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A Blockchain Based Authentication, Access Management and Nonrepudiation in Wireless Sensor Networks (MS Thesis without Source Code)

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In this thesis, the blockchain and smart contracts are used to provide registration, mutual authentication, data sharing and fair nonrepudiation in wireless sensor network. The proposed model consists of three types of nodes: coordinators, cluster heads and sensor nodes. A consortium blockchain is deployed on coordinator nodes. The smart contracts execute on coordinators to record the identities of legitimate nodes. Moreover, they authenticate nodes and facilitate in data sharing and arbitration in case of repudiation. When a sensor node communicates and access data of any other sensor node, both nodes mutually authenticate each other. The smart contract of data sharing and nonrepudiation is used to provide a secure communication and data exchange between sensor nodes. Moreover, it records the evidences during the data exchange. When the exchanged data is found illegitimate or the requesting sensor node denies the legitimacy of data, then an arbitration smart contract resolves the dispute on the basis of evidences and punishes the sensor node accordingly. Additionally, the data of all the nodes is stored on the decentralized storage called interplanetary file system. Moreover, the staller consensus protocol is used in the proposed model to increase the efficiency and transaction throughput. The transaction latency of the proposed system model is approximately 81.82% lower than the proof of work based model. Moreover, the gas consumption of the data request and data provisioning is economical.
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A Blockchain Based Authentication, Access
Management and Nonrepudiation in Wireless
Sensor Networks (MS Thesis without Source
Code)
By
Asad Ullah Khan
CIIT/SP19-RCS-012/ISB
MS Thesis
in
Computer Science
COMSATS University Islamabad, Islamabad - Pakistan
Spring, 2021
COMSATS University Islamabad
A Blockchain Based Authentication, Access
Management and Nonrepudiation in Wireless
Sensor Networks (MS Thesis without Source
Code)
A Thesis Presented to
COMSATS University Islamabad
In partial fulfillment
of the requirement for the degree of
MS (Computer Science)
By
Asad Ullah Khan
CIIT/SP19-RCS-012/ISB
Spring, 2021
ii
A Blockchain Based Authentication, Access
Management and Nonrepudiation in Wireless
Sensor Networks (MS Thesis without Source
Code)
A Post Graduate Thesis submitted to the Department of Computer Science as
partial fulfilment of the requirement for the award of Degree of MS (Computer
Science).
Name Registration Number
Asad Ullah Khan CIIT/SP19-RCS-012/ISB
Supervisor:
Dr. Nadeem Javaid,
Associate Professor, Department of Computer Science,
COMSATS University Islamabad,
Islamabad, Pakistan
Co-Supervisor:
Dr. Zahoor Ali Khan,
Assistant Professor, Computer Information Science,
Higher Colleges of Technology, Fujairah, United Arab Emirates
iii
Final Approval
This thesis titled
A Blockchain Based Authentication, Access Management and
Nonrepudiation in Wireless Sensor Networks (MS Thesis without
Source Code)
By
Asad Ullah Khan
CIIT/SP19-RCS-012/ISB
has been approved
For the COMSATS University Islamabad, Islamabad
External Examiner:
Dr. Waseem Shahzad
Professor, FAST National University Islamabad, Pakistan
Supervisor:
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science,
COMSATS University Islamabad, Islamabad
Co-Supervisor:
Dr. Zahoor Ali Khan,
Assistant Professor, Computer Information Science,
Higher Colleges of Technology, Fujairah, United Arab Emirates
Head of Department:
Dr. Majid Iqbal,
Associate Professor, Department of Computer Science,
COMSATS University Islamabad, Islamabad
iv
Declaration
I Asad Ullah Khan (Registration No. CIIT/SP19-RCS-012/ISB) hereby declare that
I have produced the work presented in this thesis, during the scheduled period of
study. I also declare that I have not taken any material from any source except
referred to wherever due that amount of plagiarism is within acceptable range. If
a violation of HEC rules on research has occurred in this thesis, I shall be liable
to punishable action under the plagiarism rules of the HEC.
Date: July, 2021
Asad Ullah Khan
CIIT/SP19-RCS-012/ISB
v
Certificate
It is certified that Asad Ullah Khan (Registration No. CIIT/SP19-RCS-012/ISB)
has carried out all the work related to this thesis under my supervision at the
Department of Computer Science, COMSATS University Islamabad and the work
fulfils the requirement for award of MS degree.
Date: July, 2021
Supervisor:
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science
Co-Supervisor:
Dr. Zahoor Ali Khan
Assistant Professor, Computer Information Science,
Head of Department:
Dr. Majid Iqbal
Department of Computer Science
vi
DEDICATION
Dedicated
to my mentor Dr. Nadeem Javaid, loving Parents and my brother,
who equipped me with pearls of knowledge and showed me the way
of spiritual and personal enlightenment in this world and the world
hereafter.
vii
ACKNOWLEDGEMENT
First of all, thanks to Allah Almighty who give me strength and confidence to
complete this dissertation.
Firstly, I would like to express my sincere gratitude to my advisor Dr. Nadeem
Javaid for the continuous support of my MS study and related research, for his
patience, motivation and immense knowledge. His guidance helped me in all the
time of research and writing of this thesis. I could not have imagined having a
better advisor and mentor for my MS study. I am truly indebted to him for his
knowledge, thoughts and friendship.
I would like to thank my parents for their continuous support, understanding and
assistance whenever I needed them throughout my MS studies and research work.
Furthermore, I would like to thank my brother Inam Ullah Khan. I believe that
without his motivation, it is not possible to succeed throughout my life. I am
always grateful to him for his encouragement and support. Moreover, I would like
to acknowledge Inam’s wife and my nephews: Hashir Meer Khan, Shahwaiz Khan
and Shamir Khan. They are blessings of Allah Almighty and they sacrifices their
happinsess for my studies. I am also thankful to Humaira Kiran Abdul Latif (State
Bank) for motivating, supporting and encourging me to pursue my MS degree.
