INS653X Project Management.docx

INS653X Project Management
Answer:
Introduction
In this digital world, internet networks and emerging technologies are proliferating,
enabling companies to interconnect multiple systems easily and manage business problems.
IoT is an abbreviation for the internet of things an emerging technology that helps business
communities to propose significant communication platforms and link various devices,
networks and servers through a single system (Xiao, Sidhu, and Christianson, 2015). The
prominent feature of IoT technology is that it provides communication networks and the
ecosystem to exchange data without requiring human assistance so that remote access
controls can be developed within businesses. Using IoT-enabled devices, it is easy for the
users to exchange data and receive real-time information by which complexity and
difficulties can be managed. Business processes, devices, networks and systems can be
connected with the IoT technology and remote accessibility can be provided that can enable
the companies to perform business operations remotely (Abdullayeva, 2016). In terms of
speed, IoT is capable to provide a faster data transmission speed that enables users to
exchange data in less time and receive proper updates from companies.
However, IoT is connected with computing networks and communication channels that can
increase security threats and hacking problems. The larger datasets can be shared over
internet networks and hackers can target IoT networks easily to produce data breach
incidents (Tormo, Mármol, and Pérez, 2015). Due to security concerns, it is challenging for
the companies and users to build trust regarding the implementation of IoT which
negatively reduces the reputation of the IoT networks. The existence of security risks and
any vulnerabilities in the IoT systems will always affect the privacy of sensitive data.
Companies can suffer from data breaches and ethical problems and influence the trust of
the users this is based on what happened to AT&T when they were slammed with a fine of
$25million by FCC due to a data breach.
It is a challenging task for evaluating the trustworthiness of the entities on the internet
network and also increases hacking and cyber-attacks (Ahmed, et al., 2019). The primary
motivation behind this research is that companies are moving toward IoT networks for
which it is essential to review and manage the security and trust-related problems. The

companies need to focus on security and confidentiality and follow proper configuration
processes so that malicious codes and unauthorized activities can be minimized and trust
levels can be improved. Kokoris, et al., (2016) defined trust management as a process of
managing security risks and issues from IoT networks to protect personal details and
computing systems. Therefore these models are highly recommended to the cyber security
area for enhancing the various approaches which are beneficiary for users.
This research will provide more information on network sharing and methods for
evaluating trust and reputation in IoT devices. Also, it enhances the security of those
addresses that have concerns relating to both security and privacy. In this research, the
concept of IoT technology will be reviewed and the implication of security on trust and
reputation in IoT will be analyzed (Yan, Zhang, and Vasilakos, 2014). There are many works
which have been done in this instance to gain in-depth insight into trust and reputation
methods used for IoT networks. Previous findings will be reviewed to manage research
gaps so that we can provide better results for the IoT environment for many
organisations. The presence of cyber threats and insider vulnerabilities in the IoT networks
can affect communication channels and the security of the transmitted data can be reduced.
According to Kravari and Bassiliades (2019), more than 70% of the companies collect
customer data using IoT-based communication networks and store it in databases that can
be targeted by hackers easily. Kowshalya and Valarmathi, (2017) presented a trust
management program that shows that building security and data protection programs can
increase complexity in businesses. It is difficult for the companies to deal with the security
and privacy concerns due to which trust and reputation can be influenced and data
breaches or ethical issues can be posed easily.
When looking at the term "trust", can be defined using different ways that have derived
from different disciplines under a different lens, which depend largely on the field being
perceived. Through such approaches (Abdalzaher et al., 2019), it will be relatively easy to
find the connection that exists when considering trust in IoT. The field of view on the trust
includes the following:
Philosophy – in this, trust is based on reliance since it can be projected on the aspects of
betrayal happening in the process. Meanwhile, reliance is checked based on disappointment
caused (Azzedin, and Ghaleb, 2019).
Psychology – here trust is based on the beliefs of how different situation needs to be
handled when looking at people's background and community.
Network and communication – capture the trust of the user to establish the relationship
built from different sets surrounding scalability and reliability (Saied, et al., 2013).
In IoT technology, trust is connected with the security and confidentiality of personal
details as system security and privacy are important for building trust among customers.
Mainly, companies collect data from the users through IoT-enabled devices and store it in
databases where hacking problems and risks can affect the privacy of collected data and

reduce trust from users easily (Asiri, and Miri, 2016). Trust is based on the security and
confidentiality of sensitive data and companies need to protect the data of users. Companies
need to protect users' data from cyber-criminals while accessing IoT networks to improve
trust and reputation. The social networking model mainly follows three parameters for
evaluating trust and reputation: honesty, community interest and cooperativeness. So, this
dynamic model is capable to find the best parameters of trust and maximize the
effectiveness of IoT networks. This research will review various methods and approaches
used for trust and reputation. Moreover, effective recommendations and taxonomy will be
developed to increase the security and confidentiality of IoT networks and will also improve
trust levels while accessing IoT networks (Rizvi et al., 2018).
Aims And Objectives Of The Research
The primary focus of this research is based on reviewing the trust and reputation modelling
in IoT. A trust framework is designed to counteract a certain type of threat. Each company
has its own unique set of risks and vulnerabilities that can be discovered by doing a data
flow analysis based on use cases. A threat profile, at its core, reveals the kind and
motivations of potential attackers.
Security strength is evaluated by a trust model, which then determines a trustworthiness
score. When evaluating the safety of cloud services, it is important to do so along several
dimensions, and these are all included in a trust value. The security of service and the
reliability of the model can be evaluated using the CSA (Cloud Security Alliance) service
challenges.
The following are objectives developed:
Understanding the enhanced concept of trust models to understand the concept of IoT
To review the challenges to trust and reputation in IoT technology
To evaluate various methods or approaches of trust and reputation for IoT
Research Questions
Below are some of the outline research questions:
What are the characteristics of IoT technology?
What are involved risk factors and security concerns affecting trust and reputation in IoT?
What are the methods and approaches used for evaluating trust and reputation in IoT
networks?
Structure Of The Dissertation
The format of this research contains various sections and chapters including an
introduction, review of the literature, methodology, results and discussion and finally a
conclusion. The introduction section will help to gain a brief overview of topics placed in

