Proposed a Smart Solutions Based -on Cloud Computing and ... · Proposed a Smart Solutions Based...

Proposed a Smart Solutions Based-on Cloud

Computing and Wireless Sensing 1Haider Rasheed Abdulshaheed,

2Wahidah Binti Md Shah,

3Siti Azirah Binti

Asmai and 4Isra Ibraheem Sadiq

1,2,3,4Faculty of Information and Communication Technology,

Universiti Teknikal Malaysia Melaka, HangTuah Jaya,

76100 Durian Tunggal, Melaka, Malaysia.

Abstract

this paper presents and discusses the performance results of both cloud

service providers and sensor service provider’s integration process. The

different security models, attacks and the efficacy of the proposed model

are highlighted in previous chapter. It is not easy to evaluate the

integration performance of cloud computing and wireless sensor network

because it’s a different domain category. In this chapter, the proposed RIP-

WSN-CC system satisfied the prearranged functions based on the trust and

reputation estimation. Then, implementing the proposed system in the real

time applications based on their security. Then, evaluate the individual

performance of the RIP-WSN-CC system with accessing the relevant data

in the network. Here, the proposed methods are tested by the suitable

environment and compared with the existing integration techniques. The

proposed system achieves better results when used high trust and

reputation values which give less execution time, and high accuracy.

Key Words:IoT, Cloud computing, wireless sensor.

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 427-449ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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1. Introduction

Giving the most reliable, available, secure cloud and wireless sensing services

that achieve the end-user requirements and preferences is the most important

aspects in integrating both CC-WSN infrastructures. Even though many studies

in the literature addressed most of these aspects but still many works need to be

done to optimize the integration solution to reach to the level of answering end-

users enquires from most trusted sensors network providers that is connected

with the most reliable cloud service providers. This most trusted and reliable

integration is becoming more important recently in developing smart IoT

solutions. The planning of such design of integration solution involves on

complex mathematical modeling and equations that are defining the different

services with their associated parameters values according to the end-user

preferences of payment and the quality of the services. Also, the connection

between these different services of wireless sensing and cloud data storage.The

proposed integration platform in this research work is developed as

mathematical modeling and implemented in cloud-environment of the JAVA

and SQL/HTTP Servers (as Servers Services obtained from WAMP Server

platform). The certain tourism online sql database answer the end-user enquires

by giving the required information for certain places of tourism attractions taken

from big data of stored Indian places. These implementations showed how to

switch and connect to most trusted services providers based on their historical

reputation values. The analysis is extended to cover small entities of services’

providers and services’ users as well as the large entities. This analysis showed

the enhancements of the proposed integrated platform solution in many aspects

in comparison with other near-fields accessed controlled methods in the

literature.

2. Limitations of Existing Solutions

In existing work, cloud computing wireless sensor network (CC-WSN)

integration with the CSU does not recover the desirable service from CSP. But

satisfied the service obtained from SNP. The authenticating CSPs and SNPs and

the calculation value of trust, reputation of CSPs, SNPs are the two main critical

and scarcely explored issues (Savas, et al. 2013). The following points reveal

that the major problem arising in the existing works. Hence, required the

proposed system for solving the issues as less authentication security, CSU can

select the CSP with no trust and reputation, CSP can select the SNP with low

trust and reputation, produced low accuracy, high execution time,

eavesdropping, impersonation, limited resource and spoofing

3. Performance Metrics

The performance metrics used for analyzing the CC-WSN integration work are

like accessible resources, truth evaluation; turn around efficiency, reliability,

availability, data integrity, reputation value, effective recommendation rate,

International Journal of Pure and Applied Mathematics Special Issue

428

malicious node detection rate and malicious node isolation rate

4. Truth Evaluation

Trust is defined as the progression of secure the data in confidence manner

which can be classified into reliability trust and decision trust. The welfare

function that depends on the individual expectations of trust with another

individual performance that achieves the subjective probability is known as the

Reliability trust. The second type of trust is the decision trust in which the

individual depend with somebody or something that extends to a negative

consequences of a relative security that safe the situation in a great process.

Trust accuracy is the proportion of locating correct trust assessment from the

trust mechanisms to the total number of estimations. The truth evaluation

accuracy is representing in the system of percentage.

𝑇𝐴 = 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑙𝑜𝑐𝑎𝑡𝑒𝑑 𝑡𝑟𝑢𝑠𝑡 𝑣𝑎𝑙𝑢𝑒

𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛𝑠

The proportion of success trade rate to the ultimate number of transactions is

called the transaction success rate.

𝑇𝐸 = 𝑠𝑢𝑐𝑐𝑒𝑠𝑠 𝑡𝑟𝑎𝑑𝑒 𝑟𝑎𝑡𝑒

𝑢𝑙𝑡𝑖𝑚𝑎𝑡𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠

5. Turn Around Efficiency

Turn around efficiency is the proportion of actual turnaround time from the

promised turnaround period. If the efficiency of turnaround is 1 due to an actual

turnaround period because it’s lesser than the promised turnaround period.

