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A Study on Trust Management Algorithms for the Social Internet of Things
XIE YINAN
SCHOOL OF COMPUTER SCIENCE AND ENGINEERING
2016
A Study on Trust Management Algorithms for the
Social Internet of Things
XIE YINAN SCHOOL OF COMPUTER SCIENCE AND ENGINEERING
A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTER OF SCIENCE IN
DIGITAL MEDIA TECHNOLOGY
2016
Contents
Abstract ............................................................................................................................ III
Acknowledgements .......................................................................................................... IV
Acronyms .......................................................................................................................... V
List of Figures .................................................................................................................. VI
List of Tables ................................................................................................................. VII
1. Introduction ................................................................................................................ 1
2. Previous Works .......................................................................................................... 4
2.1 WSN and MANET ................................................................................................... 4
2.2 Challenges of IoT Trust Management...................................................................... 5
2.3 Trust Attack Model .................................................................................................. 7
2.4 IoT Trust Management Model ................................................................................. 8
2.4.1 Trust Composition ............................................................................................. 9
2.4.2 Trust Propagation .............................................................................................. 9
2.4.3 Trust Aggregation .............................................................................................. 9
2.4.4 Trust Update .................................................................................................... 10
2.4.5 Trust Formation ............................................................................................... 10
3. IoT Trust Management Algorithm ........................................................................... 11
3.1 TRM-IoT [21] ........................................................................................................ 11
3.1.1 Introduction ..................................................................................................... 11
3.1.2 Main Theory .................................................................................................... 11
3.2 Dynamic Trust Management for IoT Applications [22] ........................................ 12
3.2.1 Introduction ..................................................................................................... 12
3.2.2 Main Theory .................................................................................................... 13
3.3 Trust Management for SOA-based IoT [23] .......................................................... 14
3.3.1 Introduction ..................................................................................................... 14
3.3.2 Main Theory .................................................................................................... 15
3.4 A Context-Aware and Multi-Service Approach [24] ............................................. 16
I
3.4.1 Introduction ..................................................................................................... 16
3.4.2 Main Theory .................................................................................................... 16
3.4.3 Conclusion ....................................................................................................... 17
4. IoT Simulation Tools Selection ................................................................................ 18
4.1 IoT Simulation Tools Requirements ...................................................................... 18
4.2 IoT Simulation Tools Comparison ......................................................................... 19
5. Simulation ................................................................................................................ 23
5.1 Simulation Workflow ............................................................................................. 23
5.2 Simulation Result ................................................................................................... 25
5.2.1 Protection against Bad-Mouthing Attack ........................................................ 28
5.2.2 Protection against On-Off Attack .................................................................... 29
5.2.3 Protection against Opportunistic Service Attack ............................................. 31
5.2.4 Protection against Self-Promoting Attack ....................................................... 31
6. Conclusions and Future Work .................................................................................. 33
6.1 Conclusion.............................................................................................................. 33
6.2 Recommendations for Further Research ................................................................ 33
References ........................................................................................................................ 35
Appendix A ...................................................................................................................... 38
II
Abstract The Internet of Things (IoT) has started to gain new momentum in recent years. Things
like Smart City, Smart Health Devices, WSN, and MANET are only a small part of it. A
large number of applications and services for IoT have emerged into markets in various
areas, such as healthcare, transport, food safety, and remote control. While applying these
applications, large sensitive data are exposed. It is necessary to consider security problems
for IoT such as malicious attacks which would threaten personal privacy and important
information. A trust management model is considered as a method to defend IoT system
against malicious attacks and provide reliable data transmission.
In this dissertation, I investigate the challenges of IoT trust management system and then
I provide an overview and comparison of prevalent trust management algorithms for social
Internet of Things. Four papers which use dynamic weighted trust models are discussed
in details for case studies. Besides, I briefly mention the evaluation of various simulation
tools that are appropriate for new IoT requirements. Furthermore, I implement the selected
algorithms on OMNeT ++ platform and compare the simulation results such as the trust
convergence, accuracy and robustness on different sets of simulations. In addition, I will
talk about the future directions of IoT trust management system at the end.
Index Terms—Internet of Things, Trust Management, Simulation, Evaluation
III
Acknowledgements Thanks for my supervisor, Assoc. Prof. Zhang Jie for providing me this topic and his
continuous support of my M Sc. Dissertation. His guidance and advice helped me in all
time of research, helped me make a better plan and pushing me to learn new technologies.
In addition, I must acknowledge the teaching team of Digital Media Technology course. I
appreciate Assoc. Prof. HE Ying and Zheng Jianmin for their lectures and project on
advanced computer graphics and image processing which help me review the C++
programming and math. They have helped me understand the concepts and apply them in
this thesis. Furthermore, I appreciate the help from Wang Chengxin for helping me make
the right choice while implementing the algorithm.
Last but not the least, I would like to thank my parents for constantly supporting me in my
studies.