After that, I would like to express my profound appreciation to my friends specially
Muhammad Riaz, Muhammad Arsalan and my relatives who supported me during
my MS and who helped me to complete my thesis. Their generous support made
this research work possible.
Last but not the least, I am greatly thankful to Director of ComSens Lab, my
mom and all of my colleagues at CUI for providing me the warm and friendly
atmosphere.
viii
ABSTRACT
A Blockchain based Authentication, Access Management
and Nonrepudiation in Wireless Sensor Networks
In this thesis, the blockchain and smart contracts are used to provide registration,
mutual authentication, data sharing and fair nonrepudiation in wireless sensor
network. The proposed model consists of three types of nodes: coordinators, clus-
ter heads and sensor nodes. A consortium blockchain is deployed on coordinator
nodes. The smart contracts execute on coordinators to record the identities of le-
gitimate nodes. Moreover, they authenticate nodes and facilitate in data sharing
and arbitration in case of repudiation. When a sensor node communicate and ac-
cess data of any other sensor node, both nodes mutually authenticate each other.
The smart contract of data sharing and nonrepudiation is used to provide a secure
communication and data exchange between sensor nodes. Moreover, it records
the evidences during the data exchange. When the exchanged data is found il-
legitimate or the requesting sensor node denies the legitimacy of data, then an
arbitration smart contract resolves the dispute on the basis of evidences and pun-
ishes the sensor node accordingly. Additionally, the data of all the nodes is stored
on the decentralized storage called interplanetary file system. Moreover, the staller
consensus protocol is used in the proposed model to increase the efficiency and
transaction throughput. The transaction latency of the proposed system model is
approximately 81.82% lower than the proof of work based model. Moreover, the
gas consumption of the data request and data provisioning is economical.
ix
Journal Publications
1 Talha Naeem Qureshi, Nadeem Javaid, Ahmad Almorgen, Asad Ullah Khan,
Hisham Almajed and Irfan Mohiuddin, ”An Adaptive Enhanced Differential
Evolution Strategies for Topology Robustness in Internet of Things”, Inter-
national Journal of Web and Grid Services,
x
Conference Proceedings
1Asad Ullah Khan, Affaf Shahid, Fatima Tariq, Abdul Ghaffar, Abid Ja-
mal, Shahid Abbas and Nadeem Javaid, ”Enhanced Decentralized Manage-
ment of Patient-Driven Interoperability based on Blockchain”, in 14th Inter-
national Conference on Broad-Band Wireless Computing, Communication
and Applications (BWCCA)
2 Fatima Tariq, Nadeem Javaid, Maria Anwar, Abdul Rehman Janjua, Muham-
mad Haseeb Khan and Asad Ullah Khan, ”Blockchain in WSNs, VANets,
IoTs and Healthcare: A Survey”, in 34th International Conference on Web,
Artificial Intelligence and Network Applications. WAINA 2020. Advances
in Intelligent Systems and Computing
xi
TABLE OF CONTENTS
Dedication vii
Acknowledgements viii
Abstract ix
Journal Publications x
Conference Proceedings xi
List of Figures xiv
List of Tables xv
List of Algorithms xvi
List of Symbols xvii
11
1.1 Introduction ............................... 2
1.1.1 Background and motivation .................. 2
1.1.2 Wireless sensor networks and blockchain ........... 2
1.1.3 Thesis contributions ...................... 4
1.1.4 Organization of thesis ..................... 4
26
2.1 Literature review ............................ 7
2.1.1 Identity authentication and access management: ....... 7
2.1.2 Trust evaluation for malicious node detection: ........ 8
2.1.3 Trust evaluation for secure localization: ............ 9
2.1.4 Trusted and secure routing: .................. 9
2.1.5 Lightweight blockchain for wireless sensor networks: ..... 10
2.1.6 Incentive mechanisms for data storage and crowdsensing: . . 10
2.1.7 Nonrepudiation in service provisioning: ............ 11
2.1.8 Problem statement: ....................... 11
313
3.1 System model: .............................. 14
3.1.1 Key management center .................... 15
3.1.2 Coordinator ........................... 15
3.1.3 Consortium blockchain ..................... 16
3.1.4 Cluster heads .......................... 16
3.1.5 InterPlanetary file system ................... 16
3.1.6 Registration and authentication ................ 16
3.1.7 Data sharing and nonrepudiation ............... 18
xii
422
4.1 Simulation results and discussions ................... 23
527
5.1 Conclusion ................................ 28
5.2 Future work ............................... 28
629
xiii
LIST OF FIGURES
1.1 Structure of Blockchain and Block ................... 3
3.1 Blockchain based Node Authentication and Data Sharing with Non-
repudiation ............................... 14
3.2 Data Sharing and Nonrepudiation Process .............. 20
4.1 Message Size of Registration and Authentication. .......... 23
4.2 Execution Time of Registration and Authentication. ......... 24
4.3 Response Time of IPFS. ........................ 24
4.4 Average Gas Consumption on Data Request and Provision . . . . . 25
4.5 Transaction Latency of PoW and SCP. ................ 25
4.6 Average Transaction Latency of PoW and SCP ........... 26
xiv
LIST OF TABLES
3.1 Mapping Table of Identified Limitations, Proposed Solutions and
Validations ............................... 17
xv
List of Algorithms
1 The Registration Process of a Node ................... 18
2 The Authentication Process of a Node ................. 19
3 Data Sharing and Nonrepudiation ................... 21
4 Arbitration Smart Contract ....................... 21
xvi
List of Symbols
ContractdataSharing Data sharing smart contract
Contractarbitration Arbitration smart contract
Datahash Hash of data
IDcoordinator Unique ID of a coordinator
IDnode Unique ID of a node
IDCH Unique ID of a CH
Keycardnode Keycard of a node
signcoordinator Signing a message with coordinator private key
coordinatorI DPCoordinator ID of node P
IDPID of node P
CHPCH ID of node P
IDQID of node Q
CHQCH ID of node P
KeycardPKeycard of node P
T hresholdiLower limit for a feature
T hresholdjUpper limit for a feature
xvii
Chapter 1
Introduction
1
Chapter 1 Chapter 1
1.1 Introduction
In this section of thesis, the contributions and organization of thesis have been
presented.
