Data Sharing System Integrating Access Control
Mechanism using Blockchain-Based Smart Contracts
for IoT Devices
Tanzeela Sultana 1, Ahmad Almogren 2,* , Mariam Akbar 1, Mansour Zuair 3, Ibrar Ullah 4
and Nadeem Javaid 1
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan;
firstname.lastname@example.org (T.S.); email@example.com (M.A.); firstname.lastname@example.org (N.J.)
2Computer Science Department, College of Computer and Information Sciences, King Saud University,
Riyadh 11543, Saudi Arabia
Computer Engineering Department, College of Computer and Information Sciences, King Saud University,
Riyadh 11543, Saudi Arabia; email@example.com
4Faculty of Electrical and Computer Engineering, University of Engineering and Technology Peshawar,
Bannu 28100, Pakistan; firstname.lastname@example.org
Received: 7 November 2019; Accepted: 6 January 2020; Published: 9 January 2020
In this paper, a blockchain-based data sharing and access control system is proposed, for
communication between the Internet of Things (IoT) devices. The proposed system is intended to
overcome the issues related to trust and authentication for access control in IoT networks. Moreover,
the objectives of the system are to achieve trustfulness, authorization, and authentication for data
sharing in IoT networks. Multiple smart contracts such as Access Control Contract (ACC), Register
Contract (RC), and Judge Contract (JC) are used to provide efﬁcient access control management.
Where ACC manages overall access control of the system, and RC is used to authenticate users in the
system, JC implements the behavior judging method for detecting misbehavior of a subject (i.e., user).
After the misbehavior detection, a penalty is deﬁned for that subject. Several permission levels are set
for IoT devices’ users to share services with others. In the end, performance of the proposed system is
analyzed by calculating cost consumption rate of smart contracts and their functions. A comparison
is made between existing and proposed systems. Results show that the proposed system is efﬁcient
in terms of cost. The overall execution cost of the system is 6,900,000 gas units and the transaction
cost is 5,200,000 gas units.
blockchain; Internet of Things; data sharing; access control; smart contracts; trustfulness;
Development of the Internet leads to a growing number of devices. The devices are more likely
to connect with each other due to the rise of networking and communication technologies (i.e., wiﬁ,
ZigBee, etc.). The connection of devices has fastened the growth of the Internet of Things (IoT)
networks. IoT is a promising technology that integrates physical world with the Internet. The IoT
network is deﬁned as; “the connection of Internet enabled devices that share data, information, and
resources with each other”. The connection among IoT devices is established without any human
intervention. One of the main aspects of IoT is to share data, information, and resources among devices.
The connection of devices extends the applications of IoT networks in various ﬁelds. Some of the
applications of IoT networks include: vehicular networks (where cars are integrated with an existing
Appl. Sci. 2020,10, 488; doi:10.3390/app10020488 www.mdpi.com/journal/applsci
Appl. Sci. 2020,10, 488 2 of 21
entertainment, trafﬁc, and navigation systems), home automation (i.e., smart homes), healthcare
(i.e., health data sharing), supply chain management (asset tracking, forecasting, vendor relations,
connected ﬂeets, etc.), security systems (i.e., sensors, buzzer connected), and many others [
of the aforementioned applications, IoT devices are connected globally and the ratio of connectivity is
The connection of devices brings several challenges in the networks. Some of the challenges are:
inefﬁcient management of data, unauthorized access, malicious attacks, single point of failure through
centralization, and many others [
]. The IoT devices consist of sensitive data; therefore, an efﬁcient
management of networks is highly required. As the IoT devices’ data is of huge amount; so, centralized
storage systems, i.e., cloud and fog are used to store that data. The cloud has the ability to process
a huge amount of data in rapid manners. Moreover, it achieves accuracy, efﬁciency, and speed for
data processing. Besides the storage and processing advantages of networks, cloud and fog also bring
latency, security, and privacy issues. However, unauthorized access of data is the major issue [3–5].
Many strategies are presented in the literature to overcome issues in the IoT networks.
The challenging tasks of IoT networks are efﬁcient data sharing and authorized access control. There
are several strategies to manage data sharing and authorized access control of data. Such strategies
are based on traditional models, which include: Attribute-Based Access Control (ABAC), Role-Based
Access Control (RBAC), and many others. However, there are many limitations in the traditional
systems, such as: single point of failure through centralization, untrustworthiness, unauthorized access
to data, etc., [6,7].
Therefore, blockchain technology is integrated with access control and data sharing mechanisms,
to eliminate the issues in traditional schemes. Blockchain provides solutions to many problems
that are more effective to provide data integrity, fairness, authenticity, security, and distribution [
Furthermore, Section 1.1 provides an in-depth understanding of blockchain technology.
The idea of blockchain is proposed by Satoshi Nakamoto in 2008 via a white paper. It is introduced
as an underlying technology for bitcoin cryptocurrency. Bitcoin is also known as the ﬁrst application of
blockchain. The blockchain technology is used to secure the ﬁnancial transactions of digital currency
by eliminating the central authority. Blockchain is a networking technology, where nodes are directly
connected to each other in a Peer-to-Peer (P2P) manner. It has eliminated the concept of centralization
through consensus mechanisms. Decisions in the network are made after the consensus among all
nodes. Furthermore, blockchain is also known as a distributed ledger technology. Ledger contains the
record of transactions made in the network, and is distributed over all nodes. In Figure 1, the basic
blockchain structure is presented.