XIE YINAN
IV
Acronyms IoT Internet of Things
SOA Service Oriented Architecture
WSN Wireless Sensor Networks
VANET Vehicular Ad-hoc Networks
MANET Mobile Ad-hoc Networks
QR Quality of Recommendation
OOA On-Off Attack
BMA Bad-Mouthing Attack
BSA Ballot-Stuffing Attack
OSA Opportunistic-Service Attack
SPA Self-Promoting Attack
V
List of Figures Figure 1. Social Structures of IoT ...................................................................................... 2
Figure 2. Trust Computation Classification Tree .............................................................. 8
Figure 3. Direct Observation Trust .................................................................................. 13
Figure 4. Indirect Recommendation Trust ....................................................................... 14
Figure 5. Context-Aware Trust Model Phases................................................................. 16
Figure 6. Features of Current Testbeds towards Experimental Requirements ................ 22
Figure 7. Local IoT Infrastructure ................................................................................... 24
Figure 8. Trust Management Simulation Workflow ........................................................ 25
Figure 9. Node’s Trust Value without Transactions ........................................................ 27
Figure 10. Trust Value Evolution in Presence of BMA .................................................. 28
Figure 11. Context-Aware Trust QR Value Evolution in Presence of BMA .................. 29
Figure 12. Trust Value Evolution in Presence of OOA ................................................... 30
Figure 13. SOA-based Trust Management with Different PM ........................................ 30
Figure 14. Malicious Node Trust Value Evolution in Presence of OSA ......................... 31
Figure 15. Malicious Node Trust Evolution in Presence of SPA .................................... 32
Figure 16. Model Structure in OMNeT++ ....................................................................... 38
Figure 17. Definition and Structure of IoTNode ............................................................. 39
VI
List of Tables Table 1. Network Simulation Availability ....................................................................... 20
Table 2. Network Simulation Properties.......................................................................... 20
Table 3. Fuzzy Trust Parameters ..................................................................................... 26
Table 4. SOA-based Trust Parameters ............................................................................. 26
Table 5. Context-Aware Trust Parameters ...................................................................... 27
VII
1. Introduction
OT is the internet that makes each device, thing and each person become a locatable,
addressable, and readable counterpart. In this internet, virtual entities can produce and
consume services and cooperate for a similar goal. IoT is regarded as the next-generation
networking paradigm and service infrastructure. A large number of applications and
services for IoT have emerged into markets in various areas, such as healthcare, transport,
food safety, and remote control, which benefit people a lot. However, without developing
security and privacy mechanisms, malfunctions and attacks will overshadow it as well [1].
The IoT’s highly distributed nature and the limitation of the devices make malicious
entities have the opportunity to hack in. Easily accessible objects in city streets or other
unprotected zones are also vulnerable against attacks [1]. So security plays an important
role in IoT.
In order to facilitate IoT entities to detect malfunctions and establish proper collaboration,
trust management is considered as a key technique in IoT for providing qualified services,
enhancing user privacy and information security [2]. Trust is a complicated concept
containing not only security, but also strength, goodness, reliability and other properties
of an entity. Gambetta defines the trust in IoT as: "trust (or, symmetrically, distrust) is a
particular level of the subjective probability with which an agent assesses that another
agent or group of agents will perform a particular action, both before he can monitor such
action (or independently of his capacity ever to be able to monitor it) and in a context in
which it affects his own action (...)." [3]. Based on this definition, several trust
management systems try to give a measurement of the trust on the agent. And almost all
the systems utilize a scalar range from 0 to 1 to measure the trustworthiness, where 0
means no trustworthiness and 1 means complete trustworthiness. Reputation is another
most commonly used concept in the trust management system and it is usually achieved
by the accumulation of trustworthiness from the directed or indirect agents in the past
transaction.
I
1
In recent years, the idea that adds social networking elements into the IoT trust evaluation
becomes more and more popular. Social features allow different devices automatically
establish social connections and its aim is expected to make the discovery, selection,
cooperation of services, information provided by distributed objects and networks that
mentioned in [4], [5], [6] and [7] more clearly. Many researchers propose trust
management systems for the social IoT so that the new systems are able to react from
nodes’ past behaviors to finish different cooperative services. Social structures of the IoT
can be seen in the Figure1.
Figure 1. Social Structures of IoT , sourced from Bao, 2012
In this dissertation, trust management system for social IoT will be discussed in details.
By adding social features to IoT, trust value could be derived from their social
relationships. Besides, the evaluation of IoT simulation tools will be mentioned. At the
end of the dissertation, further trends of IoT trust model will be discussed. The
organization of this dissertation will be divided into 6 parts:
(1) Chapter 1 gives an introduction to the importance of IoT trust management.
2
(2) Chapter 2 covers the thorough literature review in IoT trust management, including
the challenges of the IoT trust management and the summary of existing trust models and
attack models.
(3) Chapter 3 introduces four typical trust models for social IoT.
(4) Chapter 4 briefly illustrates the evaluation of IoT simulation tools.
(5) Chapter 5 provides the experimental analysis and comparison of different dynamic
weighted trust algorithms for social IoT.
(6) Chapter 6 concludes the entire dissertation and provides an outlook for the future study.
3
2. Previous Works
This chapter firstly introduces two important networks, WSN and MANET, which are
highly related to IoT. Then, comparing to WSN and MANET, I derive the challenges of
IoT trust models. Finally, I summarize the IoT trust models and attack models of recent
literatures.
2.1 WSN and MANET
Wireless sensor network (WSN) is a network used for monitoring and recording the
communication infrastructure at different locations. The sensor network is comprised of
sensor nodes, also known as detection station, each of which is small, lightweight and
portable. Each sensor node is provided with a sensor, a microcomputer, a transceiver and
a power source. Mobile ad hoc networking (MANET) is a type of ad hoc network that can
change locations and configure itself on the air. They use wireless connections such as
WI-FI, cellular or satellite transmission to log into various networks. VANET is a type of
MANET that allows vehicles to communicate with roadside equipment.
In the WSN and MANET security field, it has been widely studied in trust and reputation
management. Both of the WSN and MANET can manage a practical model for trust
management, and the refinement is still being proposed such as the hierarchical POMDP
based approach [8] and watchdog optimization[9] etc. As for the VANET, it ([10]) has
represented a secure and efficient scheme that is proved to be robust and fault tolerant
against temporary unavailability of the central server. And from another survey of
VANET [11], we can also find other trust management system that has achieved
robustness.
WSN and VANET are considered to be key technologies for several IoT application
domains in smart cities [12]. These two networks do have similar challenges of IoT trust
management. For example, sensor devices are usually equipped with a constrained battery
4
and work in an unattended mode to adapt various harsh environments such as the deep
desert and ocean abyss for a long period of time [9]. Energy consumption should be an
important issue in IoT, because we need to monitor them almost every second. Watchdog
systems have been widely used in WSNs systems, and there are several ways to solve the
restricted energy and limited storage such as [8], [9] and [13]. The nature of VANET
environments, which are large, decentralized, open, sparse and highly dynamic could also
be found in IoT system. There is also research on merging this two kind of network
together.