During the period, submit overall the work of average turnaround efficiency is

known as the turnaround efficiency of a resource (Manuel 2015). It integrates

the networking speed and the computing power that represents the number of

transaction per second.

𝑇𝐸 = 𝑝𝑟𝑜𝑚𝑖𝑠𝑒𝑑 𝑡𝑢𝑟𝑛𝑎𝑟𝑜𝑢𝑛𝑑 𝑡𝑖𝑚𝑒

𝑎𝑐𝑡𝑢𝑎𝑙 𝑡𝑢𝑟𝑛𝑎𝑟𝑜𝑢𝑛𝑑 𝑡𝑖𝑚𝑒

The promised turnaround time is defined as the projected time from a supply

provider between the suggestions of a work and delivers the finished work. This

is the required time obtaining from the resource provider offers the

authenticated user in the SLA. The authentic improvement of period is defined

as the difficult period of the user between the suggestions of a work and delivers

the completed work. In SLA resource provider, describes that the promised

turnaround time to the actual turnaround time is completely different values.

1) RELIABILITY

Reliability is defined as one of the property of trust component. The system or

component can able to perform the required function in a specified period of

time due to the quantified conditions is called the reliability. Data are often

reflected reliable when they are particular and accurate, and when they can be


429

reproduced. It is the degree of complete achievement of known jobs by the

cloud resources.

𝑅𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠 = 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 𝑗𝑜𝑏𝑠 𝑏𝑦 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠

𝑎𝑐𝑐𝑒𝑝𝑡𝑒𝑑 𝑗𝑜𝑏𝑠 𝑓𝑟𝑜𝑚 𝑟𝑒𝑠𝑜𝑟𝑐𝑒𝑠

2) AVAILABILITY

Availability is defined as the stored data in the computer that will access only

by authorized persons. The hardware components and software should be

reliable if not the availability is not ensured and have no security.

𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠 = 𝑎𝑐𝑐𝑒𝑝𝑡𝑒𝑑 𝑗𝑜𝑏𝑠 𝑓𝑟𝑜𝑚 𝑟𝑒𝑠𝑜𝑟𝑐𝑒𝑠

𝑠𝑢𝑏𝑚𝑖𝑡𝑡𝑒𝑑 𝑗𝑜𝑏𝑠 𝑡𝑜 𝑟𝑒𝑠𝑜𝑟𝑐𝑒𝑠

3) DATA INTEGRITY

Availability is defined as the stored data in the computer that will access only

by authorized persons. The hardware components and software should be

reliable if not the availability is not ensured and have no security.

𝐷𝑎𝑡𝑎 𝑖𝑛𝑡𝑒𝑔𝑟𝑖𝑡𝑦 𝑜𝑓 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑑𝑎𝑡𝑎 𝑖𝑛𝑡𝑒𝑔𝑟𝑖𝑡𝑦 𝑝𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑑 𝑏𝑦𝑟𝑒𝑠𝑜𝑟𝑐𝑒𝑠

𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 𝑗𝑜𝑏𝑠 𝑡𝑜 𝑟𝑒𝑠𝑜𝑟𝑐𝑒𝑠

4) RECOMMENDATION RATE

The rate of number of reference reputations from the nodes with their security

significance to the number of all known recommendation. It is also called as

ERR.

5) RECOMMENDATION RATE

Rate of accuracy and flexibility are detected or identified in the malicious node

and it is called as MIDR then the malicious node isolation rate is called as

MIOR. The MIDR estimation gives the good recommendations like honest and

good quality while MIOR provides better flexibility and survivability results.

6) REPUTATION

Reputation is the estimation of something or someone in general for safe

keeping the information. The reputation shows the common characters or

standard in which only access the correct authenticator.

6. Simulation Environment

The following sub-sections illustrate the operation results of cloud computing

with wireless sensor networks integration system based on the reputation

calculation, management system and the authenticated trust in java software.

Initially, the home page of integration system of CC with WSN obtaining

CCSN network for accessing the relevant data. This figure represents the sensor

devices integrated with the cloud computing to produce the cloud based sensor

networks. It is mainly used for extracting the relevant data from a network with

high security. In this section, three major fields of service details are categorized

into sensor network provider details, cloud service provider details and cloud


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service user details fields. And also insert new button for each service providers.

It can used to insert the new data from the networks that can added more

numbers of data.

7. Exploration of Rip-WSN-CC Performance

The presentation analysis of the projected RIP-WSN-CC method is evaluated by

using JAVA language. The following sections show the projected method is

appraised with respect to corresponding functions. Accordingly, the third

proposed function evaluation based on the two case studies. It is recycled to

evaluate the usefulness of the projected RIP-WSN-CC method.