this research and propose the aim and objectives. The literature review has a major
contribution when looking at different studies and research since it helps to review the
previous findings and obtain effective information for addressing research questions and
gaps. A research methodology is beneficial for developing effective research plans and
frameworks to complete the study with larger quality and effectiveness. Results and
discussion sections are reliable and help to review the collected data from the literature
review for gaining effective findings and results. The conclusion section will provide a
summary of the entire research along with the recommendations and future direction.
Background And Literature Review
IoT uses wireless sensor networks which are capable to connect numerous computing
devices and systems easily and perform communication-related activities without human
assistance. In this generation, companies are shifting towards digital technologies to
increase operational performance and IoT is a common technology implemented in
businesses to propose remote access controls and perform business operations
remotely (Alofi, Bahsoon, and Hendley, 2021). IoT is an effective technology that enables
companies to deal with complexity and data sharing problems so that real-time updates can
be received and problems can be resolved significantly.
However, IoT is connected with the communication networks and internet servers which
are not capable to manage security concerns and exchanged data can be accessed by the
cyber-criminals the cyber-criminals can access data. These researchers (Duan, et al., 2014)
have identified that security risks and threats enable the hackers to target communication
systems and databases of the companies and IoT networks are less able to defend against
cyber-attacks and criminal activities. It is challenging for companies to deal with the
security threats and vulnerabilities due to which trust and reputation-related problems can
be increased (Arabsorkhi, Haghighi, and Ghorbanloo, 2016). Hackers can access the
collected data from customers and IoT networks can be linked with the malicious codes by
which remote accessibility can be proposed and confidentiality of sensitive data can be
decreased. It is difficult for companies to identify and review the security risks associated
with the IoT networks and suffer from ethical and trust-related problems.
Introduction
Review of literature when conducting research acts as a major advantage and contribution
while conducting research. Since it helps to gain effective information related to the
research topic by reviewing the previous studies and papers. In this research, a literature
review is selected while looking at car sharing systems functionality, to understand the
challenges and problems associated with trust and reputation. To select papers, various
keywords are included such as security issues in IoT, trust models for IoT, trust and
reputation issues in IoT, trust approaches and taxonomy for IoT and many more. This
section will cover various points such as the key concept of IoT, challenges related to trust

and reputation, and trust and reputation methods.
BTRM-WSN is a bio-inspired trust evaluation architecture for wireless sensor nodes, whose
unique feature is the precise use of an ant colony system to assist a node in locating the
most trusted sensor offering a specific service, as well as the most reputable method to
access that sensor. To accomplish this, ants are dispatched and dispersed throughout the
system, leaving pheromone traces in their wake that will aid future ants in following the
correct path.
Characteristics Of IoT
Azzedin and Ghaleb, (2019) reported that IoT is an advanced communication technology
that connects computing devices, networks and servers through a single system. IoT-
enabled devices allow companies and users to access computing devices and
communication systems from any location. In this digital world, companies are accessing
emerging technologies and digital systems where IoT helps the companies to build effective
communication infrastructure and exchange data over multiple networks at a time. Baqa, et
al., (2018) agreed and stated that IoT is an effective technology that interconnects
tremendous devices and systems together without requiring human assistance so that the
level of communication can be improved. In the context of businesses, IoT plays a major
contribution in receiving real-time information and enables customers to address queries
and problems in less time (Ma, Liu, and Meng, 2020). Connectivity is one of the major
characteristics of IoT technology that provides a platform to interconnect the number of
computing systems, networks and devices to perform data transmission-related activities.
Figure 2.1: Characteristics of the Internet of Things (Bennaceur et al., 2021)
The extraction of knowledge from the developed data is significant for companies where
business intelligence and identification of business problems can be managed easily.
Bennaceur et al., (2021) reported that scalability is a major feature of IoT technology that
enables companies to handle massive data and manage data storing-related concerns. IoT is
now connected with advanced technologies such as artificial intelligence, cloud computing
as well as big data analytics. Using such features, it is easy for companies to develop remote
access controls and automated systems so that business operations and communication-
related activities can be done remotely (Mendoza, and Kleinschmidt, 2015). In future,
companies will be dependent on IoT networks and will build effective digital systems and
communication infrastructures.
Challenges Related To Trust And Reputation
Fernandez, et al., (2017) reported that IoT provides a system to connect networks, servers,

devices and machines and process communication-related activities. Using IoT-enabled
devices, higher data communication networks can be developed for increasing the speed of
data transmission and also enables the companies to receive real-time updates. However,
IoT is less capable to manage insider threats and security vulnerabilities experienced by
companies and users through cyber-attacks and criminal incidents. Fortino, et al., (2020)
agreed and stated that cyber-criminals have the potential to develop malicious programs
and target IoT-based communication networks to enter into the servers and perform
hacking activities.
Figure 2.2: Trust and reputation in IoT (Jabeen, et al., 2018)
It is challenging to evaluate the trustworthiness of entities on the IoT networks due to
which confidentiality of sensitive data can be affected easily. The term trust is mainly
associated with security as system privacy is necessary to obtain trust and IoT is less able to
manage security issues that can influence trust and reputation negatively. Jabeen, et al.,
(2018) reported that trust management plays a major role in the context of IoT security,
allowing effective data mining and fusion for delivering qualified facilities.
Major three risk factors are identified as increasing security-based trust and reptation
problems in IoT networks such as lack of understanding, improper security measures and
misconfiguration. Janiszewski, (2020) agreed and observed that lack of awareness is a
major factor among users that increase insider threats and enables hackers to perform
unauthorized activities. In order to manage such risk, a trust management model is
developed that enables the companies to deal with the understanding problems and
increase the security level. More than 60% of the companies have implemented IoT
networks, most of them are found in silicon valley and included simple security programs
which are less capable to control cyber-threats and malicious activities due to which trust
regarding the confidentiality of data can be affected (Ma, Liu, and Meng, 2020).
Neisse, et al., (2015) identified that in this digital environment, IoT networks are vulnerable
to security risks as incomplete or distorted information can be shared by hackers that can
affect trust and reputation. Implementing secured communication infrastructure in IoT
devices can help companies control and reduce trust-based security issues. Companies need
to develop proper trusting and reputation models to manage security and trust problems
and increase the privacy of database systems from cyber-attacks. The previous studies did
not compare the trust and reputation models used for the IoT networks. It is challenging to
select a suitable model for improving the trust and privacy of the IoT networks and systems.
This research focuses on the specific trust models and frameworks and the taxonomy of
trust models and protocols created to deal with the trust and reputation problems in IoT.
Trust As A Taxonomy In IoT