2.2 Challenges of IoT Trust Management
However, existing WSN and MANET are inadequate for IoT. IoT actually expands on
WSN and VANET requirements, which could be concluded as wider architectures, more
heterogeneous, inconstant resource capabilities and increased autonomy. The IoT trust
management must have strong security assurance for all IoT nodes at all circumstances,
from the identification of objects to the provisioning of services, from the acquisition of
data to the governance of the entire infrastructure. This kind of security mechanism should
take services into consideration from the very beginning of that entity’s life cycle. The
challenges of IoT trust management are as follows:
Heterogeneity
The Internet of Things interacts with the physical world with a large number of different
things which only have an interface in common in order to transmit. The differences
between those things can be the operating system, connectivity, I/O channels, and
performance. A cause for these differences may be the hardware of the things which may
lead to different computational power, storage capacity and energy consumption [3].
Everything connecting into IoT could be viewed as a node, which could be locatable,
addressable, and they may use different protocols and various data structures to
communicate. Therefore, security protocols that connect all these nodes into IoT should
be light-weighted and have good transformation property.
5
Scalability
Things connecting to the Internet of Things keep increasing would lead to countless
numbers of communication, transactions of information. Trust management system should
be robust enough to handle the sudden increasing data transaction using load balance
algorithms or other well-designed structures to alleviate the traffic jam. We need to keep
enough numbers of devices that are runnable in order to stay fully functional.
Data and Privacy
In terms of IoT, millions of data will flow into this highly automatic network. In this kind
of internet, one could get the profile of other persons without his/her permission. Data
privacy is one of the most sensitive subjects not only in IoT but also in all kinds of
information transaction systems. In addition， in IoT environment, the elements are
highly heterogeneous, the mechanism or the security protocol should consider the
properties of the IoT entities that many nodes do not have sufficient space or computation
ability to handle all the request that a server asks it to do. Therefore, single data
management may not be available to handle such diverse mechanisms. To manage such
big amount of data, we need the mechanism has the ability to deal with the transactions,
interpreting, and optimally balance all the rules.
Identity
Identity management is an important aspect of the internet of things which must be taken
into account by trust and reputation systems. Nodes in IoT may have a core identity and
several further identities and it is possible that node hides its true identity. Identity
management pays attention to authorization as well. Authentication and authorization
have some open research issues at the same time. The issues aims at how to balance the
system between the centralized and distributed way to cope with the trusted delivery.
Trust and governance
Trust is more than helping the IoT nodes to find a better trustee to contact with, or reducing
the uncertainty while they are interacting. Such mechanism could be well designed in
order to not only find the partner for meeting the needs but should also understand what
6
it means while providing the trust services. Governance helps to strengthen confidence on
the Internet.
2.3 Trust Attack Model
Besides the above mentioned challenges of IoT infrastructure, malfunctions and attacks
are also a big issue for developing IoT environment. Generally speaking, an agent in a
network would suffer communication protocol attacks that could interfere with the
network operations. I assume that these attacks have already handled by intrusion
detection techniques [14], [15], and will not be considered in this dissertation. In social
IoT, only trust-related attacks will be discussed in disrupting the trust management system.
A malicious node in IoT can perform the following attacks:
On-Off Attack (OOA)
OOA could be viewed as a random attack, just like its name, a malicious node could turn
on or turn off its switch to serve useful services. With this mechanism, the node will not
get low trust value and could perform an attack before the trust system being aware of.
With its feature of randomness, this attack is the hardest to be detected.
Opportunistic Service Attack (OSA)
Like the On-Off Attack could behave bad or good at different time, a malicious node
carries OSA could perform good service for one kind of service and misbehave for another
service. It is also known as a selective behavioral attack.
Self-Promotion Attack (SPA)
This kind of attack seems like bragging itself to gain more trust from the peers in order to
be selected as a service point, but then they will perform bad services.
Bad-Mouthing Attack (BMA)
The bad nodes make other well-behaved nodes lose high reputation by providing them
7
bad trust evaluation in order to decrease their chance to be selected as serving nodes. This
mechanism would increase other bad nodes to be selected and mess up the normal services
so that qualified services would not be done in an efficient way.
Ballot-Stuffing Attack (BSA)
The bad nodes collide with each other and intentionally vote for one of their peers in order
to increase the opportunity of the malicious node being selected as a recommended node.
2.4 IoT Trust Management Model
In order to design a trust model for IoT, which could handle the above mentioned
challenges and attack models, several IoT trust management systems have been generated,
and the trust computation almost falls into the classification in Figure 2. Five design
dimensions are introduced in the classification: trust propagation, trust composition, trust
update, trust aggregation and trust formation. The most commonly used methods are
marked with red color, and then the yellow ones, the blue ones mean the fewest visited
methods.
Figure 2. Trust Computation Classification Tree
8
2.4.1 Trust Composition
Trust composition determines which kind of trust values should be taken into
consideration. Usually, the QoS (quality of service) and social trust are two main
components. QoS trust means that the performance of an IoT node could serve. It is
usually measured by packet delivery ratio, load balance, energy consumption etc. Social
trust is the evaluation of social contact, the social relationship is divided by community of
interest (CoI). When a node has several choices, it would first select those nodes who have
social connections rather than unrelated devices.
2.4.2 Trust Propagation
In general, centralized and distributed systems are prevalent methods used in IoT trust
system. Distributed trust propagation defines that IoT devices store trust observations
towards their peer nodes without the use of a centralized server. The nodes in a distributed
system using their constrained storage space to restore historical transaction information
and handle the forwarding packages. Centralized trust propagation refers to those models
which need unified services to deal with the entity requesting and a centralized entity to
restore the trust values. The trust could only be acquired from the central server and the
server is well assigned according to different communities.
2.4.3 Trust Aggregation
Trust aggregation provides a concept that gathers all the feedback from directed or
undirected peer observation of the trust evaluation. Weighted sum method [16], [17], as
the prevalent technique to be used, refers to add weights to direct or indirect trust. It could
be developed in both fixed and dynamic way. Belief theory method [18], known as
Dempster–Shafer theory as well, is a framework used for balancing uncertainty. It could
also serve as connected to other probability theories frameworks. Bayesian inference [19]
becomes a popular trust computation model because of it's easy to implementation and
well statistical basis. With Bayesian inference, parameters in the model with a probability
distribution are updated upon new events. Regression Analysis [20] is a statistical way to
estimate relationships between trust and a set of variables characterizing the behavior of
a node.