Analysis of the Scenario of Small Quantities of CSPs, SNPs, CSUs and SNUs

In this analysis, using small quantities of cloud and sensor network (users and

providers). In this review, taken for consideration such as 3 cloud users, 4 cloud

providers and 5 sensor providers are all involved. Cloud user strainer the cloud

providers which are never qualified based on the trust, reputation and

management calculation in between the CSU & CSPs and CSPs & SNPs.

According to their assumption, filter the attributes such as one CSP and two

SNPs and does not gratify the necessities. After assigned that three number of

cloud users, three cloud providers and the three sensor providers are used for

analyzing. Therefore, the users attribute requirements are satisfied by the cloud

provider characteristics and the cloud provider needs are satisfied by the sensor

provider characteristics. Table 1 shows the parameters of CSUs and qualified

CSPs. In this table, explains about 𝐶𝑐 ,𝑇𝑐𝑢 ,𝑅𝑐 ,𝐶𝑏𝑐 ,𝑇𝑠𝑢𝑐 ,𝑅𝑠𝑐 are the parameter

that is used to calculate the trust and reputation and also evaluate the

management of the flow between the CSU and CSP. Then, determined the most

reliable CSPs as proof of concept assumptions of the following parameters

assigned values as The Trust Values of Services from CSP to CSU ( 𝑻𝒄𝒖 ) can

take values from 0.6 to 0.9, The reputation Values of Services Provided by CSP

( 𝑹𝒄) can take values from 0.6 to 0.9., The Acceptable Range of Ck which is the

Sensor Network Service Charge to Sensor Network Service Pay (SNSC-SNSP)

is [-30,30], The Trust Values of the Services from CSP to CSU ( 𝑻𝒔𝒖𝒄) is fixed

as 0.5, The Reputation Values of Services Provided of CSP ( 𝑹𝒔𝒄)is fixed as 0.5

and 𝑪𝒄is the Cloud Service Charge to the Data Service Pay ( CSC-DSP ) and it

can take values from (-10) to ( -30 ) . These values of 𝑪𝒄 with the assignment

of above Parameters values determine how the CSU select the qualified CSP.

Table 1: CSUs and Qualified CSPs 𝑪𝒄

𝐶𝑆𝑈1 ⟷ 𝐶𝑆𝑃1 -10










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Table 2 shows the parameters of CSPs and qualified SNPs. In this table,

explains about 𝐶𝑘 ,𝑇𝑘𝑐 ,𝑅𝑘 ,𝐶𝑏𝑘 ,𝑇𝑠𝑘𝑐 ,𝑅𝑠𝑘 are the parameter that is used to

calculate the trust and reputation and also evaluate the management of the flow

between the CSPs and SNPs. Then, determined the qualified SNPs for

searching the relevant information. Assumptions of the following parameters

assigned values as (𝑇𝑘𝑐 ) is the trust values from SNP to CSP and assumed to

take values from (0.6) to (.8), (𝑅𝑘) is the Reputation Values of the Services

Provided by SNP from (0.5) to (0.7), ( Cbk ) is the Interval of Acceptable Range

of Sensor Network Service Charge to Sensor Network Service Pay (SNSC-

SNSP) and this interval is assumed as [-30,30], (𝑇𝑠𝑘𝑐 ) is the Minimum

Acceptable Trust Values from SNP to CSP and is assumed as (0.5), (𝑅𝑠𝑘) is the

Minimum Acceptable Reputation Values of The Service Provided by SNP and

is assumed as (0.5) and (𝐶𝑘) is the parameters considered as Sensor Network

Service Charge to Sensor Network Service Pay (SNSC-SNSP) and can be

assigned any values from (-10) to (-30). This parameters with the above values

of parameters can assist the CSPs to determine the qualified SNPs as the

following table shows.

Table 2: CSPs and Qualified SNPs

𝑪𝒌

𝐶𝑆𝑃1 ⟷ 𝑆𝑁𝑃1 -10









Then, evaluate the effectiveness of the proposed RIP-WSN-CC system using

two weight sets such as αc, βc, γc as well as αk, βk and γk. These two set of

weight are consider below for further processing the stage. In the weight set 1,

cloud users and cloud providers are taken these parameter 𝐶𝑐 ,𝑇𝑐𝑢 ,𝑅𝑐 for

processing altogether hooked on interpretation and the weight set 2 of cloud

users and cloud providers consider individual unique of 𝐶𝑘 ,𝑇𝑘𝑐 ,𝑅𝑘 for

consideration.

Table 3 shows the cloud users weight set1 and the selections. In this counter,

using the 𝑀𝑐 𝑎𝑛𝑑 𝑀𝑘 equation (referred in chapter 3) determine the

𝐶𝑆𝑈1 ,𝐶𝑆𝑈2 ,𝐶𝑆𝑈3 that all used to select 𝐶𝑆𝑃3.