1.1.1 Background and motivation
The Internet of Things (IoTs) is an emerging domain, which allows objects to
communicate with each other over the Internet without human interference [1].
The objects are identified using unique identifiers over the Internet. Besides, the
number of IoT connections are predicted to reach 26.9 billion by 2026 [2]. The
Wireless Sensor Networks (WSNs) are the underlying technology for the IoTs,
which consist of self organizing tiny sensor nodes [3]. These nodes have limited
computational power, energy and storage resources. The applications of WSNs
are of wide range, which include military, environment monitoring, healthcare,
home automation, etc. The examples include battlefield surveillance, assessment
of damage during battle, fire detection in a forest, psychological data of a patient
in telemonitoring, etc. There are two kinds of architectures for IoT: centralized
and distributed [4]. In former, the nodes aggregate and send data to the Base
Stations (BSs) through Cluster Heads (CHs). Whereas, in latter, the nodes send
data directly to other entities in the network. The blockchain technology is widely
used in the IoT to make it distributed. It also adds the feature of decentralization
in IoT.
1.1.2 Wireless sensor networks and blockchain
The blockchain is considered as a revolutionary technology by both industry and
research experts. It was introduced by Satoshi Nakamoto as a first trustless peer-
to-peer electronic cash system, named Bitcoin [5]. The blockchain is a peer-to-peer
network of nodes, which maintain a distributed ledger [6,7]. The distributed ledger
consists of blocks and maintains the states of accounts. Every block is connected
to its previous block through a cryptographic hash. minor change in the data of a
block changes all the hashes of succeeding blocks. The structure of the blockchain
and blocks are shown in Fig. 1.1. The initial block of the blockchain is called
genesis block. Each block contains a set of transactions that are validated by
miners. A transaction is the change in the state of an account. The transactions
are verified using the consensus mechanism. The method of achieving agreement
on the state of ledger across many miners or validators in the network is known as
consensus. Furthermore, the miners are computationally rich nodes that execute a
2Thesis by: Asad Ullah Khan
Chapter 1 Chapter 1
consensus algorithm to validate transactions and add new blocks to the blockchain.
The features of the blockchain include decentralized control, transparency, security,
auditability and immutability. These features of the blockchain make it an ideal
option for many applications, i.e., healthcare, supply chain, banking, IoTs, voting
systems, etc. Currently, there are four generations of blockchain technology [8].
Each generation introduces some new features. The smart contracts are one of
these features, which are introduced in the second generation. They are used to
add terms and condition in transactions.
...
...
______Data Blocks in Blockchain_____
Block-1 Block-2 Block-3 Block-4 Block-N
Figure 1.1: Structure of Blockchain and Block
The Blockchain is divided into four types named as private, public, consortium
and hybrid [6]. This division is done on the basis of its usage. A user can join the
public blockchain without any restriction. The Bitcoin is an application of public
blockchain. The private blockchain is restricted and only allowed user can join it.
The private blockchain is also known as permissioned blockchain. The miners or
validators are predefined in the private blockchain. The consortium blockchain in
established between two or more organizations. All the stakeholders of consortium
act as validators. Moreover, a hybrid blockchain consists of both decentralized and
centralized features.
The smart contracts are automated and enforceable code scripts that are used to
perform a certain task and executed when some predefined conditions are met [9].
Blockchain has two types of unique addresses, which include account address and
smart contract address. The smart contract is stored on the blockchain and its
state changes when it is executed. Moreover, it provides a legal agreement between
two parties without intrvention of trusted third party.
In literature, the merger of blockchain with WSNs and IoTs is carried out in dif-
ferent ways. The authors in [10] propose a secure key management scheme for
3Thesis by: Asad Ullah Khan
Chapter 1 Chapter 1
dynamic WSNs. In [4], authors propose identity authentication mechanism for
WSN based on the blockchain. The author in [11] propose a scalable access con-
trol architecture for IoT based on blockchain. A smart contract is deployed among
the network of blockchain miner nodes. The blockchain contains the access con-
trol policy for a resource of IoT for a particular node. Moreover, an access control
mechanism based on smart contract for IoT is presented in [12]. Furthermore,
a trust model for malicious node detection in WSNs based on blockchain is pro-
posed in [13]. The detection process works on the basis of delayed transmission,
forwarding rate and response time. Additionally, the authors in [14] propose a
secure routing protocol for mobile IoT to prevent intrusion. Moreover, in [15] a
blockchain based scheme for authentication of nodes, trust management and secu-
rity and privacy of data being exchanged in WSN is propose. A blockchain based
authentication mechanism for peer-to-peer IoT sensor nodes is proposed in [16].
Moreover, in [17], authors propose a nonrepudiation scheme for IoTs. It records
the evidences of service provisioning on blockchain and uses homomorphic hash
for validity of data.
1.1.3 Thesis contributions
A blockchain based trusted identity authentication, data sharing and nonrepudia-
tion scheme for WSNs is proposed in this thesis. The major contributions of this
thesis are as follows:
the mutual authentication between registered nodes is performed when they
communicate and share data,
the distributed storage of data gathered by IoT nodes and sharing of the
data using smart contract and blockchain is ensured,
a nonrepudiation scheme using smart contract for data exchange between
data owner and requester IoT nodes is proposed and
the Stellar Consensus Protocol (SCP), is used in the proposed system model
to provide high transaction throughput.
1.1.4 Organization of thesis
The remainder of the thesis is organized as follows: related studies are presented
in Chapter 2. System model along with proposed methodology are demonstrated
in Chapter 3. Chapter 4describes the simulation results of our proposed schemes.
4Thesis by: Asad Ullah Khan
Chapter 1 Chapter 1
Finally, the findings of this work along with future directions are presented in
Chapter 5.
5Thesis by: Asad Ullah Khan
Chapter 2
Literature review and problem statement
6
Chapter 2 Chapter 2
2.1 Literature review
The literature review of related papers is presented in this section. The papers
are categorized according to the limitations they addressed.