When standard security paradigms can indeed be enforced owing to a lack of centralized
authority and an insufficient understanding of the environment, trust management is
considered a promising solution to promote collaboration between entities. However, there
is a dearth of workable approaches in the existing literature for collecting and
disseminating information for efficient trust evaluation and for providing users with trust
data. This could make it so that people have a hard time grasping and accepting a trust
management solution. In order to build and develop a trust management platform that can
be readily accepted by users before practical implementation, this study presents users-
driven trust modelling & management strategy. To prove the efficacy of this approach, we
show how it may be implemented in the creation of a reputation system for a mobile
application.
A thematic taxonomy is proposed for the trust and reputation in IoT networks to deal with
the trust-related challenges (Copigneaux, 2014). The thematic taxonomy is developed
relying on major seven factors including roles based on trust entities, trust management
level, trust properties, trust computation, trust metrics, trust attacks and many more (Li,
White, and Clarke, 2021). Such factors are identified to represent a trust-based IoT
environment that helps review and evaluate the challenges and risks associated with IoT
security and trust.
Figure 2.3: Taxonomy of Trust in IoT (Jabeen, et al., 2018)
A) Trust Metrics
The major aim of trust metrics is to examine standards and security patterns to evaluate
and measure trust among IoT networks (Nitti, Girau, and Atzori, 2013). A suitable trust
management system mainly relies on the best trust metrics that can help for monitoring the
interactive facilities among IoT entities (Lingda, et al., 2021). Major two types of metrics are
included such as social trust metrics and QoS trust metrics. QoS metrics are more effective
and reliable and can help provide quality services to the users and the proper security so
that trust can be improved. Modelling and understanding trust is made possible by trust
measures. They have intimate ties to established structures of trust. PGP is an example of a
program that uses a binary trust metric. Trust metrics in computing history can be traced
back to early examples such as eBay's Feedback Rating.
B) Trust Metrics On Reputation And Recommendation
Reputation is considered both in the social side's product and process section. The social
section is deemed to be a product since the entity of opinion arises from it. It also influences
how information flows within the Social Internet of Things (SIoT). Examples of this include

those developed in firms like eBay as well as the Keynote.
A centralized authority of trust is used to help maintain the feedback that is used for the
rating process. When looking at the same process in distributed systems, reputation is built
from what has been there for a certain number of times and comes from both behaviour and
customers (Urena, et al., 2019). For smooth operation, it requires the system to apply a
heuristic algorithm that does the update and integration process.
Through the above process is relatively easy for decisions to be made by humans as
compared to a system for a recommendation. Since reputation and recommendation can be
done much faster while relying on the existing surrounding. Through this, it is best for a
reputation system to be developed, which will handle the trust service platform. When
handling the trust service platform, the following modules are considered to be the most
important section (Bao, Chen, and Guo, 2013):
The feedback mechanism is also known as Reputation Measurement and Evaluation.
Propagation
Maintenance
While handling the SIoT the following components are needed during the development
process since they form the basic components:
Trust Analysis and Management – this module plays a key role in ensuring that the system
has been implemented based on the needed desire (Sicari, et al., 2015). This part normally
appears in many systems that are closely related to the trust mechanism.
Trust Broker – this section is the one responsible for providing trusted knowledge when
handling different applications and services that are operating within SIoT (Truong, et al.,
2016). Information needed must be registered previously as a requirement before using any
trust service platform.
Trust Agent – this component is known for collecting data related to trust ranging from
physical to domains found on social SIoT (Tr?ek, 2018). The data collected can fall under
opinion as an entity that contains recommendations or even feedback from other
applications.
C) Entity Role
In the context of IoT networks, trust relation contains two entities such as trustee and
trustor which are dependent on every other for sharing the common interest in the
communication networks. The trustor requires to have confidence in the trustees in regards
to honesty, beliefs and benevolence.

D) Trust Properties
Trust properties are mainly derived from the social science to the cyber-attacks and contain
various properties for IoT security and trust such as asymmetry, subjectivity, partial
transitivity and context sensitivity. Jabeen, et al., (2018) reported that context sensitivity is
one of the major trust properties for the IoT networks that need to be included in the
communication infrastructure. Trust properties are mainly used for evaluating and
reviewing the security-related concerns from IoT networks and building strong
communication programs so that trust levels can be improved.
Properties To Be Considered When Looking At Trust
A) Propagative
This section of trust has been studied by many researchers when handling computing
features in IoT. According to Chen, Guo, and Bao (2014), this property is useful in assessing
risk from schemes used in entity recognition and assessment system being built. The
entities of the system are deemed extensive mostly on trust, and recommendation, and have
ad hoc networks normally use short paths when connecting the entities.
B) Dynamic
According to Li, White, and Clarke (2021), this property looks at changes that normally
happen when different entities interact based on the trust existing. Since it can be decreased
or increased depending on the experiences gained in the process of interaction. Different
measures have been placed in computing to determine ways through which measurement
and quality of dynamics can be placed into consideration when handling computers. These
different mechanisms are placed in terms of rewards and penalties whenever interaction or
transaction occurs.
C) Subjective
Trust is more subjective and relies mostly on the interaction that has been conducted
between different parties (Copigneaux, 2014). The reason for this is that various users have
their way of perceiving what is trustworthy. Opinion and reasoning may vary based on the
activity being done at the end of a particular event.
D) Context-Dependent
This property mostly relies on the trust score being issued that depends on the context of
interaction. The best example, in this case, can be on solving two problems existing within a
farm. Where one is a prediction of yield and the other problem is detecting diseases that are
likely to break. In this, one company can be trusted to handle the production yield but not
the detection of disease.

E) Trust Applications
It is demonstrated that trust applications can be applied over IoT networks to manage trust-
related issues. Major two types of applications are included in the IoT trust such as service
management and security mechanism. The trust-based security mechanism is more
effective and beneficial that developing effective security programs and plans to deal with
the insider threats and vulnerabilities from IoT networks and increase trust levels (Saied, et
al., 2013). On the other hand, trust-based service management delivers effective services to
the IoT technology for increasing the alignment of IoT efforts with smart devices.
F) Trust Management Levels
Trust management acts as a major contribution in the IoT that helps to control and manage
the factors affecting trust and reputation in wireless communication networks. Trust
management goals can be achieved at various levels that include data perception, identity
trust, the transmission of data, and preservation of privacy and users and
applications (Azzedin, and Ghaleb, 2019), for which data perception and privacy
preservations are major levels that need to be covered in trust management so that privacy
and confidentiality can be improved.
G) Trust Computation Scheme
Trust computation is a kind of process which is mainly included for extracting
trustworthiness information in regards to the IoT entities (Chen, Guo, and Bao, 2014). It is
mainly divided into various sections such as the composition of trust, propagation, trust
update, formation and trust aggregation. Trust formation and updating both are major
schemes used in the IoT networks due to their capability to build trust among users by
updating security programs and providing quality services through IoT networks.
H) Trust Attacks
Trust attacks are mainly performed by cyber-criminals based on malicious programs and
target communication channels linked with the IoT networks. Using security threats and
attacks, it is easy for cyber-criminals to perform data breaches and criminal activities over
IoT networks (Azzedin, and Ghaleb, 2019). Trust attacks are mainly divided into three
categories such as biased recommendation, inconsistent behaviour and identity attacks. It is
found that identity attacks are more common and easily performed by hackers over IoT
networks and provide easy accessibility of the communication channels and database
systems.
Methodology: Introduction