Hash of Previous
Hash of Previous
Block 2 Block 3
Hash of Previous
Hash of Previous
Figure 1. Basic Blockchain Structure.
Appl. Sci. 2020,10, 488 3 of 21
Moreover, blockchain contains several features that enhance its signiﬁcance over traditional
transaction systems. The features of blockchain that make it more efﬁcient and reliable are security,
scalability, immutability, and anonymity [
]. The aforementioned properties and features of
blockchain technology increase its demand signiﬁcantly. Therefore, the applications of blockchain are
also increased in various ﬁelds. For example: it is being integrated with IoT and vehicular networks [
Other uses of blockchain technology are: Artiﬁcial Intelligence (AI), economy, transportation, health,
identity management, supply chain management, and smart contract services .
Major features of blockchain that make it distinct from existing systems are:
The ledger in blockchain records all transactions that are made in the networks. Blockchain
is known as a distributed ledger technology, in which ledger is distributed over all network nodes.
Transactions include: ﬁnancial, health-care, business-related transactions, etc.
1.1.2. Smart Contract
Blockchain technology eliminates the involvement of third-party through smart contracts. Smart
contracts are written computer programs. All rules for transaction between two parties are deﬁned in
smart contracts. The transaction cannot be done until all the agreements in smart contract are fulﬁlled.
Whenever any transaction is to be made in the blockchain-based networks, smart contract is triggered
1.1.3. Consensus Mechanism
The ledger is distributed over all nodes in the network. It is necessary to keep the record
synchronized. To maintain data consistency in blockchain, several consensus mechanisms are used.
The concept of single authority is also eliminated by consensus mechanisms. Decisions in the network
are made after agreement between majority nodes. Moreover, the consensus mechanisms used in
blockchain technology include: Proof of Work (PoW), Proof of Concept (PoC), Proof of Authority
(PoA), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), etc.
Cryptography brings security to the whole network. The cryptographic techniques are used
in blockchain to make the data and transactions more secure and tamper-proof. Blockchain uses
several cryptographic methods such as: hashing techniques (Secure Hash Algorithm-256 (SHA-256),
Keccak256), digital signatures (Rivest-Shamir-Adleman (RSA), Digital Signature Algorithm (DSA),
Elliptic Curve Digital Signature Algorithm (ECDSA)), and encryption techniques (Attribute-Based
Encryption (ABE), Data Encryption Standard (DES)), and many others [15,16].
Due to the rapid progress of blockchain technology, its usage is increasing. It is used in almost
every ﬁeld. Therefore, the blockchain-based data sharing and access control system is used to provide
authenticated and trustworthy sharing and access control of IoT devices’ data. Sharing of data is
done between two peers, which are: subject (request sender) and object (service provider). Smart
contracts are used to manage data sharing and access control. Furthermore, behavior of the users is
also monitored. Additionally, some permission levels are deﬁned for the subject to access services of
A lot of work was done in the literature for efﬁcient use and communication of devices in
the IoT networks. Several strategies use blockchain technology for data sharing and access control
management among devices. Some work only considered access control, whereas others focused
on sharing of data and services. The work in [
] is based on access control management of IoT
Appl. Sci. 2020,10, 488 4 of 21
devices’ data. Smart contracts are used to ensure trustfulness of the system. The author in [
proposed an access control system to prevent single point of failure and unauthorized access to the
networks. Moreover, many systems are presented to manage sharing of data among IoT devices.
], the authors proposed a trust-based sharing system. In this system, data sharing is integrated
with access control to achieve trustfulness and authentication. Additionally, the authors in [
presented a blockchain-based sharing model for the vehicular networks. The objectives of the system
are to prevent unauthorized and unauthenticated data sharing, and to achieve high quality sharing.
By considering the aforementioned work, a blockchain-based data sharing and access control system is
proposed. This system is intended to achieve trustfulness and authentication for data sharing among
IoT devices. Additionally, the authorized and privileged access to data, and efﬁciency of the system in
terms of cost are also considered.
1.3. Problem Statement
The dramatic growth of the IoT networks results in numerous challenges like: illegitimate
data sharing, unauthorized access control, unauthenticated users, single point of failure due to
centralization, and many others. To solve the aforementioned issues, many systems are presented
in the existing literature. In these systems, blockchain technology is used that is beneﬁcial due to
its decentralized, immutable, transparent, and secure nature. The authors in [
] proposed a smart
contracts-based access control framework to provide distributed and trust-based access to the data.
However, authorization and authentication of users are not considered, which affect security of
the IoT networks. Furthermore, cost and complexity of the system are increased due to multiple
smart contracts. Furthermore, the authors in [
] proposed a blockchain-based key management
scheme for access control. This scheme is intended to achieve privacy, efﬁciency, decentralization,
and scalability in IoT network access. However, unauthorized access affects validity, reliability,
trustfulness, and authentication of the system. Several schemes are proposed for data sharing in
IoT networks. The authors in [
] proposed a blockchain-based service sharing system to protect IoT
terminals from unauthorized service providers. Despite authorization, trust-based sharing is not
taken into consideration, which highly affects the reliability and validity of data sharing. Furthermore,
the authors in [
] proposed a blockchain-based data sharing in AI-powered networks. This system
works on trust-based sharing mechanism and is managed by smart contracts. However, the use of the
system in all sharing scenarios is a challenging task. Furthermore, it does not provide trustfulness,
and authentication-based data sharing. Hence, the aforementioned literature work lack to provide
authenticated, authorized, and trust-based data sharing and access control among IoT devices.