2.1.1 Identity authentication and access management:
The current identity authentication mechanisms for IoT nodes rely on trusted
third party and are vulnerable to a single point of failure. Moreover, the tradi-
tional architectures proposed for access management of IoT devices are based on
centralized models, which makes it very hard to manage a large number of devices
deployed globally. These architectures are heavyweight for IoT scenarios [4]. A
centralized BS in dynamic WSNs can be easily targeted during key management.
Moreover, current cryptographic approaches have scalability, storage, high com-
putational and communication overhead issues in WSNs [10]. Moreover, in [18],
a blockchain based energy and data sharing mechanism is proposed for electric
vehicles. The IoT devices are lightweight in terms of computational power and
cannot perform the validation of access rights [11]. In [4], the use of local and
global blockchains increases the complexity of the system. Moreover, it leads to
the storage issue in CH. The Delegated Proof of Stake (DPoS) consensus mech-
anism used in [10] is not fully decentralized. An attacker can easily perform the
51% attack against miner nodes. The number of witnesses and network speed is
negatively correlated.
The Industrial IoTs (IIoTs) rely on a centralized architecture, which leads to the
single point of failure. The sensors and their collected data in IIoTs need to be
protected against different attacks [12]. The authors in [19] propose a data sharing
and access control mechanism for IoT devices. The privacy of sensors, security
of exchanged data, identity authentication and trust management are issues that
need to be addressed [15,20]. The devices of IoT environment are vulnerable to
various security threats, i.e., confidentiality, integrity, availability, etc. The already
proposed authentication protocols tackle specific attacks [16].
The lack of traceability and transparency in the process of data exchange between
IoT devices is observed in [11]. The response of the miners to clients’ requests
makes the system more complex and results in high latency while fetching ac-
cess control information from blockchain. The management hub in the proposed
model is vulnerable to spoofing, information disclosure, denial of services, tamper-
ing, repudiation and elevation of privileges. In [12], each access control contract
7Thesis by: Asad Ullah Khan
Chapter 2 Chapter 2
implements single access control method and is used between single subject and
object pair, which increases the complexity and deployment cost of the system.
The misbehavior of the subject is evaluated on the basis of frequent resource access
request, which is not a sufficient attribute. A subject may repudiate that the file
or service provided is not legitimate.
The sensor nodes in WSNs are resource constrained devices and can be controlled
by attacker easily. The use of master key in [15] as an identifier for node or services
leads to the security issues of the node. In the proposed model of [16], every node
in the network needs to store identification data of all the nodes. The storage
problem occurs in an IoT network when a lightweight node stores huge amount of
data and the network contains millions of devices. In the process of authentication,
a source node might be authentic. However, a malicious intermediary node adds
incorrect data to the event, which results in the failure of authentication process.
The mechanism for trust factor evaluation is not added in the proposed framework
of [20]. Only the registration of users and sensors is done. If a node is registered,
then it is trusted. The reputation of nodes needs to be recorded in order to detect
malicious or misbehaving nodes after registration.
2.1.2 Trust evaluation for malicious node detection:
The WSN is a key technology in the development of IoT and supports its core
functionality. The malicious node detection models do not assure impartiality
and traceability of the process [13]. The centralized models are inefficient due to
the high cost of computation, storage, single point of failure and latency. The
fog nodes in distributed attack detection model need sufficient amount of data
to detect attacks efficiently, which might be impossible due to limited number of
devices or privacy of the connected devices [21]. In [22], an edge enabled secure
and intelligent computing model is proposed for smart cities. In [23,24], authors
proposed a fault detection mechanism to intelligently detect faults in WSN.
The consensus mechanism used in the proposed scheme of [13] is Proof of Work
(PoW), which requires high computational power and is not compatible with con-
sortium blockchain. The default consensus mechanism used by Ethereum is PoW,
which takes 5 to 10 minutes in transaction verification. The complexity and com-
putational overhead in [21] result in high response time. Moreover, the evidence
against malicious node is not stored for traceability. The failure of fog node blocks
the traffic from benign nodes.
8Thesis by: Asad Ullah Khan
Chapter 2 Chapter 2
2.1.3 Trust evaluation for secure localization:
It is difficult to find the precise location of unknown nodes in the distrusted envi-
ronment. The beacon nodes’ credibility during localization process in WSNs is a
challenging issue [25]. The range-free localization process in WSNs does not require
any special hardware, which leads to errors due to malicious nodes in the network.
The malicious nodes provide wrong location information during the localization
process. The traditional localization schemes for WSNs rely on centralized and
trusted entity, which leads to the single point of failure [26]. Moreover, in [25],
the feedback based trust value is not utilized in the aggregated trust value of a
node. The trust evaluation based on Bayesian statistics, reinforcement learning
and maximum likelihood estimation must be tested. The blockchain is utilized as
a trust management. The parameters used to evaluate trust of the beacon nodes
in [26] are very limited. The behavior and data trust also need to be used for the
evaluation of final trust value.
2.1.4 Trusted and secure routing:
The selfish behavior of upstream nodes during data transmission is not discussed
in [14]. In [27], the authors propose a blockchain based service provisioning scheme
and an incentive mechanism for lightweight clients. Furthermore, in [28], authors
proposed a secure and reliable routing potocol for underwater WSN. If there are
selfish nodes in the routing, as a result the throughput of the network decreases.
The energy of a sensor node dissipates on checking route and updating the rout-
ing table on each request [29]. In [30], the authors propose a secure scheduling
mechanism of charging using blockchain. The proposed system model is not de-
centralized and no consensus is developed among nodes. The parameters used to
discover and select the trusted route are limited. The consensus mechanism used
in the proposed model of [31] is Proof of Authority (PoA), which makes the system
somehow centralized. In the proposed routing protocol of [32,33], the gateway
agent coordinates with sensor nodes and manages keys for nodes in a centralized
manner. If gateway fails, the clusters connected to it go offline. The levels are
defined for nodes and these levels are used in data forwarding. If a node does not
send acknowledgment message for a forwarded packet, it re-transmits that packet.