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Table 3: Cloud Users and the Selections 𝜶𝒄 𝜷𝒄 𝜸𝒄 Choice

𝐶𝑆𝑈1 0.333 0.333 0.333 𝐶𝑆𝑃3

𝐶𝑆𝑈2 0.5 0.25 0.25 𝐶𝑆𝑃3

𝐶𝑆𝑈3 0.20 0.40 0.40 𝐶𝑆𝑃3

Table 4 represent the skilled cloud providers and the equivalent selections of

sensor providers. In this counter, all 𝐶𝑆𝑃1,𝐶𝑆𝑃2 ,𝐶𝑆𝑃3 selects the 𝑆𝑁𝑃1 by

calculating the parameters.

Table 4: Skilled Cloud Providers and the Equivalent Selections 𝜶𝒌 𝜷𝒌 𝜸𝒌 Choice

𝐶𝑆𝑃1 0.333 0.333 0.333 𝑆𝑁𝑃1

𝐶𝑆𝑃2 0.5 0.25 0.25 𝑆𝑁𝑃1

𝐶𝑆𝑃3 0.20 0.40 0.40 𝑆𝑁𝑃1

Meanwhile, Table 5 and Table 6 represent the second set of weights. Table 5.5

shows the cloud users and the equivalent selections of the cloud providers. The

utilization of equation 𝑀𝑐 𝑎𝑛𝑑 𝑀𝑘(referred in chapter 3) to predict 𝐶𝑆𝑈1 ,𝐶𝑆𝑈2

with respect to 𝐶𝑆𝑃3 and𝐶𝑆𝑈3 with corresponds to𝐶𝑆𝑃1 .

Table 5: Cloud Users and the Equivalent Selections

𝜶𝒄 𝜷𝒄 𝜸𝒄 Choice

𝐶𝑆𝑈1 1 0 0 𝐶𝑆𝑃3



Table 6 represents the skilled cloud providers and the equivalent selections of

the sensor providers. Furthermore, 𝐶𝑆𝑃1 chooses 𝑆𝑁𝑃3 while 𝐶𝑆𝑃2,𝐶𝑆𝑃3 both

selects the 𝑆𝑁𝑃1 as illustrates in the required table.

Table 6: Skilled Cloud Providers and the Equivalent Selections

𝜶𝒌 𝜷𝒌 𝜸𝒌 Choice

𝐶𝑆𝑃1 1 0 0 𝑆𝑁𝑃3



Analysis of the Scenario of Large Quantities of CSPs, SNPs, CSUs and SNUs

In this study comprise the cloud users, providers and sensor providers with large

amounts. So, the proposed work taken 100 cloud users, 150 cloud providers and

200 sensor providers for processing this incident. After filtered the irrelevant

services using filtering device to remove 50 cloud providers and 100 sensor

providers. Then, obtain 100 users, 100 providers and 100 sensor providers these

are satisfied the requirements. For example, needs of users attribute

requirements are satisfied by the cloud provider characteristics and the needs of

cloud providers attribute requirement are satisfied by the sensor provider

characteristics. 𝐶𝑐 ,𝑇𝑐𝑢 ,𝑅𝑐 ,𝐶𝑏𝑐 ,𝑇𝑠𝑐𝑢 and 𝑅𝑠𝑐 Are randomly initialized then used

to determine the trust and reputation value for corresponding CSU and CSP and

also the detailed parameters are used to determine the CSUs and the qualified

CSPs. In the same way, the comprehensive parameters such as skilled cloud


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providers and sensor providers are arbitrarily modified and they will be used

from the trust and reputation calculation between CSP and SNP (referred

chapter 3) about𝐶𝑘 ,𝑇𝑘𝑐 ,𝑅𝑘 ,𝐶𝑏𝑘 ,𝑇𝑠𝑘𝑐 and 𝑅𝑠𝑘 . Finally, validate the effectiveness

by initializing 100 varied weight sets like αc, βc, γc as well as αk, βk and γk.

Table 7: Equivalent Selections of Cloud Provider

Cloud users weight set Equivalent select of cloud provider

0 50

10 30

20 15

30 17

40 30

50 17

60 13

70 58

80 20

90 25

100 35

Table 7 shows the equivalent selection of cloud providers. The different weight

set of a CSU can used to predict the corresponding CSP because numerous

CSPs are involved to select the corresponding CSP based on high trust and

reputation value. If the weights are increasing than the corresponding choice

about CSPs are randomly decreasing and increasing in nature. The CSPs are not

in linear manner. For example, minimum number of weights of 10, the

corresponding choice about CSP of the proposed RIP-WSN-CC system is 30

respectively. For maximum number of weights of 100, the corresponding choice

about CSP is 35.Table 7 shows the equivalent selection of cloud providers. The

different weight set of a CSU can used to predict the corresponding CSP

because numerous CSPs are involved to select the corresponding CSP based on

high trust and reputation value. If the weights are increasing than the

corresponding choice about CSPs are randomly decreasing and increasing in

nature. The CSPs are not in linear manner. For example, minimum number of

weights of 10, the corresponding choice about CSP of the proposed RIP-WSN-

CC system is 30 respectively. For maximum number of weights of 100, the

corresponding choice about CSP is 35.