9
2.4.4 Trust Update
In general, there are two schemes involving the trust model: time-driven scheme and
event-driven scheme. The time-driven scheme decreased the importance of trust reports
that were made a long time ago. Usually, the latest evaluated trustworthiness get bigger
weights. Event-driven scheme refers to a node’s trustworthiness get updated after an event
or transaction is made. We can also combine these two methods into one equation by
assigning different weights or coefficients.
2.4.5 Trust Formation
Trust formation provides the scheme to form the overall trust by using different trust
criteria. Single trust refers to the scheme that only one trust property is involved in a trust
protocol. For example, quality of services is considered the single most important metric
in social IoT. Dynamic trust implements the common belief that trust evaluation should
be multidimensional. Various trust properties such as intimacy, honesty, unselfishness and
competence should be deemed to assess the overall trust value of a node.
10
3. IoT Trust Management Algorithm
In this chapter, I choose four papers to give an overview of dynamic weighted trust
algorithms for social IoT. The first paper gives the basic human-assisted dynamic
weighted trust management to evaluate the trustworthiness with fuzzy reputation. The
second paper proposes a dynamic trust management for a community-based social IoT
environment by considering multiple social relationships among device owners. The third
paper introduces social relationships into the system and uses mean square error to decide
the weights of directed trust and indirect trust. The third paper provides an optimization
method towards the second one. The last paper proposes an automatic way to update trust
evaluation to defense various attacks.
3.1 TRM-IoT [21]
3.1.1 Introduction
The author proposes a trust model called TRM-IoT in order to enhance communications
among different devices in IoT based on their behaviors. This paper concerns about the
performance of WSNs in IoT system. After a wide set of simulation on NS-3 simulator,
accuracy, robustness and lightness of the proposed model are also validated.
3.1.2 Main Theory
The author proposes a fuzzy trust and reputation management algorithm upon a
community of sensor nodes in IoT. First, preprocessing step let nodes to make transactions
with each other and their behavior will be recorded. Second, the system evaluates the
performance of each node. Third, the model combines the directed and indirect trust
evaluation using given weights. This model is designed to help a sensor node which
requests a specific service to find the most trustworthy assisting node. In the fuzzy trust
model description, the author provides the reputation evaluation metrics as follows: (1)
EPFR. It focuses on End-to-end packet forwarding ratio. (2) AEC. It concerns about the
11
energy consumption. (3) PDR. The package delivery ratio. Here the malicious nodes will
intently drop the packets. The combined reputation with above three factors is computed
by the following equation:
In the next part, the author provides the trust evaluation formula combined with fuzzy
elements as follows
While , 𝐹𝐹𝑖𝑖𝑖𝑖denotes the times of positive transaction and 𝑅𝑅𝐹𝐹𝑖𝑖𝑖𝑖 denotes the
times of successful transactions. 𝐶𝐶𝑡𝑡ℎ𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑜𝑜𝑜𝑜𝑜𝑜 denotes the threshold of times of successful
transactions, since we cannot simply calculate the trust value through a few arbitrary
transaction, only if the successful transaction times have exceeded this 𝐶𝐶𝑡𝑡ℎ𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑜𝑜𝑜𝑜𝑜𝑜 can it
be calculated using the equation . It also provides a similar way to evaluate
recommendation 𝑇𝑇𝑖𝑖𝑖𝑖𝑟𝑟 . Here, I represent the recommendation as R while directed trust
evaluation as D. If we choose n witness nodes to evaluate the trustworthiness, the fuzzy
membership function can be defined as the following equation.
3.2 Dynamic Trust Management for IoT Applications [22]
3.2.1 Introduction
This paper proposes a trust management protocol based on a social trust in a community-
based social IoT environment and update trust value using both direct observations and
indirect recommendations. The author provides various indicators such as the
convergence, accuracy, and resiliency properties to validate the trust model.
12
3.2.2 Main Theory
The trust valueT𝑖𝑖𝑖𝑖𝑋𝑋(𝑡𝑡) is a certain number in [0, 1]. When node i finish the transaction with
node k at time t, node i will update its trust assessment T𝑖𝑖𝑖𝑖𝑋𝑋(𝑡𝑡) as follow：
T𝑖𝑖𝑖𝑖𝑋𝑋(𝑡𝑡)= (1 − 𝛼𝛼)T𝑖𝑖𝑖𝑖𝑋𝑋(𝑡𝑡 − ∆𝑡𝑡) + 𝛼𝛼T𝑖𝑖𝑖𝑖𝑋𝑋,𝑜𝑜𝑖𝑖𝑟𝑟𝑟𝑟𝑑𝑑𝑡𝑡(𝑡𝑡), 𝑖𝑖𝑖𝑖 𝑗𝑗 == 𝑘𝑘;
X = honesty, cooperativeness, or community-interest
Direct trust observations
T𝑖𝑖𝑖𝑖ℎ𝑜𝑜𝑜𝑜𝑟𝑟𝑟𝑟𝑡𝑡𝑜𝑜,𝑜𝑜𝑖𝑖𝑟𝑟𝑟𝑟𝑑𝑑𝑡𝑡 refers to honesty value based on the direct observation of node j to node
i. T𝑖𝑖𝑖𝑖𝑑𝑑𝑜𝑜𝑐𝑐𝑟𝑟𝑟𝑟𝑐𝑐𝑡𝑡𝑖𝑖𝑐𝑐𝑟𝑟𝑜𝑜𝑟𝑟𝑟𝑟𝑟𝑟,𝑜𝑜𝑖𝑖𝑟𝑟𝑟𝑟𝑑𝑑𝑡𝑡 gives the degree of cooperativeness of node j and i based on direct
observations over the range of [0, t]. The Figure4 indicates the process of how node i
evaluates node j with directed observation and past experiences.