It results in early death of nodes and decreases the network life time. In routing
protocol of [34], the IoTs consist of resource constrained devices, which are unable
to perform PoW consensus mechanism. Moreover, in [35], the authors proposed
an efficient routing scheme for underwater WSNs.
9Thesis by: Asad Ullah Khan
Chapter 2 Chapter 2
2.1.5 Lightweight blockchain for wireless sensor networks:
In WSNs, the participants of the network have limited resources, i.e., computation,
power, storage, etc., [36]. Due to these constraints, the sensor nodes are unable
to perform the resource intensive task of mining. All the nodes need to be con-
nected according to the structure of blockchain, which is not possible and causes
storage issue. Moreover, blockchain technology requires high resources in terms
of electricity, storage and computation [37]. The IIoTs comprise of heterogeneous
devices and use of PoW in blockchain leads to the centralization of computing
power because some peers increase their computing power with time. The size of
the ledger increases with time, which leads to the storage issue in the IIoTs.
The blockchain based emerging protocols are appropriate to provide interaction
between resource constrained IoT devices [38]. The scalability issue arises due
to high latency, mobility, security, privacy, bandwidth and single point of failure
[39]. The data in Information-Centric Network (ICN) based WSNs need to be
shared as much as possible. However, in many scenarios, the identical data is
vulnerable to privacy issues [40]. The proposed techniques in literature only focus
on the security prevention and malicious attacks. However, no caching technique
is proposed in ICN based WSNs to investigate the behavior of nodes.
The aggregation protocol in the proposed scheme reduces the communication over-
head [38]. An edge server act as a centralized entity in the proposed architecture
[39]. In case of edge server failure, the whole public infrastructure is disconnected
from core network. The transaction throughput of the proposed system is almost
double to that of the Bitcoin, which is not suitable for applications where real
time response is required.
2.1.6 Incentive mechanisms for data storage and crowd-
sensing:
The data storage is one of the main constraints in WSN nodes [41]. When a node
in WSN behave selfishly and denies to store data, it affects the normal operation
of the network. The traditional IoT based monitoring systems for the quality
of frozen shellfish rely on a centralized authority and the information about the
quality of shellfish gets tampered [42]. The traditional incentive mechanisms do
not provide privacy protection for users in crowdsensing networks [43]. Moreover,
in [44,45], a blockchain based incentive mechanism is proposed for crowd sensing
network to encourage users to collect and share data.
10 Thesis by: Asad Ullah Khan
Chapter 2 Chapter 2
A trusted third party is used to verify the integrity of data blocks, which elimi-
nates the decentralization feature of blockchain technology [41]. The size of the
distributed ledger in the blockchain increases with time. The storage of ordinary
node is very limited, so it is very difficult to store a distributed ledger. The number
of experiments for the validation of proposed scheme needs to be increased [42].
2.1.7 Nonrepudiation in service provisioning:
The malicious service providers or users in IIoTs repudiate from the provisioning
or utilization of a service in an untrusted environment [17]. Moreover, the use of a
trusted third party in traditional nonrepudiation schemes makes them ineffective
and do not provide true fairness. The dispute resolution mechanisms in traditional
nonrepudiation schemes are not effective and suffer from weak fairness, which as
a result do not guarantee trust. In [46], authors proposed a blockchain based for
dynamic pricing in IIoTs. Moreover, in [47], authors proposed a blockchain based
secure service provisioning mechanism of IoTs network with incentive and fair
payment mechanisms. When a client requests for a service, the crypto collectible
tokens are transferred to the address of the service provider [17]. If the service
provider is malicious, than client losses these tokens. The credibility of service
providers must be recorded based on clients’ feedback, behavior and quality of
service. Moreover, the homomorphic hash function is used in the proposed scheme,
which is very slow.
2.1.8 Problem statement:
The authors in [4] propose a scheme for secure key management in dynamic WSNs.
The DPoS consensus mechanism is not fully decentralized and attackers can easily
organize 51% attack against miner nodes. Moreover, the entire public key for a
node cannot be formed using identity information in certificateless cryptography.
A malicious node generates public key repeatedly to perform a sybil attack. A
blockchain based identity authentication mechanism for multi-WSNs is proposed
in [4]. The workload on CHs increases and results in energy depletion due to the
use of local and global blockchains. The authors in [17] propose a blockchain based
nonrepudiation scheme for service provisioning in IIoTs. However, the homomor-
phic hash function is very slow and has high computational overhead, which in
not possible in IIoT scenarios. Moreover, in the verification of information, digital
signatures or hashes need to be delivered separately using a secure channel. The
authors in [11] proposed an access management architecture based on blockchain
for IoT scenarios. The blockchain is used to store access control policies. However,
11 Thesis by: Asad Ullah Khan
Chapter 2 Chapter 2
there is lack of traceability and transparency in the process of data exchange be-
tween IoT devices. There is high latency while fetching access control information
from blockchain.
12 Thesis by: Asad Ullah Khan
Chapter 3
System model and proposed methodology
13
Chapter 3 Chapter 3
Figure 3.1: Blockchain based Node Authentication and Data Sharing with
Nonrepudiation
3.1 System model:
In the proposed system model, blockchain is utilized to achieve authentication,
data sharing and nonrepudiation between multiple WSNs, as shown in Fig. 3.1.
A consortium blockchain is deployed between coordinators and CHs nodes. The
identity information of coordinators and CHs is stored on the blockchain. The co-
ordinators act as mining nodes in the proposed system model. The smart contracts
for registration and authentication, data sharing and arbitration are deployed on
the coordinators. Whenever a new sensor node wants to join the network, it needs
to be trusted by a CHs node. If the node is trusted, it is registered in the network.
Otherwise, the registration fails. The proposed model provides mutual authentica-
tion when sensor nodes want to communicate with each other. The ambient data
generated by the senor nodes is stored on the distributed storage called IPFS.