Research methodology has a major contribution to the research that can manage the
research problems and focus on the research aims and objectives. The development of
effective methodologies can lead to increase research quality and provide better ways to
address research gaps (Urena, et al., 2019). The methodology section intends to increase
understanding and knowledge in the field of IoT and trust and reputation modelling.
Numerous research methods are selected for this project such as research design, research
approach, philosophies, data collection and many more.
Since the introduction of the internet, both the quantity and variety of information sources
have expanded at an exponential rate. These days, knowledge is both a commodity and a
necessity in the decision-making process. It is common practice for businesses to share
some of the data they already have in order to obtain additional data that could be useful to
their operations. This data may be sensitive, so businesses need to know if they can have
faith in the partners they're sharing it with. To address this predicament, various trust
models were developed.
To examine the dynamics of relationships, trust models apply rational principles. Whenever
a computer operating a trust model interacts with another agent, it evaluates that agent to
establish its level of trustworthiness. All interactions between agents are governed by this
single value of trust. At the same time, values below that level are viewed with scepticism.
The problem is these trust models are so broad and different from one another that it's hard
to tell which ones tackle which aspects of the trust idea. This study suggests a set of factors
to be used in evaluating different security models in order to discover the problems that are
solved by particular models. This set of requirements can be broken down into four broad
classes. Limitations on space mean only one of these criteria may be examined in depth,
followed by an example study of a trust model to show how these criteria are applied in
practice.
Research Philosophy
Research philosophy is mainly used in the studies to create effective frameworks to address
research problems and gain depth information regarding research themes (Ureña, et al.,
2019). Two major types of philosophies are included in the studies: positivism and
interpretivism. Positive philosophy is capable to provide observational research based on
the research objectives and requires developing and testing the research hypotheses. As
opposed, interpretivism is a branch of Epistemology capable of focusing on the research
problems and developing effective data-gathering ways to address the identified problems
and issues (Bao, Chen, and Guo, 2013). An interpretivism philosophy aims to provide in-
depth analysis in regards to the research topic by providing observational research.
In this research, an interpretivism philosophy is selected as it can propose effective
approaches and plans to gain in-depth insight into research questions. Using such a

philosophy, there is no need to develop research hypotheses and testing processes by which
complexity can be reduced and objectives can be achieved effectively.
Research Approach
A research approach is reliable and beneficial for the dissertation that has the capability can
develop significant research plans and outlines based on the objectives and research
themes. It is capable to propose significant data collection platforms for the research so that
depth information can be obtained and research questions can be addressed in less
time (G?owacka, Krygier, and Amanowicz, 2015).
Major two types of approaches are included in the studies such as inductive and deductive.
Inductive Approach: The inductive approach is also defined as inductive reasoning that
begins with the observational process and theories are developed toward the research
questions(Ureña, et al., 2019).
Deductive Approach: On the other side, the deductive approach is based on the
generalization reasoning of the research topic that requires proposal hypotheses and
provides limited information in the research.
This research followed an inductive approach due to its ability to provide significant ideas
and observational programs to develop reliable plans and outlines so that research can be
done in the right direction. Using such an approach, it is easy for achieving the developed
goals and objectives and obtain effective findings.
Research Design
Truong, et al., (2016) reported that research design is one of the important methodologies
used to outline the research structure. By using the investigation design, the study can get
the right way to comprehend the investigation issue to obtain valuable and proper data. It
also helps the scholar to propose an outline of the process of investigating the study and
permits them to resolve the research problems and concerns in less time. It is found that
different types of designs could be added to research for example quantitative and
qualitative (Sicari, et al., 2015). In this manner, quantitative design is beneficial to focus on
gaining numerical info with the help of statistical techniques. On the other side, qualitative
design can allow the researcher for developing opinions and gain theoretical information. It
also delivers the base for quantitative design and supports the data collection methods.
A qualitative design is followed in this research as it is beneficial for gaining in-depth
observation and proposing effective data collection processes. Using qualitative research, it
is easy to manage and address the research questions and research can be completed within
time with significant findings. The design can be based on the below approaches.

Fuzzy Technique
Traditional authentication techniques are ineffective in distributed and dynamic IoT
environments, when identities are unknown in advance. The gadgets end up sharing
information and applications with people they know and trust. After that, a Fuzzy system
for Trust-Based Access Control is introduced (FTBAC). In FTBAC, trust is calculated by
taking into account three factors: experience, expertise, and suggestion. The trust ratings
are then linked to privileges, access requests, and credentials to create confirmation of
granting or not granting accessibility.
Device Layer, Requesting Layer, as well as Access Control Layer, are the three layers that
make up FTBAC. Both Smart devices and our interactions are included in the first layer. The
second level gets data on experience, competence, and recommendations before calculating
a fuzzy trust rating. Following the principle of least privilege, the third approach is
considered in the decision mechanism and correlates the computed fuzzy trust value and
access privileges. The simulation findings show that FTBAC is both flexible and scalable, as
well as energy-efficient. While managing accessibility through a mechanism based on
cryptographic security improves the level of confidence, it comes at the cost to minimize the
time.
A fuzzy way of calculating trust is presented. There are three layers to it: a sensing layer, a
core network, and a network protocol. Users view the IoT ecosystem to be a Service
Provider, as well as trust management enables the IoT to give better-qualified services to
every Service Requester. Retrieval, distribution, and decision-making are all parts of this
trust model. It also creates security procedures depending on a judgment function
depending on the customer's trust score. Unless the customer's credential complies with
these security criteria can the client access the IoT.
The trust model is employed to maintain user security by connecting location-aware,
identity-aware, and authentication records. Whenever a user requests a service, They
receive a trust value. There seem to be three methods of verification, one per trust value.
Clients must supply biometric data if the trust value is low. If they have a medium level of
trust. The goal is to use a fuzzy process to gain a categorization of the operations to assess
the responsiveness of the provided data.
Social Networking
Because smart technologies are human-related gadgets, they are typically found in public
spaces and connect via wireless channels, making them vulnerable to malicious assaults.
Smart objects are frequently heterogeneous and must collaborate to perform a task. There
are three types of social connections: friendship, ownership, as well as the community.
As a form of self-promotion, malicious nodes carry out trust-related threats to disrupt the