Figure 1: Equivalent Selections of Cloud Provider

0

50

100

0 10 20 30 40 50 60 70 80 90 100Eq

uiv

ale

nt

sele

ct o

f cl

ou

d

pro

vid

er

Cloud users weight set


434

The corresponding choice about CSP is obtained as 15 in the proposed RIP-

WSN-CC approach for 20 weight set of CSU. In the same way, the

corresponding choice about CSP during the processing of the proposed RIP-

WSN-CC system in the presence of different weight set for maximum range of

70 to produce 58 CSPs respectively. Table 5.8 shows the equivalent selections

of sensor provider. The different weight set of a CSP can used to predict the

corresponding SNP because numerous SNPs are involved in this process to

select the corresponding SNP based on the high trust and reputation value. If the

weights are increasing than the corresponding choice about SNPs are obtaining

in nature. For example, minimum number of weight set of qualified CSP is 10

than the corresponding choice about SNP of the proposed RIP-WSN-CC system

is 28 respectively. For maximum number of weight set of qualified CSP is 100,

the corresponding choice about SNP is 10.

Table 8: Equivalent Selections of Sensor Provider Skilled cloud provider weight set Equivalent select of sensor provider

0 26

10 28

20 20

30 58

40 18

50 10

60 10

70 55

80 25

90 78

100 10

Figure 2 represents the equivalent selections of sensor provider. Table 4 and

Table 6 represent the comparison weight set and corresponding choices about

the SNPs and moreover using for CSP to select the appropriate SNP diverse

weight sets that never alter the consistent effects are illustrates in that

corresponding figure. Where X-axis represents the number of weight set for a

skilled CSP and y-axis represents the corresponding choice about SNP.

Figure 2: Equivalent Selections of Sensor Provider

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100

Eq

uiv

ale

nt

sele

ct o

f

sen

sor

pro

vid

er

Skilled cloud provider weight set


435

The corresponding choice about SNP is 78 in the proposed RIP-WSN-CC

approach for 90 weight set of a qualified CSP. In the same way, the

corresponding choice about SNP during processing the proposed RIP-WSN-CC

system in the presence of different weight set for maximum range of 70 to

produce 55 corresponding choice of SNPs respectively.

8. Comparison Analysis of Existing

with Proposed System

The comparison analysis is mainly established the RIP-WSN-CC flowcharts

and the utilities focused on three aimed functions. The Proposed RIP-WSN-CC

system results are compared with various existing system. Then, the upcoming

section deals with the performance evaluation of the proposed work. Table 5.9

explains about the comparative values of accessible resources of existing system

with the proposed RIP-WSN-CC system. This shows that the existing accessible

resources control based RBAC, TBAC compared with the proposed system that

achieves high available resources.

Table 9: Comparison of Existing with Proposed Accessible Resources

RBAC System Trust Based System RIP-WSN-CC

40 40 42

40 43 45

40 41 42

40 39 41

40 38 41

40 41 44

40 43 45

40 42 45

40 43 44

40 42 44

Simultaneously, the consumer entity conducts resource access requirements to

existing and proposed system. Based on the access control rules, the three

systems can access the authorized user. Hence, the RIP-WSN-CC system

access 20% more efficient than the existing systems. Figure 5.3 shows the

accessible resources of the proposed with existing systems. The X-axis of the

graph represents the retrieving time in terms of seconds and y-axis illustrates the

quantity of resources in terms of numeric. From the graph, it is clearly observed

that there are increases the number of accessible resources that changes in the

existing and proposed systems. In Role-Based Access Control ( RBAC ), the

user entity is constant in the number of accessible resources while in Task-

Based Authorization Control (TBAC ) and RIP-WSN-CC the number changes

with respect to trust degree. Hence, the proposed method achieves higher

accessible resources than the existing system and attain fine granularity.


436

Figure 3: Accessible Resources of Existing with Proposed Work

Table 10 illustrates the comparative analysis of the trust evaluation with respect

to the time. The success rate of cloud computing calculations is compared with

other calculation methods. The efficacious times of the whole quantity of cloud

service proportions are known as the success rate of cloud computing services

in which achieves higher efficient than the existing access controls. Hence, the

proposed system attains 30% more efficient than the existing system. Figure 4

illustrates the trust evaluation between the proposed and the existing system.

The X-axis of the graph represents the time in terms of seconds and Y-axis

illustrates the success rate of cloud services. From the graph, it is analysed that

the RIP-WSN-CC system slightly increased due to increasing the accessing time

and there is a higher variations energetically. If the users have high trust, high

success rate and large number of resource accessible in the cloud service.

Hence, the proposed method achieves higher efficient success rate than the

existing methods.