To overcome the aforementioned issues discussed in Section 1.3, a blockchain-based data sharing
and access control system is proposed in this work. The proposed system aimed to achieve authorized,
authenticated, permissioned and trust-based access control in the IoT networks. Furthermore, efﬁciency
of the system is determined in terms of cost. The proposed system is an extension of the work in [
The main contributions of this work are as follows:
a blockchain-based system is proposed for efﬁcient data sharing, trustworthy and authorized
access control among IoT devices’ users,
multiple smart contracts are used for efﬁcient, secure, authorized, and trust-based access
management of users in the network,
•secure and reliable data sharing is also achieved through smart contracts,
one main smart contract; i.e., Access Control Contract (ACC) is used to manage the overall access
control and sharing among users and also to enhance efﬁciency of the system in terms of cost,
authentication of users in the network is maintained by Register Contract (RC) through users’
registration and the record is maintained in the user registration table,
Appl. Sci. 2020,10, 488 5 of 21
•misbehavior of users is checked by JC; after that, the corresponding penalty is determined,
misbehavior of each user is recorded in the system by creating a subject misbehavior record table,
•different permission levels are set to provide permissioned access rights to users, and to achieve
the trustful and reliable sharing,
in addition, both transaction and execution costs of smart contracts and their functions are
at the end, a comparison is made between existing and proposed systems, which is given in Table 1.
Table 1. Cost Consumption Comparison of Benchmark and Proposed Systems.
Systems Execution Cost Transaction Cost
Access control system  5,484,074 gas units -
Data sharing model  458,761 gas units 662,673 gas units
Proposed data sharing and access control system 5,200,000 gas units 6,900,000 gas units
Rest of the paper is organized as follows. In Section 2, a detailed literature review is presented.
In Section 3, objectives of the system are presented. Furthermore, in Section 4, the proposed system
is discussed in detail with its workﬂow. After that, the simulation results are described in Section 5.
In Section 6, a comparison of existing and proposed system is given. In Section 7, the whole paper
2. Related Work
Blockchain technology is intended to provide efﬁcient management of data sharing and access
control in the IoT networks. Several schemes are presented in the literature that integrate blockchain
with IoT network.
The authors in [
] presented a smart contracts-based access control system to provide trustfulness
and validation of users. However, this system lacked to provide the direct interaction between IoT
devices and also compromised in terms of high cost. The authors in [
] presented a blockchain-based
distributed access management architecture for IoT devices to achieve high mobility, concurrency,
accessibility, and resiliency towards attacks. However, the proposed architecture does not provide
authorized and authenticated access. The authors in [
] proposed an ABAC system for IoT networks in
order to achieve less communication and computational overhead and enhanced ﬂexibility, and efﬁcient
maintenance of system. Therefore, the consensus mechanism, i.e., PoC used in the system, performed
efﬁciently for some parts of the system. The authors in [
] presented a blockchain consensus-based
access control scheme to authenticate users through their provided Channel State Information (CSI).
However, the system lacked to provide trustfulness and does not perform efﬁciently in non-cooperative
environments. The authors in [
] designed a multiple blockchain-based cross-chain framework for
access control management in IoT networks. The main objective of the framework is to achieve IoT
devices’ security. However, the privacy and trustfulness of users’ information are not guaranteed
efﬁciently. The authors in [
] proposed a novel Blockchain-based Distributed Key Management
Architecture (BDKMA) to overcome the single point of failure issue. The system achieves scalability;
however, the blockchain technology is not fully used. The authors in [
] presented an off-chain-based
sovereign blockchain to ensure the security and effectiveness in access control. Therefore, the system
does not provide trustful transactions, when it is integrated with industrial use cases.
The authors in [
] proposed a blockchain-enabled efﬁcient data collection and secure sharing
scheme to provide high quality sharing, safe and reliable environment for MTs, and resiliency towards
attacks. However, the trustfulness and security of nodes are not ensured. The authors in [
a novel blockchain-based service provisioning mechanism for Lw clients. The validity of on-chain
and off-chain services is maintained through smart contracts. Therefore, the system lacked to provide
authorized access control and secure sharing. The authors in [
] presented an AI-based trusted sharing
network for mobile communications by implementing hyperledger fabric. The authors achieved
Appl. Sci. 2020,10, 488 6 of 21
trusted sharing through smart contracts and ﬁne-grained access control. However, the scheme does
not perform efﬁciently in all sharing environments. The authors in [
] proposed a reputation-based
data sharing scheme. The goal is to enhance data sharing quality among network nodes, and to ensure
security of data storage. However, the system is only efﬁcient for small area networks. The authors
] designed a blockchain-based infrastructure for security and privacy-oriented service sharing
system in IoT. The system is scalable that reduces its efﬁciency.
The authors in [
] proposed a blockchain-based secure data sharing system for vehicles to
provide efﬁcient incentives and to discard fake announcements by vehicles. Therefore, the ethereum
blockchain used in the system lacked to achieve high throughput. The authors in [
] presented an
encryption-based data sharing system using blockchain. The system maintained the integrity, privacy,
and non-repudiation of data and achieved better encryption. However, it does not perform well
for decryption. The authors in [
] proposed a Data security Sharing and Storage system based on
Consortium Blockchain (DSSCB). The system achieved efﬁciency; however, authentication and security
sharing do not perform better in real time evaluation. The authors in [
] proposed an Electronic
Health Records (EHRs) system to address issue of sensitive medical data leakage and to ensure secure
data access. Therefore, the real-world implementation of the system is not provided. The authors
] presented a bubble of trust mechanism to provide trustworthiness and conﬁdentiality of data,
identiﬁcation and authentication of devices. The system is efﬁcient in terms of cost; however, the
communication between nodes in a bubble is not controlled. Furthermore, compromised devices are
not eliminated. The authors in [
] presented a blockchain-based identity management system that
works on a key management protocol based on Self Certiﬁed public Key-Based System (SCKBS). The
system ensures several security requirements such as: authentication, conﬁdentiality, and auditability.