normal operation of IoT. Another decentralized, encounter-based, as well as activity-based
trust management mechanism for IoT has been suggested. Network entities that
communicate with one other, in particular, produce a trust evaluation and then trade that
with other nodes, resulting in an implicit rate that appears to be a suggestion.
Cooperativeness and communal interest are the criteria used to calculate trust. As a result,
even if the settings vary continuously, this dynamic protocol can discover the appropriate
trust parameter to enhance the application's effectiveness.
Introduce a completely secure overlay network in the context of social media networks.
Because it is centred on continuous peer-to-peer contacts and community development, it
guarantees quick, simple, and secured access to the Internet. Every gadget and community
has a unique identity that influences the trustworthiness of other nodes based on their
actions. Physical closeness, satisfaction, clarity of answer, seniority in the trusted channel,
common aims, justifications, and history of engagement are some of the characteristics to
consider. Chains of credibility will enable the creation of groupings and distinctive identities
to gain accessibility and disseminate related data. When users connect to the network, they
do so via crossing the trust chain created by nodes.
Cooperative Approach
To recognize the malicious activity of surrounding nodes, a hierarchy trust model for IoT is
being introduced. To track object-reader interactions, t Verifiable Caching Interaction Digest
(VCID) is used. The study also includes a long-term reputational strategy for managing
organizational trust.
In a pervasive Computing architecture, a decentralized strategy manages collaboration.
Specifically, creating a trust management system with IoT that may assess a node's trust
based on its previous behaviour. The model calculates the trust value based on both direct
and indirect observations from surrounding nodes.
For distributed networking in the Internet of Things, an attack-resistant trust management
paradigm has been developed. This approach determines and propagates reputations in
distributed routing architectures, allowing for the establishment of dependable trust
relations of self-organized units and the avoidance of dangerous attacks. Evidence synthesis
and the Bayes algorithm are used to calculate the suggested trust and historical statistical
trust.
Research Strategy
The research strategy is helpful for the research project that provides a platform to assess
the data assembly ways by which investigation can be completed reliably. the development
of an effective strategy can lead to increase research quality and effective outcomes can be
obtained (Truong, et al., 2016). There are various strategies included in the studies for

example surveys, focus groups, literature reviews, case studies and many more.
In this research project, a literature review based on the research strategy is included due
to its capability to reduce challenges and obtain effective information. The literature review
intends to gain depth insight into IoT and trust-related issues by addressing the research
gaps and problems. In this manner, various resources are included such as peer-reviewed
papers, online websites, books, journal papers and many more. In the literature review,
more than 50 research papers have been reviewed that focus on the trust and reputation-
related problems associated with IoT technology. In terms of effectiveness, the literature
review is more effective and reliable as that delivers a platform to propose effective data
collection approaches so that research questions can be addressed in less time. It also
supports qualitative research design and helps maintain the research quality by reducing
research gaps significantly.
Data Collection
The data collection method plays a significant part when looking at research methodology
that can deal with the research questions by proposing effective data gathering approaches
and techniques. The selection of a data collection technique is based on the research theme
and developed questions. Major primary and secondary methods are included in the studies
for collecting data (Truong, et al., 2016).
In this manner, primary data is mainly gathered from fresh resources including interviews,
surveys and many more and support quantitative research. On the other hand, secondary
data is gathered using literature review, case studies and observations that mainly support
qualitative research. Using primary research, the complexity and difficulties can be
increased that require conducting either survey or interview for collecting data from the
participants. However, secondary is an effective way to obtain data in less time and manage
the research problems.
In this research project, a secondary data collection technique is added as it is capable to
provide effective that can provide in-depth insight into IoT and trust-related issues. Using
literature review, it is easy to collect secondary data and address research questions in less
time. Therefore, data collection is a beneficial methodology for achieving research
objectives and gathering related data easily.
Analysis Of Data
Data analysis plays a major role in the research, enabling the researchers to review and
analyze the obtained data and propose effective findings. A data analysis technique aims to
gain significant results or outcomes and address the developed questions effectively.
Different techniques are included in the data analysis such as statistical, content analysis
and thematic (Tr?ek, 2018). In this research, descriptive content analysis is included due to

its ability to manage the research gaps and analyze the collected data sufficiently to obtain
reliable results. In terms of complexity, content analysis is less complex and difficult which
helps to review the literature review and obtain effective points and facts in regards to the
research topic. Therefore, it is stated that all these are reliable and helpful methodologies
used in the project that can manage research problems and proper effective plans to
address research questions and achieve aims and objectives.
Trust Evaluation Based On Complexity Provided By IoT Services
IoT application normally uses different approaches when it comes to how information and
data are evaluated within a given system. Since one approach cannot be fulfilling enough to
calculate matters arising locally or even those that need to be handled remotely.
Centralized
According to Kravari and Bassiliades, (2019), this approach uses a centralised platform that
can be accessed by nodes found within its domain that relies on the request and services
trusted. The platform is the one managing all the trust information like negotiations,
calculations, and the making of the decision. Also, it provides the user with all the
information that will be used in making all the necessary trust evaluations. This means that
what has been rated applies to the whole rating system since it is treated as global. Areas,
where centralization plays a key role, include electronic marketing as well as social
networks with companies like Facebook, Amazon, and e-bay taking their chances.
Distributed
This evaluation method works through the cooperation of nodes as they calculate trust
locally through conducting observation and exchange of reports with their neighbouring
nodes (Guo, and Chen, 2015). An example is when the node considered as the trustor
estimates the final value from the trustee through comparison with the report it has as the
trustor node. This is done also to other global peers. In cases where direct is not available,
validation of data plays a key role in identifying information on other sections of the
domain.
Decentralized
This trust mechanism acts as an alternative to the existing model where it combines both
architectures of centralized and distributed making it to be more reliable. Since it contains
added points based on what the two designs had. This is evident through the capability of a
centralized model being able to evaluate trust information and place it where its agency
matters the most. While distribute approach is capable of handling trust computation
regularly (Kounelis, et al., 2014). The major concern is the application of trust authorities
that will help in evaluating information when this is applied on large scale.