Table 10: Comparison of Existing with Proposed Trust Evaluation S.No Traditional Access Control Trust Based Access Control RIP-WSN-CC System

1 89 89 92

2 85 89 91

3 80 88 91

4 73 89 91

5 70 89.5 92

6 65 90 92

7 64 89.5 92

8 62 88 91

9 59 85 92

10 50 89 91

Figure 4: Trust Evaluation


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Table 11 illustrates the turnaround efficiency rate of proposed with existing

model such as QoS trust model, combined trust model and first in first out

(FIFO) model. There are totally 10 submission in a sequence and each one

submission have 500 occupations. Then, submit altogether in each submission

concurrently.

Table 11: Comparative Analysis of Turnaround Efficiency S.No QoS Trust Model Combined Trust Model FIFO Model RIP-WSN-CC System

500 0.6 0.49 0.41 0.62

1000 0.6 0.5 0.42 0.62

1500 0.61 0.54 0.41 0.64

2000 0.6 0.56 0.39 0.63

2500 0.61 0.58 0.37 0.63

3000 0.7 0.59 0.35 0.72

3500 0.69 0.6 0.37 0.71

4000 0.73 0.6 0.3 0.75

4500 0.75 0.61 0.34 0.76

5000 0.79 0.61 0.3 0.81

For example, 1 submission with 500 jobs are portioned into four groups such as

QoS trust model, collective trust method, FIFO method and RIP-WSN-CC

method. The FIFO method of each job uses one database server, combined trust

model of each job expenditures double database servers recovers information

beginning together servers, progressions of the information is offered to the

output flat file, QoS model of separate job practices double database servers that

recovers the information from one server and administered data deposited in

other server database.

Figure 5: Turnaround Efficiency

If different database and different number of jobs processing is differentiated by

a submissions. The RIP-WSN-CC of each job uses two database servers. Hence,

the RIP-WSN-CC model outperforms the existing three methods due to increase

the jobs. Therefore, the RIP-WSN-CC system recovers the high turnaround

efficiency than the existing models. Figure 5 illustrates graphical representation

of turnaround efficiency rate of proposed with existing system. The X-axis of

the graph characterizes the number of jobs in submissions besides y-axis


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illustrates turnaround efficiency rate. From the graph, it is clearly observed that

there is increasing in number of jobs with increase the turnaround efficiency

21% more than existing works. The proposed method achieves higher efficiency

than the existing system.

Table 12 illustrates the reliability response of the proposed with existing system.

This section have 10 submissions and each and every submission have a set of

500 jobs such as submission 1= 500 job,submission2= 1000 jobs, and

submission 3 = 1500 jobs and increasing up to 10 submissions. They increased

500 for each stage and the final suggestion achieves 5000 jobs. In this research

work, different SQL queries have run and needs one database server for each

job. But, here infrequent jobs failure due to the limitation of the scalability. If

increased the number of jobs in a system, causes failure in some jobs that

produce time-out is one of the reason. When they write information in flat files

that occurs number of times failure in the job. Therefore, the proposed models is

improved than other model. Figure 6 represents the proposed and existing

systems reliability. The X-axis of the graph represents the number of jobs in

each submission and y-axis illustrates the reliability in terms numeric. From the

graph, it is clearly observed that there is an increase in the jobs ranges from 500

to 5000 with increase in reliability. The proposed method achieves higher

reliable than the existing system. Hence, proposed system achieves 33% more

reliable than the last pre-existing system FIFO model.

Table 12: Comparative Analysis of Reliability S.No QoS Trust Model Combined Trust Model FIFO Model RIP-WSN-CC

500 0.71 0.61 0.41 0.74

1000 0.7 0.61 0.42 0.73

1500 0.78 0.59 0.38 0.81

2000 0.69 0.6 0.39 0.72

2500 0.63 0.7 0.38 0.73

3000 0.72 0.58 0.35 0.73

3500 0.78 0.55 0.4 0.8

4000 0.7 0.51 0.3 0.72

4500 0.89 0.7 0.29 0.91

5000 0.92 0.77 0.27 0.94

Figure 6: Reliability


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Table 13 illustrates the availability of the proposed with existing system. To

evaluate the availability of the trust using input as similar categorization. In this

experiment, randomly trundle the quantity of database servers to measure the

availability among different jobs. If increases the job sizes then increases the

availability. From this analysis, it is evaluated that the proposed method

provides the maximum availability range, when compared to the other systems

such as QoS, trust based and FIFO. The RIP-WSN-CC provides an efficient

outputs, so it attains the best performance results.

Table 13: Comparative Analysis of Availability

S.No QoS Trust Model Combined Trust Model FIFO Model RIP-WSN-CC

500 0.79 0.85 0.7 0.87

1000 0.8 0.8 0.65 0.81

1500 0.9 0.79 0.68 0.92

2000 0.8 0.75 0.58 0.83

2500 0.8 0.7 0.44 0.83

3000 0.8 0.75 0.44 0.83

3500 0.9 0.7 0.46 0.92

4000 0.9 0.76 0.4 0.93

4500 0.89 0.79 0.5 0.91

5000 0.9 0.8 0.4 0.92

Figure 7 illustrates the graphical representation of the availability of the

proposed system. The X-axis of the graph represents number of job sizes in

range (500-5000) and y-axis illustrates the availability range in terms of

percentage (%). From the graph, it is evidently observed that there is a growth

of job size with randomly increase or decrease the availability range of

resources. Hence, the proposed method achieves greater availability resources

than the existing system is the greater advantages.