Figure 3. Direct Observation Trust
Indirect recommendations
Γ= 𝛽𝛽T𝑖𝑖𝑖𝑖𝑋𝑋 (𝑡𝑡)
1+𝛽𝛽T𝑖𝑖𝑖𝑖𝑋𝑋 (𝑡𝑡)
, T𝑖𝑖𝑖𝑖𝑋𝑋(𝑡𝑡) = (1 − 𝛾𝛾)T𝑖𝑖𝑖𝑖𝑋𝑋(𝑡𝑡 − ∆𝑡𝑡) + 𝛾𝛾T𝑘𝑘𝑖𝑖𝑋𝑋,𝑟𝑟𝑟𝑟𝑑𝑑𝑜𝑜𝑟𝑟(𝑡𝑡), 𝑖𝑖𝑖𝑖 𝑗𝑗! = 𝑘𝑘;
T𝑖𝑖𝑘𝑘𝑋𝑋 provides the trust value of recommendation of node k towards node i. The
13
contribution of recommended trust increases proportionally as either T𝑖𝑖𝑘𝑘𝑋𝑋 or β increases.
The Figure5 indicates the process of how node i evaluates node j with recommendation
and past experiences.
Figure 4. Indirect Recommendation Trust
3.3 Trust Management for SOA-based IoT [23]
3.3.1 Introduction
In this paper, the author introduces similar levels of friendship, social relations, and social
interests into an IoT trust model. It uses dynamic weights to adaptively balance direct trust
and indirect trust. A dynamic reduction of convergence time and trust estimation bias is
used to mitigate the existence of malicious nodes in the service and their bad behaviors.
In trust composition, it takes QoS and social trust into consideration. In trust propagation,
this model is based on a distributed system and each node collects trust information
automatically through the network. It could both be trustee and trustor upon request. In
trust aggregation, it uses Bayesian inference and self-observation to form the direct trust
value, the social similarity is used as a weighted sum to aggregate into indirect trust value.
As for trust update, it utilizes both time-driven and event-driven scheme. When the trust
value is outdated, it also takes trust decay into consideration. As for the resilience against
14
the malicious nodes, OOA is not well detected. Aggregation in direct trust and indirect
trust with dynamically updated weights helps to prevent the OSA. By using social
similarity as a part of recommending criteria, BMA and BSA are adaptively filtering. SPA
is well detected during the trust propagation. This paper is actually an optimization of the
second case [22].
3.3.2 Main Theory
Firstly, the author mentions the similarity metrics and the formulas are similar to the
cosine of the angle between the friendship vectors (VF), F denotes the friends list. The
formula is shown as follow:
sim�𝑢𝑢𝑥𝑥,𝑢𝑢𝑜𝑜� =VF𝑥𝑥�������⃗ ∗ VF𝑜𝑜�������⃗
�VF𝑥𝑥�������⃗ ��VF𝑜𝑜�������⃗ �=
�F𝑥𝑥 ∩ F𝑜𝑜�
�|F𝑥𝑥| ∗ �F𝑜𝑜�
Then, the author uses the similarity metrics to add weights to the direct trust and redirect
trust value. By using the mean square measurement, the weights of the formula are
calculated by solving the following equation. 𝑡𝑡𝑥𝑥,𝑖𝑖𝑜𝑜 denotes the direct trust value, 𝑡𝑡𝑥𝑥,𝑖𝑖
𝑟𝑟
denotes the indirect trust value, 𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑥𝑥,𝚤𝚤(𝑜𝑜𝑟𝑟𝑛𝑛)�������� is the most recent direct user satisfaction
experiences within the last trust update ∆t which is also called the ground trust value:
MSE(µ) = � (𝜇𝜇 ∗𝑖𝑖
𝑡𝑡𝑥𝑥,𝑖𝑖𝑜𝑜 + (1 − 𝜇𝜇) ∗ 𝑡𝑡𝑥𝑥,𝑖𝑖
𝑟𝑟 − 𝑖𝑖𝑥𝑥,𝚤𝚤(𝑜𝑜𝑟𝑟𝑛𝑛)��������)2
By calculating the derivative of the formula aforesaid, we could find the best solution to
maximize the protocol performance meanwhile, minimize the trust bias. Then the author
mentions the storage management, which uses the max-min-median heap to store the trust
value. Thus , the query for extreme numbers can be done in O(1) constant time, while all
other operations such as searching, insertion and deletion can be achieved in O(log n)
logarithmic time.
15
3.4 A Context-Aware and Multi-Service Approach [24]
3.4.1 Introduction
In this paper, to overcome the limitation of IoT architecture, such as heterogeneity,
capabilities, and services, paper designs a context-aware and multi-service trust
management system with trust values updated by events and time.
3.4.2 Main Theory
The paper firstly gives a description of their model, the overall progress shows in Figure
3. The model consists of a circular continuous operation: 1) Trust manager to obtain the
trust management information, 2) A requesting node post the request to the trust
management system to ask for an assistant agent, 3) Trust manager filter the qualified
agent according to the past transaction reports and the reputation of different nodes , 4)
The requesting node report the trust value of every assistance agent based on their service
quality and 5) Update the trustworthiness recursively.
Figure 5. Context-Aware Trust Model Phases
As for phase 2) Entity selection, when a node asks for assistance, the trust manager returns
16
a list of trustworthy assisting nodes. Five steps are provided to filter the possible nodes. 1)
Restrict the set of proxies pi 2) Restrict the set of reports for each proxy 3) Compute
weights for each report 4) Compute trust value for each proxy 5) Provide requestor with
a list of best-suited proxies. To restrict the report, the paper proposes the context similarity
(1), (2) which is calculated by contextual distance.
dS𝑖𝑖 = �𝑆𝑆𝑇𝑇𝑐𝑐𝑟𝑟𝑇𝑇𝑟𝑟𝑡𝑡 − 𝑆𝑆𝑖𝑖� (1)
dC𝑖𝑖 = �𝐶𝐶𝑇𝑇𝑐𝑐𝑟𝑟𝑇𝑇𝑟𝑟𝑡𝑡 − 𝐶𝐶𝑖𝑖� (2)
(for reports carrying a positive evaluation)
The result of 𝑎𝑎𝑖𝑖𝑖𝑖 is used to solve the weight, which is determined by contextual distance
(𝑎𝑎𝑖𝑖𝑖𝑖) and age.