Ethical Considerations
Conducting research requires focusing on the ethical issues and problems of the study. This
research project developed an ethical approval form and submitted it to the university
based on the provided guidelines to deal with the ethical challenges. In order to manage
security concerns, data protection laws and regulations are reviewed and followed which
helps to reduce ethical and unauthorized problems from the research. Only authentic
sources are included in the data collection that helped to obtain data with lesser ethical
concerns and risks. In the conducted literature review, various research papers and articles
were analyzed and in-text citations were added in order to provide credit to the
writers (Tr?ek, 2018). Moreover, a reference list is added to the research by which readers
can access the complete papers included in the literature review and improve their
knowledge. Therefore, by using such activities and processes, ethical challenges and risks
can be managed and the quality of the project can be improved.
Limitations
Research methods and approaches that are more effective and reliable helped to develop
significant outlines and gain significant data in regards to the trust and reputation issues
related to IoT. This chapter helped to increase understanding and knowledge about
research methodologies and managed research gaps significantly. In this research, a
qualitative design is followed as it is beneficial for gaining in-depth observation and
proposing effective data collection processes.
Using qualitative research, it is easy to manage and address the research questions and
research can be completed within time with significant findings. However, the major
limitation of the methodology is that it focuses on secondary research based on a literature
review by which theoretical information can be obtained and quality can be affected.
Qualitative design provided brief and limited information about trust and reputation
modelling in IoT and it is difficult to support arguments with the viewpoints of the
participants. Future research should focus on such limitations and need to conduct primary
research to get participants' opinions regarding IoT-based trust and reputation modelling.
Analysis And Findings From Data: Introduction
Data analysis is one of the major parts of the research that delivers a platform to review and
evaluate the collected data for obtaining effective findings. In this report, descriptive
content of analysis is selected as it is capable to issue findings or results effectively and also
managing research problems easily. This chapter will review and compare the various trust
and reputation models used in IoT technology and will also propose a taxonomy for
improving security and trust within IoT networks.

Trust And Reputation Methods
Kravari and Bassiliades, (2019) reported that trust and reputation both are major parts of
IoT security that need to be managed from IoT networks by implementing proper models
and approaches. In order to manage trust issues from IoT networks, companies need to
manage identity and access controls by implementing proper security frameworks and
programs. Increasing the security of IoT networks can help to build trust and reputation
and protect the personal data of the users from cyber-criminals. There are the following
methods and approaches used for evaluating trust and reputation in IoT:
A) Social Networking Model
Kusuma, et al., (2020) reported that social networking is an effective trust model that can
review and evaluate the security risks associated with IoT networks and analyze trust-
related problems. It is a conventional peer-to-peer network-based trust approach that is
linked with the social network information shared over IoT devices. In the context of IoT
trust and reputation, any entity builds social relations with other entities which are based
on social networks and communication channels (Guo, and Chen, 2015). It follows the Eigen
trust model for IoT networks where every peer is assigned a global trust value based on the
previous transactions over communication networks.
Figure 4.1: Trust Management in IoT (Jabeen, et al., 2018)
Malicious nodes are mainly developed by hackers that realize trust-related attacks and help
to reduce the cyber-security of sensitive data. In order to manage such problems, the social
networking model helps to connect malicious detection nodes with the IoT networks and
provides self-promoting and risk detection programs. Mohammadi, et al., (2019) reported
that the major advantage of the social networking approach is that it is based on the
theoretical models that depend on the communication networks used in the IoT devices.
The social networking model mainly follows three parameters for evaluating trust and
reputation: honesty, community interest and cooperativeness (Kounelis, et al., 2014). So,
this dynamic model is capable to find the best parameters of trust and maximize the
effectiveness of IoT networks. It is completely based on the subjective model which is
mainly used for managing the trustworthiness of the IoT-based security programs and
frameworks. However, the major disadvantage of this model is that it is less capable to
detect and identify insider risks and threats leading to security concerns due to which trust
cannot be increased and privacy can be affected.
B) Fuzzy Technique

Origgi, (2019) identified that the traditional security and access control models cannot
manage security and privacy concerns where identities are not described earlier which can
negatively influence trust and reputation. A fuzzy approach is more effective and beneficial
for the IoT networks that develop trust-based access control to deal with security and data
breach-related problems (Bauer, et al., 2013). In the context of IoT networks, the trust is
computed in trust-based access control by considering three factors experience, knowledge
and suggestion. The trust values are now mapped to the permissions, and requests for
accessing the confidential platforms.
Figure 4.2: Trust and reputation in IoT(Pollák, et al., 2016)
Pecori, (2016) stated that the fuzzy trust model also follows the PeerTrust-based reputation
model in order to manage factors influencing the security and trust of personal details. The
key benefit of the fuzzy model is that it is capable to increase trust and reputation in IoT
networks by implementing security controls and access controls. Pollák, et al., (2016)
identified that the fuzzy-based trust model is capable to ensure flexibility and security of the
IoT networks and building effective trust and reputation. In order to calculate trust, the
fuzzy technique composes three layers as sensor layer that contains physical devices, the
core layer that contains access networks and the application layer that includes distributed
networks and interfaces (Kang, et al., 2013). For IoT networks, the fuzzy trust model
enables the IoT to deliver more qualified and secured services to the companies (Ullah et al.,
2019). However, the major disadvantage of the fuzzy trust model is that it requires proper
resources and systems for evaluating trust and reputation which can produce challenges
and difficulties in IoT systems (Hussain et al., 2020).
C) Cooperative Approach
Rafey, et al., (2016) proposed a hierarchical trust model for IoT technology which is based
on the cooperative approach to detecting unwanted and malicious signals from computing
networks. The cooperative approach is more effective and suitable for trust and reputation
in IoT that uses a decentralized model to handle cooperation in the IoT-based
communication architecture (Rana and Bo, 2020). In this manner, a trust management
system is developed which is capable to evaluate and review the security risks and focus on
the trust of a node based on the previous behaviour.
Figure 4.3: trust model for IoT networks (Salamai, 2021)
The key feature of the cooperative approach is that it calculates the value of trust and
reputation regarding both direct observation and indirect observation with the

