principleis called the fuzzy inference engine. The main com-ponent of fuzzy inference is membership functions,fuzzy logic operators, and if-then rules. All rules inthe fuzzy rule base are combined into a single fuzzyrelation that lies under the inner product on inputuniverses of discourse, which is then viewed as a sin-gle fuzzy IF-THEN rule. A reasonable operator forjoining the rules is union.Layer one IF-THEN fuzzy represent as:

Rαn = An ×Rn × Cn

µA∩R∩C(a, r, c) = µA(a) ∩ µR(r) ∩ µC(c)(7)

Interpreted as a single fuzzy relation defined by:

R27 = t2n=17Rna

Suppose π, λ, and ψ be any three arbitrary fuzzysets and are also input and output to the fuzzy in-ference engine, respectively. To view R27 as a single

fuzzy IF-THEN rule and using the generalized modusponens:

µPoor∩Satf∩Accept(ϕ) = Supλε(A,R,C)

t[µλ(A,R,C), µR27(A,R,C)]

Using product inference engine format we have:µϕ(T ) = max06x627[Supa,r,cεU(µA,R,C(a, r, c))

(q27k=1(µak,rk,ck(ak

, rk, ck))µϕx(T ))]

Layer Two IF-THEN fuzzy represent as:

Rβn = En ×Mn × Cn × Pn × EMn

µE∩M∩C∩P∩EM (e,m, c, p, em) =µE(e) ∩ µM (m) ∩ µC(c) ∩ µP (p) ∩ µEM (em)

Interpreted as a single fuzzy relation for layer twois defined by:

R300 = t300n=1R

na

Interpreted as a single fuzzy relation defined as:Ran

3.9 De-Fuzzifier

For discrete membership function, the defuzzifiedvalue denoted as x∗ using COG is defined as:

x∗ =∑ni=1 xiµ(xi)∑ni=1 µ(xi)

where xi indicates the sample element, µ(xi) is themembership function, and n represents the numberof elements in the sample. The center of gravity de-fuzzifier specifies x∗ and x∗∗, as the center of areacovered by a member function of A and B.Center of Gravity (COG) for layer one and two arecomputed as represented in equation 8 and 9, re-spectively.

x∗ =∫ vµa( v)d v∫µa

(v) d v

Centerofgravity(COG)forLayer2(8)

x∗ =∫Bµb(B)dB∫µb(B)dB

(9)

Where∫

is the conventional integral.

Figure 4 represents the 3D view of rule surface ofproposed PFSC only based on CR and EM. It ob-served that PFSC weightage is Good (Yellow shade)if CR is ≥0.6 (60%), and PFSC weightage is Satisfy(Greenish Shade) when EM Simulation is lain be-tween 0.15 and 0.4 (15% and 40%). Otherwise, PFSCweightage is Weak or Poor (Bluish Shade).

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56 Evaluation of Planet Factors of Smart City through Multi-layer Fuzzy Logic (MFL)I — Areej Fatima et al.

Figure 4. Rule Surface of Proposed PFSC based on ClimateResilience and Energy Factors

Figure 5. Rule Surface of Proposed PFSC System Based onMWL and ES

Figure 6. Rule Surface of Proposed PFSC System Based onPW and MWL

Figure 5 and Figure 6 represent the 3D viewby prevailing different input parameter values. Theformer is based on MWL and ES, while the latteris based on PW and MWL. Yellow, Greenish, andBluish shade represents weightage Strong, Good, andPoor respectively.

4 Simulation ResultsFor layer two simulation results, the five-memberfunction has 300 inputs and outputs representinglayer-2’s, and results show that what weight is forPFSC. Since layer two evaluation is final, it indicatesthe actual strength and feasible planet factors forthe smart city. Figure 7 shows that if the valuesES, EMF, and CR are in a lower range while PWand MWL are in a high range, then PFSC is not anefficient condition. Figure 8 shows that if the valuesES and EMF are not available while CR is in an

Figure 7. Layer Two Lookup Diagram for Proposed PFSCSystem (Poor)

Figure 8. Layer Two Lookup Diagram for Proposed PFSCSystem (Satisfy)

Figure 9. Layer Two Lookup Diagram of Proposed PFSCSystem (Good)

average range, PW and MWL are in a high range,then PFSC is an efficient condition. Figure 9 showsthat if the values ES and EMF are not available whileCR is in an average range, PW and MWL are in ahigh range, then PFSC is an efficient condition.