In the proposed system model, each time when a node wants to access the data
of a particular node, the contractdataSharing is triggered. The requesting node
14 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
sends data request and crypto tokens to the smart contract. The data owner node
uploads data to the IPFS, sends both hash of location and data to the smart
contract. If the exchanged data is valid, the smart contract releases the crypto
tokens to the wallet address of the owner. In case of a dispute between requester
and owner node, the contractdataS haring sends the crypto tokens, address of the
data and hash of data to the contractarbitration. The contractarbitration provides
judgment and resolves the repudiation issue.
There are different consensus protocols, which are used to reach an agreement
in the decentralized network. Both Bitcoin and Ethereum use PoW, which is
computationally expensive. The other consensus protocols implemented some form
of Byzantine Fault Tolerance (BFT). The BFT is faster and cheaper than PoW,
however, it sacrifices the decentralization. In [48], the authors proposed SCP as
a decentralized alternative to the BFT. It is also known as Federated Byzantine
Agreement (FBA). The SCP is an open membership consensus protocol, which
means anyone can join and leave the consensus process. In SCP, each validator
decides which other validators they trust. The list of trusted validators is called
a quorum slice. The quorum slices of the validators overlap to form a quorum or
network-wide consensus for a transaction. The transaction latency of the SCP is
very low as compared to PoW consensus protocol. Moreover, the SCP is more
secure than PoW and is not vulnerable to 51% attack. Due to the low latency and
high throughput of SCP, the SCP is used in the proposed model.
The details of all the entities of the proposed system model are given below.
3.1.1 Key management center
The KMC is used to generate public and private key pairs for sensor nodes and CHs
during the registration phase. The Elliptic Curve Cryptography (ECC) is used at
KMC for public and private key pair generation. The public keys of all nodes are
stored in the blockchain to overcome the issue of certificate-less cryptography. The
generated public key is then stored on the blockchain to authenticate the sensor
node. Moreover, the private key is shared with the corresponding node.
3.1.2 Coordinator
The coordinator is a computationally rich node of the network. The consortium
blockchain and smart contracts are deployed on these nodes. The coordinators
act as miners and use SCP to verify the transactions. Therefore, the operations
performed by the KMC are continuously monitored by the coordinators to prevent
15 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
any malicious activity. If KMC tries to add a public key for any node that is
already registered in the network, the registration smart contract restricts KMC
from registering the node.
3.1.3 Consortium blockchain
In the proposed system model, the consortium blockchain is deployed on the co-
ordinators. All the entities of the system model are registered on the blockchain.
The coordinators act as miner nodes in the consortium blockchain while CHs re-
quest data from blockchain using smart contracts. In [4], the local and global
blockchains are deployed on the CHs and BSs, respectively. It reduces the net-
work lifetime and increases the computation overhead. The usage of consortium
blockchain reduces the computing overhead of the CHs, which increases the net-
work lifetime. The main features of the consortium blockchain are scalability, high
transaction throughput, low energy consumption and low transaction cost.
3.1.4 Cluster heads
The CHs are selected on the basis of high residual energy. The CHs are also
part of the blockchain network, however, they cannot validate the transactions.
Additionally, CHs requests blockchain for authentication of sensor nodes on the
behalf of their cluster members. Moreover, the sensor nodes in a cluster are trusted
by CHs. The sensor nodes are resource constrained therefore, they forward the
request of registration to KMC through CH.
3.1.5 InterPlanetary file system
The IPFS is used for the distributed storage of data generated by the sensor nodes
and it reduces the storage overhead on blockchain. It handles single point of failure
issue in data storage. Furthermore, it reduces the bandwidth consumption of the
network and efficiently stores data without duplication. The sensor nodes upload
data to IPFS upon data request by another node.
3.1.6 Registration and authentication
The initialization of identity information of nodes is the responsibility of KMC. The
KMC generates a unique identity of nodes by hashing their MAC address, IDnode =
keccak(MAC ) including itself. The KMC then generates a pair of public P ubnode
and private Sknode keys for all the entities of the WSN including itself using ECC.
The integrity of messages is verified using these keys during the registration and
16 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
Table 3.1: Mapping Table of Identified Limitations, Proposed Solutions and
Validations
Limitations Identi-
fied
Proposed Solutions Validations
L1: Computation over-
head on CH with the us-
age of public and private
blockchains [4]
S1: Consortium
blockchain is used
V1: Message size and ex-
ecution time during reg-
istration and authentica-
tion process (4.1,4.2)
L2: DPoS is not fully de-
centralized and vulnera-
ble to 51% attack [10]
S2: SCP is used V2: Transaction latency
of SCP (4.5,4.6)
L3: High latency while
fetching data access poli-
cies from blockchain [11]
S3: Smart contract pro-
vide data sharing be-
tween nodes
L4: Public key does
not map to the identity
in certificate-less cryp-
tography [10]
S4: Identity based cryp-
tography is used
V3: Message size of reg-
istration and authentica-
tion (4.1)
L5: Homomorphic hash-
ing is slow and requires
high computation power
[17]
S5: Smart contracts and
IPFS for data exchange
and nonrepudiation
V4: Response time of
IPFS and transaction la-
tency of PoW and SCP
(4.3,4.5)
authentication process. The Keycard of each node contains the unique identity of
that node IDnode,I Dcoordinator is coordinator’s identity where the node is located
and a signed message signcoordinator (keccak(I Dcoordinator ||IDnode )). The Elliptic
Curve Digital Signature Algorithm (ECDSA) is used for signing a message.
In the process of CH registration, the node sends IDcoordinator ,IDC H and Keycard
to the smart contract, deployed on the coordinators. The smart contract verifies
the data of CH and checks if the identity of a node already exists in the blockchain.