𝑎𝑎𝑖𝑖𝑖𝑖
= 𝑚𝑚𝑖𝑖𝑎𝑎��(𝑎𝑎𝑆𝑆𝑟𝑟𝑐𝑐𝑥𝑥2 ) ∗ (𝑎𝑎𝐶𝐶𝑟𝑟𝑐𝑐𝑥𝑥
2 ) ∗ �𝑎𝑎𝑆𝑆𝑖𝑖2
𝑎𝑎𝑆𝑆𝑟𝑟𝑐𝑐𝑥𝑥2 +
𝑎𝑎𝐶𝐶𝑖𝑖2
𝑎𝑎𝐶𝐶𝑟𝑟𝑐𝑐𝑥𝑥2 ��
∗ �(𝑎𝑎𝑆𝑆𝑟𝑟𝑐𝑐𝑥𝑥2 ) ∗ (𝑎𝑎𝐶𝐶𝑟𝑟𝑐𝑐𝑥𝑥
2 ) ∗ ���𝑆𝑆𝑟𝑟𝑐𝑐𝑥𝑥 − 𝑆𝑆𝑖𝑖�
𝑆𝑆𝑟𝑟𝑐𝑐𝑥𝑥 − (𝑆𝑆𝑟𝑟𝑐𝑐𝑥𝑥 − 𝜇𝜇)�2
+ �𝐶𝐶𝑖𝑖
�𝐶𝐶𝑡𝑡𝑐𝑐𝑟𝑟𝑇𝑇𝑟𝑟𝑡𝑡 + 𝜇𝜇��2
�
In the learning phase, author firstly updates witness nodes' qualities of recommendation
(QR) and then update from assisting nodes' reputation levels. QR plays an important role
in defending the system against various attacks. Author Combines QR and service ratings
on a time-based weighting factor.
3.4.3 Conclusion
From my point of view, the main contribution of this paper are what follows:
1) This model presented generic, context-aware trust management model for IoT
2) Dynamic trust scores assigned to nodes based on node status and required function
3) Independent score is given for Quality of Recommendation
17
4) QR score is adjusted through learning phase
5) System withstands several classes of attacks
4. IoT Simulation Tools Selection
In order to address the challenges and achieve efficiently, we need to test and improve the
research algorithms before they are exposed to the reality. In the field of IoT research,
establishing a network of IoT in the real scene is very difficult, a single test bed takes up
a lot of time and costs. Fortunately, with the help of simulation tools, we can easily get
the analysis, monitor the process and evaluate the security and safety of a trust model.
Researchers could easily deploy, layout and configure the nodes through writing scripts.
However, different simulation tools have different properties. Hence, how to choose a
simulation tool is always a question.
4.1 IoT Simulation Tools Requirements
Increasing heterogeneous devices and technologies require the simulation tools should
take into account the fact that interoperability at different layers of the communication
stack for resource-constraint devices [15]. According to the challenges that I discuss in
Chapter 2, the simulation environment should not only satisfy the new features but also
need to be close to real time scenario. There are various criteria which could help users to
select the simulation tools. The criteria are as follows:
Scale: According to the large scale of IoT, compared to other networks, IoT
experiments require the simulation tools which could run hundreds or even larger
number of agents simultaneously.
Heterogeneity: Things interact with the physical world with a large number of
different things, which only have an interface in common in order to communicate.
The remaining components used hardware of the things, which lead to different
computational power, storage capacity and energy consumption. Therefore,
18
simulation tools need to satisfy devices heterogeneity and underlying solutions
demands. The gateway devices should be an active part and the tools should provide
effective configuration. Besides, simulators should support diverse execution
environments by easing programmability.
Repeatability: It means repeated experiments within and across the different emulators.
Simulation tools should provide adequate packaging test specifications, marks, and
the results, so user can easily redo comparison across different sets of simulations.
Experimentation (learnability, GUI): In user view, the tools and services of simulation
tools have to provide a platform to ease the design and interaction so that researchers
could intuitive to take in hand. The applications with detailed documentations and
specific instructions always give better user experiences. Besides, the visualization is
another important part of the experimentation. Better GUI provides a clear way for
user to test and monitor their experiment.
Mobility: Many entities are human-carried devices, thus many devices move around
in the simulated environment. Therefore, addressing mobility and dynamics are also
significant to exploit and control the mobility realistic during the experiment.
User involvement: Automatic detections with feedback when a user directly affects
the effectiveness of data collection and provides an efficient mechanism for the users.
4.2 IoT Simulation Tools Comparison
When building an IoT simulation, first thing taken into consideration is its scope. In terms
of the technological scope, a simulation tool can be single-domain if it could provide only
a particular type of IoT technology [25]. Almost all of the simulation tools in IoT area are
designed specifically for wireless sensor network or RFID devices. There are few tools
could combine multiple techniques that used in IoT.
Here I list several network simulators that can be used to simulate IoT environment or
related networks in Table 1.
19
Table 1. Network Simulation Availability
Name of Network Simulator
Free Availability(site)
NS2 YES http://www.isi.edu/nsnam/ns NS3 YES http://www.nsnam.org/ns-3-24 OPNET NO http://www.riverbed.com/sg/products/steelcentral/opnet.html?r
edirect=opnet NetSim NO http://www.ssfnet.org/download OMNeT++ YES http://www.omnetpp.org/component/docma
/cat_view/17-downloads REAL YES http://www.cs.cornell.edu/skeshav/real
J-Sim YES https://sites.google.com/site/jsimofficial/downloads Cooja YES http://www.contiki-os.org
Familiar programming languages may benefit the implementation of the simulation in
term of users. Good GUI support may give users an intuitive analysis of the result and
better experience. In Table 2, I listed several network simulator properties to facilitate
choices in simulation tools. Table 2. Network Simulation Properties
Name of Network Simulator
language platform GUI support
NS2 C++,Otcl Unix/windows Poor NS3 C++, Python Linux monitoring of simulation flow OPNET C (C++) Windows excellent NetSim java Windows poor OMNeT++ C++ Windows/linux monitoring of simulation
flow, C++ development, topology definition, result analysis
REAL C Unix J-Sim Java, Tcl Windows/linux good QualNet C++ Linux excellent cooja java Virtual Machine monitoring of simulation
flow, result visualization
20
Here I recommend 4 simulator tools according to the frequency that is used in other IoT
simulation experiments, the user-friendly GUI and Operability.