However, access mechanisms are not used efﬁciently. The authors in [
] presented a blockchain-based
smart toy data exchange system to provide decentralized, secure, trusted, fair, and reliable data
exchange. The system is efﬁcient and ensures transparency and data conﬁdentiality. Therefore, the
data delivery time that is increased due to logs and high throughput are not handled. The authors
] presented an access control system based on eXtensible Access Control Markup Language
(XACML) policies. The system ensures auditability of resources through generalized access control.
However, privacy and auditability are not provided properly. Furthermore, permissioned blockchain
used in the system does not work well. The authors in [
] proposed a blockchain-based mutual
authentication system for Industry 4.0. The system aimed to provide efﬁcient and secure access control.
However, real implementation of the system in smart factory is not presented.
Furthermore, the authors in [
] addressed security issues in the networks. To overcome
the issues, the authors proposed a blockchain-based trusted system for nodes’ routing and recovery.
The insecurity and untrustworthiness of data is also identiﬁed by the authors in [
]. To overcome
the issues, blockchain-based systems are presented. The systems also provide the efﬁcient use of
devices data. Additionally, the authors in [
] presented a blockchain-based system to achieve
trustfulness and authentication of data in the networks.
The related work is summarized in Table 2.
Appl. Sci. 2020,10, 488 7 of 21
Table 2. Summary of Related Work.
Techniques Problems Addressed Contributions Evaluations Limitations
Blockchain  Critical access control
Smart contracts-based access control
Gas price and misbehavior Cost and overhead
DTLS protocol  Security, centralization Access management architecture Throughput, latency, and
response time Single management hub
PoC, AES-128, PBFT  Complexity and security ABAC and hash operation Overhead Consensus mechanism not efﬁcient
PBFT, CNN  Unauthorized access D2D underlying cellular networks Channel rate and spectral efﬁciency Non-cooperative scenarios
PoW, PBFT  Data management and security Decentralized access Security and cost consumption User privacy
PoW Untrustworthiness and
management system Auditability and scalability Not persistency
ECDSA, PoC  Security and data mismanagement Sovereign blockchain Execution time Inefﬁcient in industries
DRL  Limited MT resources and security Secure sharing Energy consumption and data
collection ratio Trustfulness and security
PoA, PoW  Security and untrustworthy Service provisioning mechanism Delay, throughput, and gas
PBFT  Complex system and AI bottleneck Data sharing system Security, privacy, and scalability Lacks efﬁciency
ECDSA  Constrained resources and trust Reputation-based data sharing Efﬁciency and security
Not suitable for large area networks
MEC tier, NBT, SHA-256  Insecure sharing Certiﬁcate-less aggregate
signature scheme Tests on different scenarios Lacks efﬁciency
PBFT  Security and un-authorization Secure data sharing Security analysis and efﬁciency Ethereum does not perform well
FBSS  Bottleneck of ABE Encryption-based sharing Security analysis Lack efﬁciency
PBFT, SHA-256 Untrustworthiness and
Data security sharing and
storage system Safety analysis Real-time authentication and
PoW, SHA-256  Privacy leakage Permission-based access Execution time Real world implementation
ECDSA  Un-authentication and high cost Decentralized authentication
mechanism Time and ﬁnancial cost Compromised devices
SCKBS  Security risks Identity-based access control
Energy consumption, requests
per second Inefﬁcient access mechanisms
Chain-code algorithm Security, reliability, and
privacy leakage Data exchange system Request speed and transaction time Delivery time and throughput
XACML, PoC  Resource protection XACML-based access
control system Monetary cost, resource and time Performance lacks
algorithms Security problems Mutual authentication system Transaction time No real implementation
Appl. Sci. 2020,10, 488 8 of 21
3. Objectives of the Proposed Access Control and Data Sharing System
In this section, the objectives of the proposed system for access control and data sharing among
IoT devices’ users are presented. Such objectives are given below.
The trust of IoT devices’ users is maintained through a misbehavior-judging method,
which is implemented by JC. The users who misbehave are known as untrusted users and are
penalized. Only the trusted users are allowed to access their required services.
In this system, authentication of users is done by RC. Any user, who becomes
part of the system is registered by RC. Furthermore, a record is maintained in the user registration
In this system, only authorized users are allowed to access their required
services. The users’ authorization is maintained through permission levels.
•Efﬁcient data sharing:
The data sharing among IoT devices’ users is maintained through the
ACC. Which maintains the sharing of services among users. Furthermore, the permission-based
sharing is also achieved through the permission levels.
•Understanding of user behavior
In this system, the behavior of users is checked by
implementing behavior judging method in JC. Misbehavior is conducted by subjects by sending
too many requests. After identifying misbehavior, the requests of subjects are halted for
a particular time. That is why, only the users who behave well are provided with the
•Less cost consumption:
The system consists of three smart contracts, as in [
]. In the previous
scheme, ACC is generated by users, for every transaction. It will increase the overall cost of
the system. However, in this system, single ACC is used to control the access of the system.