Figure 7: Availability

Table 14 illustrates the data integrity of the proposed with existing system.

Same submission of job sequence is given to evaluate the integrity of data. The

existing system such as QoS trust, combined trust, and FIFO model results are


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compared to the proposed system. From this analysis, it is evaluated that the

proposed method provides the maximum data integrity range, when compared

to the other systems. The RIP-WSN-CC provides efficient outputs.

Table 14: Comparative Analysis of Data Integrity S.No QoS Trust Model Combined Trust Model FIFO Model RIP-WSN-CC

500 0.58 0.6 0.39 0.63

1000 0.61 0.62 0.4 0.65

1500 0.65 0.6 0.41 0.66

2000 0.78 0.62 0.38 0.8

2500 0.69 0.59 0.39 0.71

3000 0.72 0.55 0.35 0.75

3500 0.76 0.57 0.37 0.78

4000 0.79 0.5 0.3 0.81

4500 0.75 0.6 0.28 0.77

5000 0.8 0.59 0.31 0.82

Figure 8 demonstrates the graphical representation of the data integrity of the

proposed system. The X-axis of the graph represents number of job sizes in

ranges from 500-5000 and y-axis illustrates the data integrity range in terms of

percentage (%). From the graph, it is clearly observed that there is an increase in

the job size with randomly increases the accuracy and consistency of data.

Hence, obtain the maximum integrity in the proposed method achieves greater

advantages than the existing system.

Figure 8: Data Integrity

Table 15 shows the comparative analysis of reputation value of existing system

with the proposed RIP-WSN-CC system. The RIP-WSN-CC system achieves

better performance when compared with the existing system such as FSLR and

RP-CRM systems. Hence, the RIP-WSN-CC system reputation value

calculation increases 0.65 to 0.99. Therefore, it achieves more efficient than the

existing systems.

Table 15: Comparative Analysis of Reputation value

Time (sec) FSLR RP-CRM RIP-WSN-CC

20 0.59 0.63 0.65

40 0.73 0.8 0.83

60 0.9 0.92 0.94

80 0.94 0.95 0.97

100 0.96 0.98 0.99


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Figure 9 shows the reputation value archived of the existing model of

Familiarity and Subjective Logic Based Reputation ( FSLR) , the proposed

model solution of Recommendation and Privacy-Based Cross-layer Reputation

Mechanism ( RP-CRM) and the proposal of Reliable Integrated Platform of

WSN and Cloud Computing (RIP-WSN-CC) systems.

Figure 9: Reputation Value

The X-axis of the graph represents the accessing time in seconds and y-axis

illustrates the reputation values. From the graph, it is clearly observed that there

is an increasing the reputation value that changes the accessing time in existing

and proposed systems. Hence, the proposed method achieves higher ranges than

the existing system.

Table 16 illustrates the comparative analysis of the effective recommendation

value with respect to the time. The recommendation rate of cloud computing

calculations is compared with other calculation methods such as FSLR, SLCRM

and RP-CRM. The efficacious times of the complete figure of recommended

cloud service rate are known as the success rate of cloud computing services.

The effective rate of cloud services achieves higher efficient than the existing

access systems. Hence, the proposed system attains 20% more efficient than the

existing RP-CRM system.

Table 16: Comparative Analysis of Effective Recommendation Rate

Time

(sec) FSLR SLCRM RP-CRM RIP-WSN-CC

20 53 54 71 73

40 58 59 79 80

60 61 62 82 84

80 64 65 87 89

100 65 66 89 91

Figure 10 illustrates the recommendation rate between the proposed and the existing

system. The X-axis of the graph represents the time in terms of seconds and Y-axis

illustrates the ER rate of cloud services in terms of percentage. From the graph, it is clearly

observed that the existing access control success rate methods upsurges based on the time.

Hence, the proposed method achieves higher efficient success rate than the existing

methods.


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Figure 10: Effective Recommendation Rate

Table 17 illustrates the comparative analysis of the MIDR with respect to the

time. The malicious node identification or detection rate of cloud computing

calculations are compared with other calculation methods such as reliable

recommendation and confidentiality based Cross-layer Reputation Mechanism

(CRM) systems and these two CRM systems include the Recommendation and

Privacy-based Cross-layer Reputation Mechanism (RP-CRM) and Subjective

Logic-Based Cross-layer Reputation Mechanism (SL-CRM). The MIDR rate of

proposed cloud services achieves higher efficient than the existing access

controls. Hence, the proposed system attains 20% more efficient than the

existing RP-CRM system.