neighbouring nodes. The trust management system based on the cooperative model is
capable to contains several phases such as collecting the trust value of the nodes,
establishing a collaborative service with the nodes, and evaluating self-updates with the
help of retrieving information from the previous activities, setting a recommendation score
to every node. Salamai, (2021) reported that trust modelling is completely dependent on
the security programs and networks linked with the IoT and corporation modelling helps to
calculate and propagate the reputation that allows the development of reliable and effective
trust relations among self-organized nodes. In terms of effectiveness, a cooperative
approach is more reliable and beneficial for the IoT networks that can manage the security
issues and risks from internal networks and protect data effectively. However, the major
limitation of this trust approach is that it is complex and requires more time to build trust
models for the communication networks and external risks cannot be minimized (Chen et
al., 2018).
D) Identity-Based Method
(Tr?ek, 2018) The author reported that IoT is mainly connected with the computing
networks and communication channels which are less capable to defend against cyber-
attacks due to which hackers can be performed cyber-criminal activities and trust can be
affected. a Web social network is mainly used for detecting and managing the security-
based trust issues from IoT networks. It is found that an identity-based approach is more
effective and reliable for trust management in IoT technology that follows the social
networking models to deal with trust and reputation problems.
Figure 4.4: IoT trust (Truong, et al., 2016)
In order to increase confidentiality and privacy for the IoT networks, it is recommended
that the identity-based technique should be followed as it is capable to limits attacks and
criminal activities from outside the IoT network and identifying malicious nodes. Truong, et
al., (2016) agreed and stated that an identity-based network has the potential to monitor
the movements of the network-based nodes from the IoT systems and detect malicious
codes and risks to protect data from hackers. Moreover, the identity-based trust model can
be connected with the communication channels and device node identification from the
host addressing so that chances of hacking can be minimized.
More than 70% of the cyber-criminals perform unauthorized activities over IoT networks
and communication channels for which identity-based is helpful that provides a way to
increase the authenticity of the networks and communication nodes. The shared
authentication of nodes receipts through the sign of the identity characteristics, produced
by a trusted third party. Truong, et al., (2016-II) observed that identity info is revealed only
to the authorized focusses and the Domain Trusted Entity establishes an internationally

trusted substructure by the pre-sharing of cryptographic credentials. The infrastructures-
based communication is being endangered through cryptographic protocols and is capable
to deal with trust and reputation-related problems. The major advantage of the identity-
based model is that it uses cryptography algorithms that are capable to convert data into
codes so that privacy and hacking problems can be minimized (Pöhls, et al., 2014).
However, it requires changing communication infrastructure and advanced networks and
systems are required that can increase operational costs.
E) TRMSim-WSN Model
Turkina and Ihnatiev, (2020) reported that TRMSim-WSN is an effective trust and
reputation model which is used for IoT-based distributed networks. It is capable to identify
and review the malicious nodes from the communication networks and providing a way to
incorporate a new trust model. The major benefit of the TRMSim-WSN model is that it
stimulates the security risk detection programs over IoT networks so that insider threats
and vulnerabilities can be managed and a proper effective communication
framework (Mahalle, et al., 2013). As compared to the other trust and reputation models,
TRMSim-WSN is more reliable and beneficial that delivers a generic API composed to deal
with trust-related challenges. Major five steps are followed by TRMSim-WSN to simulate
trust within IoT networks such as gathering information, scoring, entity selection,
transaction, reward and punish.
Figure 4.5: TRMSim-WSN model (Ureña, et al., 2019)
Ureña, et al., (2019) examined that TRMSim-WSN initially gather behavioural information in
regards to the members accessing IoT communication networks. The collected information
is utilized for delivering a score that can be used to examine the reputation and trust of each
user in the database system. The most trustworthy entity is mainly chosen and a transaction
is performed for evaluating the satisfaction and security levels. As per the identification, it is
easy for managing trust-related problems and increase security of the sensitive data.
Therefore, it is stated that TRMSim-WSN is helpful for the trust and reputation in IoT and
can be used to increase the security and trust of the communication networks. However, the
implementation of the TRMSim-WSN model can increase problems within data
management and communication systems. Therefore, all these are common trust and
reputation models that can be implemented in the IoT technology and communication
networks.
Results And Discussion
It is found that IoT is an effective technology that provides a platform for connecting
multiple devices and networks through a single system. In terms of effectiveness, IoT

technology is more effective and suitable for the data communication by which remote
access controls can be developed. The conducted literature examined that IoT is less
capable to deal with the security risks and vulnerabilities due to which users can suffer
from the data breach and hacking problems that influence trust and reputation among
users (Azzedin, and Ghaleb, 2019).
The presence of security risks in the IoT networks can affect the confidentiality of personal
details and users can suffer from ethical issues and cyber-criminals. Trust and reputations
both are linked with the security of IoT networks for which proper security and trust
models need to be implemented. Tr?ek, (2018) agreed and stated that trust can be
improved among users by increasing the security of the IoT networks for which secured
infrastructures and communication platforms need to be developed. As compared to other
communication networks, IoT is a common technology used as a wireless communication
network and enables companies to collect data from the users that require better trust and
security models.
Comparison Between Trust And Reputation Methods
Trust management plays a major contribution in the IoT networks that provides a way to
focus on the trust and reputation challenges and increase the security of personal data. The
findings show that the trust model based on the trust management approach can help the
companies to increase privacy and information security and overcome perception
uncertainty and insider threats.
Both trust and reputations are dependent on the security programs linked with the IoT
networks where it is a challenging task for the companies to build trust among users as
cyber-attacks are increasing rapidly. Truong, et al., (2016) examined that trust in IoT is
based on security and it is important for companies to securely store collected data into a
database and connect communication systems with the security frameworks. Various types
of trust and reputation methods are reviewed in the literature such as social networking,
fuzzy technique, corporative approach, PeerTrust, identity-based approach and TRMSim-
WSN model.
It is found social networking is a theoretical trust model which is capable to deal with the
security issues linked with communication networks. As compared to other trust models,
social networking ensures quick, easy and secure access to the users and personal details as
it is based on the PeerTrust approach. Social networking based on the PeerTrust model is
capable to work in peer-to-peer networks where factors affecting trust can be reviewed and
evaluated at every node.
The literature review shows that in the social networking approach, every IoT device
contains the identity and changes the trust of nodes by reviewing their behaviour. Security
can be established within the IoT networks using proper risk assessment plans and it is

easy to build trust and reputation among users. However, it is a theoretical and behavioural
trust model due to which significant and depth insight into IoT trust cannot be obtained.
Bennaceur, Idoudi, and Saidane, (2021) agreed and stated that traditional access controls
are not capable to identify the factors affecting trust and reputation among IoT networks
due to which privacy can be affected.
Table 4.1: Comparison between trust models used for IoT networks
Trust Model
Formed for this basis
The problem it addresses
Limitation
eBay Model for Auction
Has a centralized model of trust that relies on the online feedback system
Evaluate the trust that is evident between buyers and sellers
Has limited ability to restrict identities that are duplicate or fake.
Point-Based Model of Trust