It also reduces the cost in terms of gas consumption of smart contracts, which is given in the
4. Proposed System Model
A blockchain-based data sharing and access control system for IoT is proposed in this work,
which is shown in Figure 2. This system is designed after getting motivation from work in [
Moreover, comparisons of the proposed and existing systems are made. The ﬁrst comparison is made
on the basis of scenarios, platforms, and tools used for simulations, as shown in Table 3. Another
comparison is made between parameters of existing and proposed systems, which is shown in Table 4.
In this system, data sharing is done between two network’s users or peers called subject and object.
The subject is a user that actually wants the data services. Whereas, the object contains services that are
required by the subject. Services include: data, ﬁle, program, etc. Furthermore, three smart contracts
are used to manage data sharing and access control among subjects and objects.
The components used in this system are: IoT devices, smart contracts (i.e., ACC, RC, and JC) as
], misbehavior judging method, data access permission. The detailed discussion of components is
presented in Sections 4.1–4.3. Additionally, workﬂow of the system is also discussed in Section 4.4,
which shows the overall working of the proposed system model.
Appl. Sci. 2020,10, 488 9 of 21
(1) Calls Main Smart Contract
Verify Subject (User)
Access Control Contract
Check Subject Misbehavior
Misbehave Check Subject Permission Level
Share Requested Service
System Management Layer
Pass Request to Object
Figure 2. Proposed System Model.
Table 3. Comparison with Existing Works.
System Models Scenarios Implementations Platforms Simulation
system Access control in IoT Remix IDE, Go
access control in IoT - IOTA and Tangle Ubuntu 16.04
Access control 
Access control with
blockchains OMNeT++ 5.4.1
Data sharing among
MTs using deep
Go Ethereum Python
Data sharing in AI
powered network - Hyperledger fabric -
sharing and access
Access control and
data sharing Solidity language Ethereum, spyder Remix IDE, python
Table 4. Parameters Comparison with Other Works.
Systems Trustfulness Authorization Authentication Validation Decentralized Reliability
Access control system Yes No No Yes Yes No
Access control system No No No No Yes No
Access control system No Yes No No Yes Yes
Data sharing system No No Yes Yes Yes Yes
Data sharing system Yes No No No Yes No
Proposed data sharing and
access control system Yes Yes Yes Yes Yes Yes
Appl. Sci. 2020,10, 488 10 of 21
4.1. Smart Contracts
In the proposed system, multiple smart contracts are used to manage data and service sharing
among network users. The smart contracts are: ACC, RC, and JC. Where ACC manages the overall
access control of the system, the RC is used to register users (subjects and objects) in the system.
It also generates a registration table, which stores the information of users. Users’ authentication
and authorization are also maintained by the registration table. Furthermore, behavior of subjects is
determined by the JC. It checks if any misbehavior is conducted by the subject or not. When a subject
sends too many requests or cancels the generated request, it is considered to be misbehavior. After
the misbehavior conduction, the penalty is imposed on subject by the JC. Hence, trustfulness of the
subject is determined by its behavior. If no misbehavior is done by the subject, then permission levels
are checked. After that, the request of subject is passed by ACC to the corresponding object, to get the
required service. Moreover, a detailed description of ACC, RC, and JC is given below.
It is the main smart contract that manages the access control between IoT devices. Whenever the
subject requires any service from the object, it sends a request to the system. After that, ACC maintains
the access control for subject. It also increases the performance efﬁciency of system. Whereas, in
the benchmark system [
], multiple ACCs are deployed by objects for each request. In this system,
access control is done by the user instead of the system itself. Each time a request is generated by
the subject, an ACC is deployed by the object in response to that request. In a result, complexity and
cost of the system are increased. Moreover, processing time of the system is also effected. However,
in the proposed data sharing and access control system, access control among users is managed by
a single ACC. When a subject sends service request into the system, ACC is executed. Other smart
contracts are executed for registration and authorization of the users. After registration of the user,
ACC forwards the request of subject to the object by checking corresponding permission level.
The users who are intended to access the services should be legitimate. For that, the authentication
of users is done by RC, by registering them in the system. For registration, RC maintains the registration
table, as in [
]. In the table, all information of the users is stored. The registration table generated by
RC is shown in Table 5. The information of users stored in the registration table are: subject, object,
service, and time. Moreover, veriﬁcation and authentication of the subjects are also maintained by
registration table. The registration table consists of the following information:
•subject: the particular user that sends the service request,
•object: user that contains required services and entertains the request of the subject,
•service: particular data or service, which is requested by the subject, and
•time: the time at which a request is generated.
Table 5. User Registration Table.
Subject Object Service Time
Subject A Object X File-1 2019/5/17 11:12
Subject B Object Y Program-2 2019/6/14 1:15
Subject C Object Z File-3 2019/8/8 3:00
A judging method is implemented by the JC, which judges the behavior of users in the system.
When a subject sends service request in the system, its behavior is veriﬁed by the JC. The misbehavior
is always conducted by the subject. When the subject sends frequent and too many requests for a
Appl. Sci. 2020,10, 488 11 of 21
service, it is considered to be misbehaving. Additionally, if the subject cancels its generated request,
it is also known as misbehavior. After that, corresponding penalty is determined for a subject who
misbehaves. In penalty, the state of subject is turned off for a particular time. When the state of a
subject is off, it cannot send request to the system. On the other hand, if the subject has not misbehaved,
then the permission levels are checked. The request of subject is granted or denied, according to its
behavior and permission levels. After that, alert messages are generated by the JC.