5 ConclusionThe connected approach for computing smart indi-cator loads features, for choosing such criteria, withthe significance of the decision maker’s subjectivity.Indeed, doling out the heaviness of a savvy pointer re-garding another smart marker for planet factors, eachdecision maker is conveyed to reason in a less targetway [19]. If there should arise an occurrence of a realcity, the foundation of right qualities requires the mas-ter’s commitment to the different picked fields [20].This research has opened doors of innovation for dif-ferent major projects in Pakistan. Gwadar is Pak-istan’s biggest up-coming project, started a few yearsago. Although the first phase of the Gwadar port hasbeen established, and billions of dollars have beeninvested in infrastructure, yet there is no evaluation

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August 2019, Volume 11, Number 3 (pp. 51–58) 57

and estimation weight for supporting and validity fora smart city. By using this model, we can get a goodestimation of the evaluation of this city for the PFCSsystem. Consequently, it will be conceivable to fur-nish the arrangement creator with data for "readyconsultation" to give him the data that permit tovisit and to gauge the impacts of his mediation. Theproposed creative framework results in an undeni-ably expanded understanding and essential use, forboth the chiefs and the resident, without respectingabilities and individual subjectivity.

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58 Evaluation of Planet Factors of Smart City through Multi-layer Fuzzy Logic (MFL)I — Areej Fatima et al.

Areej Fatima is currently work-ing as a lecturer at the departmentof computer science, Lahore Garri-son University, Lahore, Pakistan. Sheis doing a PhD at the School ofcomputer science, NCBA&E, Lahore,Pakistan. She completed her master

of computer sciences from the department of com-puter science NCBA&E, Lahore, Pakistan. Areej’sresearch interests primarily include cloud computing,IoT, intelligent agents, image processing and cogni-tive machines with various publications in interna-tional journals and conferences.

Muhammad Adnan Khan is cur-rently working as an assistant pro-fessor at the School of computer sci-ence, NCBA&E, Lahore, Pakistan.He completed his PhD at ISRA Uni-versity, Pakistan. Before joining theNCBA&E, Khan has worked in var-

ious academic and industrial roles in Pakistan. Hehas been teaching graduate and undergraduate stu-dents in computer science and engineering for thepast ten years. Presently he is guiding 4 PhD schol-ars and 3 M.Phil. Scholars. He has published about109 research articles in international journals as wellas reputed international conferences. Khan’s researchinterests primarily include MUD, channel estimationin multi-carrier communication systems using softcomputing, image processing and medical diagnosiswith various publications in journals and conferencesof international repute.

Sagheer Abbas is currently work-ing as an assistant professor atthe School of computer science,NCBA&E, Lahore, Pakistan. Hecompleted his PhD at the School ofcomputer science, NCBA&E, Lahore,Pakistan. He completed his M.Phil.

In computer science at the School of computer science,NCBA&E, Lahore, Pakistan. He has been teachinggraduate and undergraduate students in computer sci-ence and engineering for the past eight years. He haspublished about 48 research articles in internationaljournals as well as reputed international conferences.Sagheer’s research interests primarily include cloudcomputing, IoT, intelligent agents, image processingand cognitive machines with various publications ininternational journals and conferences.

Muhammad Waqas has com-pleted M.Phil from the departmentof computer science, of NCBA&E,Lahore, Pakistan. Waqas’s researchinterests primarily include cloud com-puting, IoT, intelligent agents andcognitive machines with various pub-

lications in international journals and conferences.

Leena Anum is an assistant profes-sor at the School of business adminis-tration at NCBA&E, Lahore. She didher PhD in business administrationin March 2019. Her research interestsinclude various cross-disciplinary ar-eas ranging from knowledge manage-

ment, knowledge-based systems to smart cities.

Muhammad Asif is currentlyworking as a lecturer at the depart-ment of computer science, NCBA&E,Lahore, Pakistan. He completed hismaster of computer sciences fromthe department of computer scienceNCBA&E, Lahore, Pakistan. Asif´ s

research interests primarily include fuzzy system,cloud computing, IoT, and smart city with variouspublications in conferences of international repute.

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