Access and control authentication
Via point-based authorization
Sensitive information is protected while in an open environment.
The description of how point values are gathered is not stated.
Basic Management Model of Trust
Properties from the trust
Transfer of trust.
Existing relation between the transfer of trust and entailment of policy
Not advisable when dealing with distributed systems.
Credential Based Model of Trust
Information based authorization

Authentication procedure in a distributed environment
Centralized environment.
Figure 4.6: Root of trust in IoT (Bennaceur et al., 2021)
In order to manage such problems, a fuzzy technique is developed which is capable to
propose a decentralized approach to deal with the trust and reputation problems in IoT.
The major benefit of the fuzzy approach is that it develops a trust-based access control
where trust is computed by considering various factors such as knowledge, experience and
recommendations. The literature review identified that the fuzzy approach can identify the
trust values associated with the IoT users and map into the permissions to provide
accessibility regarding communication channels (Gui et al., 2018).
Another advantage of the fuzzy approach is that it contains three layers to manage trust-
related problems from IoT networks as device layer that monitor computing devices, the
request layer that provides updates regarding security and trust problems and the access
control layer that provide remote access to the collected data. Fernandez, et al., (2017)
examined that the fuzzy approach is beneficial for increasing the trust level among IoT
networks and dealing with security issues by proposing security frameworks. However, the
major drawback of the fuzzy approach is that it overhead the communication networks due
to which the connectivity may be lost. From the conducted literature, it is found that a
cooperative trust model is a decentralized approach to handling trust-related problems
from IoT architecture.
Figure 4.7: IoT security and trust challenges (Bennaceur et al., 2021)
In the context of trust management, the cooperative approach follows the PowerTrust
model which is capable to consider the distribution of peer networks in a secured and
trusted framework. A cooperative major is beneficial for the IoT networks that calculate
trust value in regards to the direct observation and indirect observation coming from the
communication nodes (Wei et al., 2014). As compared to the other trust models, the
cooperative approach is more effective that collecting the trust values from nodes and
establishing a collaborative service with the network so that trust-related issues can be

minimized. The findings show that the cooperative approach is capable to recommend trust
programs and historical trust issues are computed by the evidence combination and Bayes
algorithm to examine effective information.
However, the major disadvantage of the cooperative approach is that it requires building
communication and security frameworks for IoT networks that can increase complexity and
affect overall performance. Fortino, et al., (2020) proposed a trust model based on the
identify method which is capable to determine trust management for the IoT networks and
communication systems. The major benefit of the identity-based method is that it increases
the security of the communication networks and limits cyber-attacks from the IoT networks
along with malicious codes (Tabassum and Lebda, 2019).
Figure 4.8: Trust evaluation factors (Jabeen, et al., 2018)
As compared to other trust models, the identify-based method is more effective and suitable
that delivers a platform to detect and monitor the malicious risks from networks and nodes
so that trust can be increased among users and privacy of sensitive data can be improved.
TRMSim-WSN is one of the common trust models used by companies to deal with the
security and trust issues associated with IoT networks. The major advantage of TRMSim-
WSN is that it delivers a platform to develop and incorporate trusted networks for the IoT
devices so that collected data from the users can be protected from cyber-criminals. Jabeen,
et al., (2018) agreed and stated that the TRMSim-WSN model simulates the risk
identification programs and compares the security models with the previous activities to
obtain significant information.
It contains various steps for evaluating trust and reputation in IoT networks such as
gathering data, scoring trust, entity selection, and transaction and reward programs.
However, TRMSim-WSN requires to development of proper programs and risk
identification frameworks that can increase challenges and difficulties in businesses and IoT
networks. In terms of cost, TRMSim-WSN implementation requires advanced
communication systems and programs which are more expensive to implement. Therefore,
it is recommended that companies have a trust and reputation model as per their
requirement to minimise trust and security concerns.
Conclusion
From the above findings, it may be said that IoT is an effective technology that can be
combined in businesses to manage communication gaps and propose remote access
controls but it is difficult to resolve security and trust-related problems. This research
helped to review the challenges and risks associated with trust and reputation in IoT
networks and helped to increase understanding and knowledge about IoT trust. It is found

that it is important for companies to include IoT in their business operations due to their
ability to propose data communication flows and provide smart analytics so that
identification and evaluation of the collected data can be done in less time. It is capable to
build wireless communication networks by which it is easy for the users to exchange data
or information without requiring connecting cables and wires.
With the help of IoT networks, business communities are capable to deal with
communication-related problems and collecting data from the customers for performing
analysis and evaluation-related activities. Due to improper knowledge about cyber-security,
it is challenging for the employees to deal with the security threats and communication
channels that can be accessed by the attackers which can affect trust and reputation
negatively.
Various types of trust and reputation methods are reviewed in the literature such as social
networking, fuzzy technique, corporative approach, PeerTrust, identity-based approach and
TRMSim-WSN model. The literature review identified that the fuzzy approach can identify
the trust values associated with the IoT users and map into the permissions to provide
accessibility regarding communication channels. The findings show that the cooperative
approach is capable to recommend trust programs and historical trust issues are computed
by the evidence combination and Bayes algorithm to examine effective information.
This dissertation concluded:
Enhanced knowledge and awareness of cyber security and its model.
The fuzzy approaches for better security of IoT users.
Cooperative approaches for recommending the proposed trust model.
Recommendations
It is recommended that companies should focus on the security and trust-related problems
associated with the IoT networks and proper trust models should be applied so that data
privacy and confidentiality can be improved. In order to evaluate trust and reputation, it is
recommended that the TRMSim-WSN model should be implemented due to its capability to
develop a simulation system for reviewing the security issues and protecting IoT networks
from malicious codes (Yoon et al., 2015). Moreover, companies should develop proper risk
assessment plans and ensure that collected data from the users through IoT networks are
stored in secured database systems.
Future Direction
The major limitation of this research is that it focuses on secondary research based on a
literature review by which theoretical information can be obtained and the quality of
research can be affected. However, future research will survey questionnaires for gaining
viewpoints of the participants regarding trust and reputation in IoT networks and will

manage research gaps.
Moreover, a trust model will be implemented over IoT-based communication networks to
deal with the security and trust-related problems and will also suggest effective security
controls and frameworks for improving data privacy.
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