NS3: As the most frequently used simulation tool, NS-3 is often used to construct a
model of the raffic generator with computer network simulation, channels, such as
Wi-Fi devices and protocol such as TCP / IP. The results are well collected and can
be visualized.
OMNeT++: It is developed by C++. Specifically designed for discrete event
simulation for communication networks modeling, multiprocessors and other
distributed or parallel systems. OMNeT++ is a modular, extensible, C++ simulation
library as well as a framework. It is designed for network simulators building and
offering IDE based on Eclipse, host of other tools and graphical runtime environment.
It could provide technology support through connecting to different frameworks and
simulation models and at the same time provide several ten thousand or more network
nodes.
QualNet: QualNet is utilized to simulate thousands of mobile nodes, through the
multi-hop ground, aircraft, and satellite media connect with different communication
capabilities and large cables and wireless networks.
Contiki Cooja: Cooja is a flexible java based simulator and it is designed for
simulating networks of sensors running the Contiki operating system. It is said the
operating system is designed for IoT, and motes can allow precise inspection of the
system behavior, or at a less detailed level, which is faster and allows the simulation
of larger networks.
According to the experimental requirements mentioned in section 4.1, I draw the Figure
6 to give an intuitive result of these four simulation tools. In our experiments, 200 IoT
nodes would be sufficient, so no higher criteria are needed. Once the node scale could
achieve 200, it means that the simulation could be run fluently without memory overflow.
Although the Contiki cooja could apply 200 nodes at the beginning, it could not handle
the memory issue well during the experiments. 4 levels of heterogeneity: 1. Homogeneous
21
2. A mix of motes 3. A mix of devices 4. A mix of technologies. The 3 levels of learnability:
1. Difficult 2. Fair 3. Easy. The 4 level of GUI: 1.No GUI 2. Plug-in 3. Good GUI 4.
Excellent GUI. The 4 levels of user involvement: 1. No 2. Passive collection 3.
Participatory sensing 4. Active user feedback. 4 levels of mobility: 1.No 2. Emulated 3.
User-implemented 4. Robot control.
Figure 6. Features of Current Testbeds towards Experimental Requirements
In the simulation analysis part, I choose to use OMNeT ++ as simulation tools.
22
5. Simulation In this section, I will explain the main workflow of the simulation and compare the result
after implementing the three papers’ algorithms [21], [23], [24] in section 3.
5.1 Simulation Workflow
Since I want to compare the result of these three algorithms, there exists some
modification on three algorithms. First of all, I need to add a centralized trust authority
which is known as a trust manager in the scenario like Figure 7 shows. Trust manager
helps to record the transaction history. Secondly, while IoT nodes request assisting for
accomplishing a service, trust manager runs the algorithm with the past trust data which
stored in it to help the requesting IoT nodes to select the suitable nodes. After the nodes
to finish the transaction, there will be a report sent by two nodes. The report contains the
result of the quality of service and the evaluation of recommendation nodes. For example,
while the assisting node is a malicious node, most of the time it leads to a negative result
(-1or 0) and the recommendation node will get a (-1 or 0) in their quality of
recommendation (QR). If the reported quality of service is good, the QR is also a positive
value.
23
Figure 7. Local IoT Infrastructure
According to the above assumption, the workflow of running the algorithm shows as the
Figure 8.
At the beginning of the simulation, OMNeT++ will automatically initialize the module,
in this stage, I arrange the node kinds in order to divide nodes into different groups and
assign social relationship. I also arrange the malicious attributes to different nodes. After
the initialization, each node will get a chance to ask another node to assist in finishing a
transaction periodically. Each traversal of the IoT nodes is called a transaction period.
Then, the request will be sent to the trust manager. The trust manager will operate the
algorithm according to direct reports and indirect recommendations in order to select the
best assisting node. After finishing the transaction, the quality of service will be
evaluated and sent back to trust manager. This process will not end until the simulation
time runs out.
24
Figure 8. Trust Management Simulation Workflow
5.2 Simulation Result
The simulation tool in this paper is OMNeT ++. Each run is implemented with randomly
selected IP source and destination pairs. Malicious nodes and the attacked nodes are
selected specifically to facilitate the observation. I build communication and
retransmission upon UDP. The OMNeT ++ setup parameters and configuration details are
listed in the following tables. For the first paper [21], denotes as Fuzzy Trust. The
configuration is in Table3.
25
Table 3. Fuzzy Trust Parameters
parameter value
Simulator OMNeT++ 4.1
Nodes number 200
Malicious nodes [20,60]
Node placement(x, y) 600,400
Packet size 1024bytes
MAC Layer IEEE 802.11
ζ 0.7
α 0.75
λ 4.6
𝐶𝐶𝑡𝑡ℎ𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑜𝑜𝑜𝑜𝑜𝑜 12
𝐶𝐶𝑅𝑅𝑡𝑡ℎ𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑜𝑜𝑜𝑜𝑜𝑜 12
For the third paper [23], denotes as SOA-based Trust, the parameters are listed in the
Table4. Table 4. SOA-based Trust Parameters
parameter value
Nodes number 200
Malicious nodes [20,100]
Node placement(x,y) 600,400
Packet size 1024bytes
𝛺𝛺 0.5
σc 0.01
𝜑𝜑 0.001
𝜆𝜆 10-20s
26
For the forth paper [24], denotes as Context-Aware Trust. The parameters are listed in the following Table5.
Table 5. Context-Aware Trust Parameters
parameter value
Nodes number 200
Malicious nodes [20,60]
Node placement(x,y) 600,400
Packet size 1024bytes
Initial QR 1
Service kinds 6
Preparing step of IoT nodes transaction seems like constructing the past memory of each
node, during this section, the transaction reports will be recorded in order to be used in
future trades. The preparation period is implemented by a random algorithm. The Figure
9 shows the trust value after this period. We can find Fuzzy Trust overlaps the Context-
Aware Trust while SOA-based Trust remains the trust around 0.835. Y-axis denotes the
trust value of the attacked node and the x-axis denotes the transaction period numbers.