The alerts generated for access are as follows (! indicates the alert messages):
•Access Authorized !
•Requests are Blocked !
•Static Check Failed !
•Misbehavior Detected !
•Static Check failed & Misbehavior Detected !
If no misbehavior is conducted by the subject, then access is granted. JC generates an alert
message of access authorization. Whereas, if there is any misbehavior conducted by the subject, other
alert messages such as: permission denied or access blocked are generated by the JC. These alerts show
that permission is not granted to the subject and a misbehavior is conducted.
In the data sharing system, misbehavior is conducted by subjects. When a subject sends too
frequent access requests, it is considered as misbehavior. As a result, trust of the system is highly
effected. Furthermore, misbehavior highly affects the performance and efﬁciency of the system. If
multiple requests are sent by one user, then all network trafﬁc is only consumed by that particular user.
Other issues may occur in IoT networks like: congestion, latency, etc. Several types of misbehaviors
that are done by the subject are:
•subject sends too frequent service access requests,
subject sends multiple access requests for service(s) in a particular time, i.e., 5 requests in
10 min, and
•the request is canceled after generation.
The misbehavior of subject is determined by a misbehavior judging method, implemented by JC.
Which maintains trustfulness of the system. Furthermore, a misbehavior ﬁeld is maintained by the JC,
which is shown in Table 6. The ﬁeld records all misbehaviors conducted by subjects. In the result of
misbehavior, penalty for subjects is determined by the JC. In penalty, requests of a subject are halted.
In the halted state, subject is no more able to send requests in network for a certain time period.
Table 6. Subject Misbehavior Record.
Subject Object Misbehavior Misbehavior-Time Penalty
Subject-A Object-X Multiple requests in 3 min 2019/3/27 11:12 Request halted for 2 h
Subject-B Object-Y Canceled request 2019/4/1 1:05 Request halted for 10 min
Subject-C Object-Z Frequent requests 2019/4/3 3:09 Request halted for 3 h
4.3. Data Permission Control
The proposed system is intended to provide authorized access control and permission-based
sharing of services. Several permission levels are set for subject to access services of the object.
The permission levels determine privileges for the user to access services. These levels are deﬁned
according to the sensitivity level of data. Moreover, the permission is granted to subject based on its
privilege level; what type of data is requested by the subject. The data permission levels are as follows:
Appl. Sci. 2020,10, 488 12 of 21
•L0: Data is not accessible,
•L1: Data can be used in aggregated computation without revealing the raw data ,
•L2: Data is partly allowed, and
•L3: Data or service is accessible.
Hence, all of the above-mentioned levels determine the acceptance or denial of users’ access
request. L0 deﬁnes that the subject is not legitimate to avail the requested service. Such services can be
personal information of object. Legitimacy of the subject is determined by the object. In L1 and L2,
service is shared with some restrictions of usage. In L3, service is fully accessible to subject.
4.4. Workﬂow of System Model
The logical ﬂow of the proposed system is presented, which is shown in Figure 3. The data
sharing system works as follows:
•at ﬁst, a subject sends request for any service (i.e., data, ﬁle, storage unit) in the IoT networks,
further, communication and access management for subject is managed by the smart contracts in
when the request of subject is generated, ACC (main smart contract) is executed to control the
overall access management,
after that, authentication of the users is done by the RC, which registers the users’ information
and maintains records of users via a registration table,
•then, trustfulness of the system is maintained by JC, which checks the behavior of a subject,
if any type of misbehavior is conducted by the subject, JC determines penalty for the subject and
halt its state in the system,
•in another case, if no misbehavior is done, then permission level for the subject is checked,
•then ACC forwards the request of subjects to corresponding objects,
•after that, request of the subject is fulﬁlled by the object, and
•at last, transaction is stored into the blockchain.
Sends request for resource
Subject ACC RC JC Object
RC is called to register subject
If misbehaviour occurs
Penalty assigned (subject's requests are halted for certain time)
If no misbehaviour occurs
Service is provided to subject
Request forwarded to ACC
Request send to object
JC is called for verification
Permission levels checked
Figure 3. Flow Diagram of Proposed System Model.
Appl. Sci. 2020,10, 488 13 of 21
5. Simulations and Results
In this section, simulation results for cost consumption are discussed. Ethereum blockchain is
used in this system; for that, cost of smart contracts and their functions is calculated in terms of gas
usage. Furthermore, the ether value is also checked for each gas unit. Further sections describe the
simulation environment and the cost consumption of each smart contract and its functions.
5.1. Simulations Setting
All the simulations are performed on Intel Core i5, CPU 2.50 GHz with 4 GB RAM, running
on Windows 10. The smart contracts are written in solidity language. Solidity is an object-oriented
programming language, used to implement smart contracts on blockchain platforms, mostly in
ethereum blockchain. Ethereum is an open-source, public blockchain platform for the execution of
smart contracts. In ethereum, all computational tasks are performed by smart contracts .
Furthermore, trufﬂe environment is used for smart contracts development. Trufﬂe is the
development environment and testing framework, used for ethereum blockchain. It makes an easy
compilation, deployment, and management of the smart contracts. Moreover, the graphs for cost
consumption of smart contracts are taken in python language using spyder platform.