The CH is registered in the blockchain after all the required verifications by smart
contract. The registration request of a sensor node includes I Dcoor dinator,IDnode,
IDC H , and Keycard. Furthermore, in the registration process of a sensor node,
the smart contract verifies the data sent in the request and checks if the sensor
node is already registered under any other CH. The sensor node is successfully
17 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
Algorithm 1: The Registration Process of a Node
Input: IDcoordinator ,IDnode ,IDC H ,Keycardnode
Output: Successfull registration message
1if isExists(I Dnode) == T rue then
2return(F alse,error);
3else if isV erif ied(I Dcoordinator ) == F alse then
4return(F alse,error);
5else if isV erif ied(I DCH ) == F al se then
6return(F alse,error);
7else if isV erif ied(K eycardnode) == F alse then
8return(F alse,error);
9else
10 return(T rue, Node registered successfully);
11 end
registered in the blockchain after all the verifications are done. Algorithm 1shows
the process of registration.
A secure channel needs to be created before two sensor nodes interact with each
other. When a node Pwants to communicate with a node Q, it sends an in-
teraction request to the contractdataSharing through CH. The request includes
coordinatorI DP,IDP,CHP,IDQ,CHQand K eycardP. The smart contract
verifies the identity information sent in the request. The CHs of both Pand Q
nodes send a request of authentication to the smart contract. The sensor node
establishes a secure connection when both Pand Qbelong to the same cluster.
The smart contract sends the authentication message to the CHPand CHQ, if
node Pand Qbelong to different clusters. The process of authentication between
sensor nodes is shown in Algorithm 2.
3.1.7 Data sharing and nonrepudiation
The process of data sharing and nonrepudiation is shown in Fig. 3.2. In the pro-
posed system model, the ECDSA is utilized during the transactions. The ECDSA
based digital signatures of the requesting node and data owner are recorded on
the blockchain in each transaction. The digital signatures make it impossible for
either requester or owner to deny any of their actions. The digital evidences of
each step are recorded on the blockchain to achieve transperancy and fairness in
the nonrepudiation process. The Algorithm 3shows the steps of data sharing and
nonrepudiation. The details of these steps are as follows.
Step 1: The data requesting node sends request to the contractdataS haring of
a particular node. A transaction is recorded on the blockchain for this request,
18 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
Algorithm 2: The Authentication Process of a Node
Input: coordinatorI DP,IDP,CHP,IDQ,CHQ,K eycardP
Output: Sends authentication message to CHPand C HQ
1if isExists(I DP) == F alse then
2return(F alse,error);
3else if isExists(I DQ) == F alse then
4return(F alse,error);
5else if isAlive(I DP) == F alse then
6return(F alse,error);
7else if isAlive(I DQ) == F alse then
8return(F alse,error);
9else if C HP== CHQthen
10 Node Pand Qinteract in a secure manner;
11 else
12 Smart contract sends message of authentication to the CHPand CHQ;
13 end
which contains the digital signature of the requesting node. The request message
contains the features of data required by the sensor node, the identity informa-
tion of owner node and crypto tokens. The crypto tokens are deposited to the
contractdataSharing address for security purpose.
Step 2: The contractdataS haring first checks for the existence of data requester and
owner nodes in the blockchain. The data access request is rejected if the node is
not registered in the blockchain. Otherwise, the contractdataShar ing sends features
of the requested data to the owner.
Step 3: The owner node uploads the requested data to the IPFS and stores the
hash returned by the IPFS. This address is later used to access the data from
IPFS.
Step 4: The owner node sends the hash, address and crypto tokens to the smart
contract deployement address. The tokens of owner are deposited to the smart
contract to create a trusted, secure and reliable environment for data exchange.
Moreover, the owner’s response is recorded on the blockchain as a transaction with
digital signature.
Step 5: The contractdataShar ing sends the IPFS address and hash of the data to
requesting sensor node.
Step 6: In this step, the requesting node accesses the data from IPFS using its
address. The data accessing node confirms legitimacy of data; otherwise, step 7
and 8 are executed.
Step 7: In case of illegitimate data, the requesting node reports to the contractdataSharing .
Two possibilities exist here: either the requesting node denies that the exchanged
19 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
Data Sharing
Smart Contract
Data Requesting
Node
Data Owner
Node
Arbitration Smart
Contract
Data Verification
Reward and
punishment for
nodes and
store result in
blockchain
1. Sends data request and tokens to
smart contract
6. Accesses data
5. Sends hash of the requested data
3. Owner uploads data and IPFS returns
its hash
4.Sends tokens and IPFS address
of data
2. Sends features of the requested data
7. Reports if data is illegitimate
8. Sends request, crypto tokens and IPFS
address of data
Figure 3.2: Data Sharing and Nonrepudiation Process
data is not legitimate or the data owner shared illegitimate data.
Step 8: In this step, the contractdataShar ing invokes the contractarbitration. The
contractdataSharing shares the requesting node information, owner information,
crypto tokens and IPFS address of the data with contractarbitration . The contractarbitration
checks the features of requesting node and the data uploaded on the IPFS and
makes a decision regarding the dispute. At the end, either the data owner or the
requesting node is punished and the crypto tokens are transferred to the other
node. Moreover, the decision of the arbitration is recorded on the blockchain and
a malicious node is blocked from the network for a specific time.
The contractarbitration is invoked when a dispute occurs between data requesting
and the owner node. There are two possibilities: either the requesting node or
owner node is malicious. The contractarbitration as shown in Algorithm 4checks
the data uploaded to the IPFS with the features requested by the data requester.
The contractarbitration checks if the data is between a given threshold or not. The
owner is punished and the tokens are transferred to the requester’s wallet address
20 Thesis by: Asad Ullah Khan
Chapter 3 Chapter 3
Algorithm 3: Data Sharing and Nonrepudiation
Input: Request, Tokens
Output: Transfer tokens to the Owner
1Requester sends Request and tokens to the contractdataSharing ;
2if isExists(I Dnode) == T rue then
3ContractdataSharing sends features to Owner;
4Owner uploads data to I P F S;
5I P F S returns the hash;
6Owner sends hash and tokens to contractdataS haring ;
7Requester accesses data from I P F S;
8if Data is illegitimate then
9Requester reports contractdataSharing ;
10 ContractdataSharing sends Request, tokens and Datahash to
Contractarbitration;
11 else
12 ContractdataSharing sends tokens to the Owner;
13 end
14 else
15 returns(F alse, er ror);
16 end
Algorithm 4: Arbitration Smart Contract
Input: Request, Tokens, Datahash
Output: Arbitration Result
1if Data T hresholdiAND Data T hr esholdjthen
2Punishes Requester and sends token to Owner;
3Blocks Requester from getting services;
4else
5Punishs Owner and sends token to Requester;
6Blocks Owner from service provisioning;
7end
when the data is illegitimate. Otherwise, the requester is punished and the crypto
tokens are transferred to the owner’s wallet address.