Figure 9. Node’s Trust Value without Transactions
27
5.2.1 Protection against Bad-Mouthing Attack
In this section, I test the three algorithms’ reaction in presence of the BMA, and after the
first attack, no transaction will happen to this attacked node. Y-axis denotes the trust value
of the attacked node and the x-axis denotes the transaction period numbers. We can find
Fuzzy Trust has no changes since this algorithm updates the trust value based on events,
if no events happen, the trust value will not be updated. SOA-based Trust gets back to
normal trust level more quickly compared with other two. Context-Aware Trust falls down
quickly and once it finds the malicious recommendation value, the attacked node’s trust
value will quickly get renounced. Figure 10 displays the trust value evolution of the
attacked node.
Figure 10. Trust Value Evolution in Presence of BMA
QR is a key concept that used in Context-Aware Trust. By checking the consistency the
QR reports, we could find the misbehaved recommendation and it also indirectly affects
the recommended nodes or attacked nodes. Here I check the trust value in the condition
of with or without QR in the presence of Bad-Mouthing Attack. According to Figure 11,
we can find QR helps the trust manager to find the malicious reports and increase the
trust value of the appropriate nodes that have been attacked.
28
Figure 11. Context-Aware Trust QR Value Evolution in Presence of BMA
5.2.2 Protection against On-Off Attack
In this section, I test the three algorithms’ reaction to the OOA, and after the first attack,
the attack will happen periodically. According to Figure 12, we can find Fuzzy Trust get
good value while there’s no attack and get decreased trust value while malicious node
behaves badly. The Fuzzy Trust value changes obviously at the beginning phases
according to the behavior of the malicious node. However, because of its algorithm, the
more transaction happens, the less trust value will be affected. SOA-based Trust will
regain its normal trust value after a certain time and it doesn’t perceive the attacks. And
Context-Aware Trust falls down quickly and needs a long time to regain its high trust
value.
29
Figure 12. Trust Value Evolution in Presence of OOA
Here I test the convergence speed of the SOA-based Trust while PM means the proportion
of the malicious node. The results can be seen from Figure 13. The initial configuration is
10%, while in the below experiments, I compare it with 30% PM. We can find the recovery
speed after suffering a bad mouthing attack has slowed down.
Figure 13. SOA-based Trust Management with Different PM
30
5.2.3 Protection against Opportunistic Service Attack
In this section, I investigate the protection of three algorithms against OSA. After a long
time cumulative good behavior, the malicious node has gained high reputation by offering
one kind of good service and then misbehave by providing bad service. The result has
been shown in Figure 14. As for Fuzzy Trust, the more the transaction happens, the less
influence the attack has. The Context-Aware Trust and SOA-based Trust have positive
actions against this attack and the former one has better reaction.
Figure 14. Malicious Node Trust Value Evolution in Presence of OSA
5.2.4 Protection against Self-Promoting Attack
In this section, I investigate the protection of three algorithms against SPA. All the three
algorithms have detected this attack. The results have been shown in Figure 15.
31
Figure 15. Malicious Node Trust Evolution in Presence of SPA
32
6. Conclusions and Future Work
This chapter provides an overall conclusion and recommendations for further research.
6.1 Conclusion
After studying on IoT trust management system, it is not difficult to find there is little trust
management system work and when we talk about the resiliency to different trust attacks,
the approaches are quite similar. In Chapter 5, the selected algorithms have been
implemented on OMNeT++ and the simulation results have shown in different figures.
Fuzzy Trust algorithm can only detect the self-promoting attack and Context-Aware Trust
has positive reactions against all the attacks except for on-off attack. SOA-based Trust
provides the best convergence speed. According to the trust convergence, accuracy and
resiliency against malicious trust-related attacks, Context-Aware Trust gives the best
results.
6.2 Recommendations for Further Research
Based on the investigation, trust management for social IoT still have large spaces to be
improved. In recent researches, trustworthiness can only be used in the limited community
in which IoT nodes interact with similar services. However, the situation when a node
enters a new community, just like a person immigrate to other countries, its
trustworthiness in a new area has not been considered yet. According to the challenges
that have been discussed in Chapter 2 and above mentioned issues, we could expand on
research direction as follows: 1) The first research direction is to explore advanced trust
aggregation techniques such as brief theory and regression analysis based on the existing
model. 2) The second research direction is to try out innovative trust evaluation methods
in new architecture such as machine learning, data mining and find the efficient way to
add them into IoT trust evaluation. 3) The third research direction is to update existing
trust model that is resilient against all attacks. 4) The fourth direction is to extend the use
of the trust formation methods, for example, we could use a weighted sum as the main
33
trust value without aggregating all the other trust properties into one, but serves as an
assistant value. 5) Last but not least, the 5th direction is to design a trust transformation
method that could handle the immigration of the nodes from a different community.
34
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Appendix A
Figure 16. Model Structure in OMNeT++
OMNeT++ applies kinds of models and each of them consists of component architectures.
The large components and models are consisted through components using NED (a high-
level language) and programming in C++. This makes sure the designed models could
achieve free reuse. Besides, OMNeT++ provides the extension of GUI support. Its
modular architecture offers an easy way for the user application to embed the simulation
kernel as well as models. OMNeT++ is a framework approach which provides the basic
machinery and tools instead of directly providing the simulation components. Figure 4
shows the model structure of OMNeT++.
In Figure 16, the boxes represent simple modules (thick border), and compound modules
(thin border). Arrows connecting small boxes represent connections and gates. OMNeT++
provides interfaces which make it is easy to be extended.
38
Figure 17. Definition and Structure of IoTNode
Figure 17 shows the definition and structure of IoTNode, which is the module used in
implementation. IotNode inherits the wirelessNode of INet Framework. Here I put my
trust management protocol upon udpApp, and the mobility is implemented by
MassMobility. IP address and packet forwarding of each node are automatically
configurable according to XML.
39
40
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