5.2. Cost Consumption
The cost consumption of system is calculated in terms of gas units consumed by smart contracts.
Gas is a measurement unit, which measures the computational power for execution of transactions
in ethereum platform. Gas price is deﬁned by the miners at start of the transaction and is measured
in Gwei. Moreover, gas units are calculated for execution of smart contracts and their functions.
In ethereum blockchain, gas units are converted into the ether value (also written as eth) or ﬁat money.
Eth is the fuel of ethereum blockchain.
The gas units are converted into eth value. The gas price is set as 20 Gwei. Furthermore, the cost
of Gwei is calculated by multiplying gas units with gas price. After that, the amount is divided by the
unit of single ether, i.e., 1 ether = 1,000,000,000 Gwei.
Transaction cost: It is the cost of sending smart contract’s code to the ethereum blockchain.
It depends on the size of smart contract. The size of smart contract is based on the computational tasks
it performs. For example: if the smart contract contains high computational tasks; then it is large in
size and the transaction cost is also high. Furthermore, the transaction cost consists of: transaction cost,
contract deployment cost, and transaction data cost.
Execution cost: It is the cost of storing global variables and method calls of the smart contracts. It
also depends on the computational operations performed in terms of transaction execution.
The gas is calculated by three things: gas cost, gas price, and gas limit. Gas cost is the number
of units that are needed to perform any action in the ethereum network. Gas price is the value of
one unit, which is measured in ether. Gas limit is the amount of gas, which is paid by participants of
5.2.1. Smart Contracts Cost
The transaction and execution costs of smart contracts such as: ACC, RC, JC are illustrated in
Figure 4. As it is shown in the aforementioned ﬁgure, gas units consumed by ACC are higher than
other smart contacts, i.e., RC and JC. It is obvious that ACC is the main smart contract that manages
the overall access control and transactions among two IoT devices and it performs more computational
tasks. Tasks performed by the ACC are access control management, granting service permission to
users, and transactions management. Therefore, more gas units are consumed by the ACC. After that,
RC consumes more gas units to perform user registration task and also creates user registration table.
All users’ records are maintained in the user registration table. RC only manages registration; that is
why it has less cost consumption than ACC. Furthermore, JC has less cost consumption rate. As the
Appl. Sci. 2020,10, 488 14 of 21
purpose of JC is to check the behavior of users and to assign the penalty to users who misbehave.
The misbehavior is recorded in the form of table. Moreover, the gas units are converted into ether, as it
is the cryptocurrency of ethereum blockchain. The gas to eth conversion is done by the criteria given
above in Section 5.2.
Figure 4. Smart Contracts Cost.
Transaction cost: The transaction cost of smart contracts: ACC, RC, and JC is shown in the above
ﬁgure. It is clear from the ﬁgure that the transaction cost of ACC is much more than RC and JC.
It manages the overall access control of the system and it contains more functions than other smart
contracts. Therefore, more cost is used according to the size of smart contract. The transaction cost of
ACC is 4,000,000 gas units, which is equal to 0.08 eth. The transaction cost of RC is 1,600,000 gas units,
which is equal to 0.032 eth. The cost of RC is not much high, because it only performs registration
operations of users and creates a user registration table. Additionally, the transaction cost of JC is
1,300,000 gas units, which is equal to 0.026 eth. JC consumed less transaction cost as it implements the
judging methods for users.
Execution cost: The execution cost of smart contracts is also shown in the ﬁgure. Likewise the
transaction cost, the execution cost of ACC is also higher than other smart contracts. The cost is
3,000,000 gas units, which is equal to 0.06 eth. ACC contains more computational operations, function
calls and global variables to control access of the system. After ACC, the execution cost of RC is high,
i.e., 1,200,000 gas units that is equal to 0.024 eth. JC consumes less cost as compare to other smart
contracts, which is 1,000,000 gas units and is equal to 0.02 eth.
5.2.2. Functions Cost
Each smart contract contains various functions to manage access control among IoT devices. In this
system, both transaction and execution cost for the functions of each smart contract are calculated.
The costs are calculated to show the number of gas units consumed by each function of smart contracts.
The cost consumption rate of each function is discussed below.
Functions of ACC:
The transaction cost and execution cost for each function of ACC are shown
in Figure 5. It contains more function calls as compare to other two smart contracts, because ACC
manages the access control of whole system. However, the gas consumption is calculated for main
Appl. Sci. 2020,10, 488 15 of 21
functions of the smart contract. The functions of ACC that perform several operations include user
registration, generating permission levels for the subject, and data access function. The transaction
and execution costs of the functions are given below.
Figure 5. ACC Function Cost.
Transaction cost: As it is illustrated in the aforementioned ﬁgure, transaction cost of the user
registration function is higher than other functions. The user register function consumes 89,000 gas
units, which are equal to 0.00178 eth value. All users are registered in the network for authentication.
Furthermore, the data access function consumes 30,000 gas units, which are equal to 0.0006 eth values.
In the access control function, requests of subjects are passed by ACC to the corresponding objects.
After that, gas consumed by permission level function is 25,000 gas units, which is 0.0005 eth. This
function is called after judging behavior of the users. The privileges are checked for users to access the
required service, for this reason, it consumed very less cost.
Execution cost: The execution cost of ACC functions is also given in the above-mentioned ﬁgure.
The execution cost of user register function is 65,000 gas units and its ether value is 0.0013 eth.