21 Thesis by: Asad Ullah Khan
Chapter 4
Simulation results and discussions
22
Chapter 4 Chapter 4
4.1 Simulation results and discussions
The performance of the proposed system model is validated through simulations
and presented in this section. The Intel Core-i5 laptop with 6GB RAM and
2.5GHz processor is used to perform simulations. The Ethereum blockchain is
used for simulations and smart contracts are written in Solidity. The web3.py
library is used to provide interaction between user (requester or owner) and smart
contracts and ipfshttpclient is used to store files on IPFS. The efficiency of the
proposed system model is evaluated in terms of average transaction latency, aver-
age gas consumption, response time of IPFS, message size and execution time of
registration and authentication phase. The message size of sensor nodes and CHs
during registration and authentication phase is shown in Fig. 4.1. The message
size of the sensor nodes decides the lifetime of a network and a sensor node. The
message size of the sensor nodes is smaller than the message size of CH nodes. The
reason is that the sensor nodes only interact with CH; however, CH communicates
with both sensor nodes and coordinators.
Authentication Phase Registration Phase
400
350
300
250
200
150
100
50
0
Message Size (bytes)
Sensor Node
Cluster Head Node
Figure 4.1: Message Size of Registration and Authentication.
The execution time for authentication and registration of sensor nodes and CHs
is shown in Fig. 4.2. The registration time of ordinary node is high because the
validity of a CH and a coordinator is achieved. Moreover, the execution time
for authentication of CHs and sensor node is almost equal as it requires node id
for verification. In Fig. 4.3, the response time of IPFS during data upload and
retrieval is shown. The data files of 5MB to 35MB are uploaded to the IPFS.
23 Thesis by: Asad Ullah Khan
Chapter 4 Chapter 4
Authentication Phase Registration Phase
4
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.5
0
Execution Time (sec )
Sensor Node
Cluster Head Node
Figure 4.2: Execution Time of Registration and Authentication.
The response time of IPFS increases with the increase in data. Moreover, the
response time during data retrieval from IPFS also increases with the increase in
file size. The data is stored in chunks on different IPFS nodes and data retrieval
from these nodes require more time. The response time during data upload is high
as compared to data retrieval because of the hashing of the content.
5 10 15 20 25 30 35
Data Size (MB)
Response Time (sec)
4.5
4
3.5
3
2.5
2
Data Upload
Data Retrieval
Figure 4.3: Response Time of IPFS.
24 Thesis by: Asad Ullah Khan
Chapter 4 Chapter 4
Data Request
10
4
Request
Provision
Data Provision
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Average Gas Consumption (Gwei)
Figure 4.4: Average Gas Consumption on Data Request and Provision
The gas consumption shows the amount of cryptocurrency required to perform a
multiple operations based transaction in the Ethereum ecosystem. In Fig. 4.4,
the gas consumption of data request and provision is depicted. The cost of data
request and data provision in PoW based system is 45756 gwei and 46002 gwei,
respectively. The execution cost of both data request and data provision functions
in terms of dollars is 0.10 USD (1 Ether 2212.37 USD).
PoW
SCP
0
10
15
20
25
Latency (sec)
5
SCP Latency
PoW Latency
Figure 4.5: Transaction Latency of PoW and SCP.
25 Thesis by: Asad Ullah Khan
Chapter 4 Chapter 4
The transaction latency in blockchain network is referred to the time taken from
the submission of a transaction to the addition into a block. The average trans-
action latency of PoW and SCP based system as shown in Fig. 4.5 and Fig. 4.6
is approximately 22 sec and 4 sec, respectively. The proposed SCP based scheme
is approximately 81.82% more efficient than PoW based scheme in terms of trans-
action latency. The transaction latency of the proposed system model based on
consortium blockchain with SCP consensus mechanism is stable in both data pro-
visioning and arbitration process.
1 2 3 4 5 6 7 8 9 10
Day
Average Transaction Latency (sec)
8
4
10
12
14
16
18
20
22
24
6
SCP Latency
PoW Latency
Figure 4.6: Average Transaction Latency of PoW and SCP
26 Thesis by: Asad Ullah Khan
Chapter 5
Conclusion and future work
27
Chapter 5 Chapter 5
5.1 Conclusion
In this thesis, the blockchain and smart contract based registration and authentica-
tion mechanism for sensor nodes is proposed. The consortium blockchain deployed
on coordinators is used to store the transactions and smart contracts. The smart
contracts are used to provide the identity authentication, data sharing, nonrepu-
diation and arbitration. A huge amount of data is generated due to the presence
of a large number of nodes. Moreover, the data collected by sensor nodes is stored
on the IPFS. The nonrepudiation of the data stored on the IPFS is provided by
contractdataSharing . In case of a dispute between the data requester and data owner,
the contractarbitration is invoked. The contractarbitration provides decision on the dis-
pute and punishes the owner or requester. Moreover, the SCP is used to achieve
consensus between coordinators. The efficiency of the proposed system model is
validated in terms of message size and execution time during registration and au-
thentication, average transaction latency, average gas consumption and response
time of IPFS. The transaction latency of the proposed system model is approxi-
mately 81.82% lower than PoW based model. Moreover, the gas consumption of
the data request and data provisioning is also stable and economical.
5.2 Future work
In future, we will add reputation of service providers and requesters. This rep-
utation will be updated on the basis of feedback and behavior. Moreover, the
performance of proposed scheme will be tested in real time.
28 Thesis by: Asad Ullah Khan
Chapter 6
References
29
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