Table 17: Comparative Analysis of Malicious Node Detection Rate

Time (sec) FSLR SLCRM RP-CRM RIP-WSN-CC

20 22 21 23 25

40 44 43 46 47

60 66 64 67 69

80 80 78 81 82

100 85 85 86 88

Figure 11 illustrates the MID rate between the proposed and the existing

system. The X-axis of the graph represents the time in terms of seconds and Y-

axis illustrates the MID rate of cloud services in terms of percentages. From the

graph, it is clearly observed that the MID rate of outdated access control

methods increases as time increases, while proposed system estimation

technique there is an advanced and variations energetically. Entities with a

complex MID rate of cloud services. Hence, the proposed method achieves

higher efficient MID rate than the existing methods.

Figure 11: Malicious Node Detection Rate


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Figure 11 Malicious Node Detection RateTable 18 illustrates the comparative

analysis of the Malicious Node Isolation Rate (MIOR) with respect to the time.

The MIO rate of cloud computing calculations are compared with other

calculation methods such as FSLR, SLCRM and RP-CRM. The MIOR rate is

an identical effect that does not varies while increasing the accessing time. If the

MIOR is low then the system achieves greater efficacy. Hence, the proposed

system attains more efficient than the existing system.

Table 18: Comparative Analysis of Malicious Node Isolation Rate

Time (sec) FSLR SLCRM RP-CRM RIP-WSN-CC

20 68 58 54 52

40 68 58 54 52

60 68 58 54 52

80 68 58 54 52

100 68 58 54 52

Figure 12 illustrates the malicious node isolation rate between the proposed and

the existing system. The X-axis of the graph represents the time in terms of

seconds and Y-axis illustrates MIOR cloud services in terms of percentage.

From the graph, it is clearly observed that the MIO rate of outdated access

control methods linear as time increasing on, while proposed system estimation

method there is lower and deviate vigorously. Hence, the proposed method

achieves lower efficient MIOR rate than the existing methods.

Figure 12: Malicious Node Isolation Rate

9. Result/Findings

In this work, the CC-WSN integration which is based on the RIP-WSN-CC

system is tested by different performance measures. The main intention of this

research is to improve the security of the authentic CSP and SNP, calculating

trust and reputation and reduced the cost of services. For this purpose, trust and

reputation based CC-WSN integration system is proposed in this work. Hence,

numerous parameters such as accessible resources, truth evaluation, turnaround

efficiency, reliability, availability, data integrity, reliability, availability, MIDR,

MIOR and ERR are utilized to estimate the RIP-WSN-CC technique

performance. The proposed methods attain better security and execution time

than the existing RBAC, TBAC, FIFO, QoS model. In this evaluation, it is


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proved that the proposed RIP-WSN-CC system outperforms than the existing

models.

10. Conclusion

The research has been accomplishments few research gaps as highlights on the

importance roles of wireless sensors networks and cloud computing

infrastructures on the developments of smart IoT solutions in different domains.

Also, highlights on the different aspects of the requirements of the delivered

data quality to the cloud users from sensors connected cloud architectures and

the limitations addresses in the literature about that. Continue with defining

clearly the different services involve on wireless sensing and cloud computing

mathematically with equations of the parameters according to end-user

preferences of the quality of the delivered data. Also, defining the mathematical

model of connecting these service for integration platform of the both WSN-CC

infrastructures. Implementations of the proposed integrated platform for most

reliable integration in cloud-based Java environments with all the server

services required as https, SQL and Apache server. Evaluation of the proposal

in small and large entities of users and services providers of WSN and CC and

analyze its performance in comparison with the nearest existing access control

techniques in the literature. The contributions of this research works can be

clearly illustrated as defining most reliable integrated platform solution of both

of CC-WSN infrastructure by thoroughly investigating the services provided by

each providers and match them with the requirements of the end users to

determine the most reliable integration satisfying end-user enquires. The

proposed method modeled mathematically, implemented in cloud-based

environment as proof-of-concept and analyzed in different analysis assumptions

to show its superiority to other existing methods in the literature.

11. Research Limitations

This research is handled as proof-of-concept purpose meaning that it cannot be

claimed that all the services offered by cloud computing and wireless sensing

providers are considered. Moreover, the implementation is based on cloud-java

environments implemented in single desktop with the support of group of

server’s services from Microsoft WAMP Server. Also, it is worthy to mention

that for the sake of the simplicity for the theoretical analysis some of the

services’ parameters are set with assumed values and the analysis been carried-

out in case of pre-defined small entities of services providers and users’

scenarios.

12. Future Researches

The extension of this research can be considered in some aspects as to leverage

the implementation to be in real world cloud computing connected with

physical wireless sensors networks infrastructure. Also, different domains of

implementation as maybe in medical and health fields can be considered. It is


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expected also that more services to be considered based on the uniqueness of

the implementation scenarios in certain domains also, the analysis can be

carried out with end-user real requirements of the data delivery quality. All

these points can shift the proposed method to be implemented from proof-of-

concept level to the level of real cloud computing connected wireless sensing

networks for future smart IoT applications.

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