The registration function also consumes more execution cost. Furthermore, the function that grants
data access to the users consumes 9,000 gas units that are equal to 0.00018 eth value. Moreover, the
function for setting permission levels for the users consumes very less cost, i.e., 5,000 gas units that
are equal to 0.0001 eth. Hence, the functions with high cost consumption are executed more than the
functions that cost less.
Functions of RC:
The transaction and execution costs of RC functions are shown in Figure 6.
The objective of RC is to manage the registration of users in the network, and to maintain a registration
table to record the users’ information. The functions of RC are user registration and registration
Appl. Sci. 2020,10, 488 16 of 21
Figure 6. RC Function Cost.
Transaction cost: The transaction cost of RC functions shows that user registration function
consumes more transaction cost. The cost consumed is 130,000 gas units that is equal to 0.0026 eth.
The function registers the users in the network. Furthermore, the registration table is maintained by
the register table function. The cost of registration table function is 45,000 gas units, which is equal to
0.0009 eth values.
Execution cost: Furthermore, the execution cost of RC functions is also calculated. The cost of
registration function is 130,000 gas units that is equal to 0.0026 eth. The registration table consumes
23,000 gas units, which are equal to 0.00046 eth value.
Functions of JC:
In Figure 7, the transaction and execution costs of JC functions are given. The JC
is used to maintain trustworthiness of the system by checking behavior of each user. The behavior
judging method is implemented by JC, which checks if any misbehavior is conducted by the subjects or
not. After that, the penalty is determined for subject. The functions of JC are misbehavior calculation
function and misbehavior judge function.
Appl. Sci. 2020,10, 488 17 of 21
Figure 7. JC Function Cost.
Transaction cost: The aforementioned ﬁgure illustrated the transaction cost for functions of JC.
The cost of misbehavior judging function is more than other functions, which is 195,000 gas units.
The gas units are equal to 0.0039 eth values. As the misbehavior of users and penalty is determined by
this function; that is why it costs more. Furthermore, the transaction cost of misbehavior calculation
function is 80,000 gas units. The gas units are equal to 0.0016 eth value.
Execution cost: Moreover, the execution cost of JC functions is also shown in the above ﬁgure.
The misbehavior judge function also consumes more execution cost. The cost is 165,000 gas units that
are equal to the 0.0033 eth value. The cost of misbehavior calculation function is 60,000 gas units,
which is equal to 0.0012 eth.
6. Analysis and Comparison
In this section, a comparison is made to evaluate the performance of proposed system in terms of
cost consumption of smart contracts.
In this section, a comparison is made between existing and proposed systems. One of the objective
of this system is to reduce the cost for execution of the smart contracts. The cost is given in the form of
gas consumption of ethereum smart contracts. In the above Section 5, the execution and transaction
costs of each smart contract and its functions are calculated. However, in this section, comparison
is made between cost consumption of smart contracts in [
] and in the proposed system. The cost
consumption rate is given in Table 1, which illustrates the transaction and execution costs of smart
Appl. Sci. 2020,10, 488 18 of 21
contracts. The table shows the overall cost of systems for a single transaction. As it is given in the
table that less cost is obtained by the smart contract in [
]. The reason is that the system contains
a single smart contract that manages the sharing among users. Therefore, in [
] and proposed system,
three smart contracts are used to manage the access control among users. The smart contracts in [
consumes more cost. Because ACC is generated by the user (subject or object) for each transaction.
Whenever ACC is generated, the cost for executing the smart contract is used, which increase the
overall system cost. The execution cost of ACC for a single transaction is 2,543,479, as given in [
For multiple transactions, the overall cost of the system will increase. Also the overhead of the system
is increased. However, in the proposed system, the ACC is not created for each transaction. It is
a single smart contract that manages the service sharing among users. Furthermore, the overhead of
system is also reduced.
The proposed system is efﬁcient and performs better in terms of different parameters.
Any user can easily access the system, whenever it requires any service. As the
public blockchain is open and available. The users can easily become part of the system.
The trustfulness of users is maintained through the JC smart contract. JC
implements the behavior judging method that judges the behavior of each subject. If the
misbehavior is conducted by subject, it is known as untrusted and its requests are halted for
a particular time period. In another case, if the misbehavior is not conducted, then subject is
considered as trusted.
The major challenges in IoT networks are the provisioning of authenticated, trusted data sharing,
and authorized access control among IoT devices. Therefore, in this paper, blockchain technology is
integrated with the data sharing and access control system. Main objectives of this system are to achieve
authentication, authorization, and trustfulness. With the integration of blockchain technology, several
beneﬁts are brought into the networks. To achieve the aforementioned objectives, multiple smart
contracts are used in this system. The smart contracts are ACC, RC, and JC. Where ACC maintains
the access control between IoT devices and manages the sharing of data among them. Furthermore,
RC is called to manage the authentication of users in the system by registering them. The users’
information is recorded in the user registration table. Moreover, the misbehavior judging mechanism
is implemented in this system. JC is used to check the behavior of users. Whenever a misbehavior is
conducted by the subject, the corresponding penalty is determined. Besides that, if no misbehavior is
conducted by the subject, the privileges are set for that and access is granted. Furthermore, simulations
are done in terms of cost consumption of smart contracts and their functions. The cost is calculated
in terms of gas units. After that, a comparison is made between the existing and proposed systems.
Results show that the system is efﬁcient in terms of cost, as the access control is managed by ACC.
The proposed system is less complex for access control management among IoT devices.
Tanzeela Sultana and Nadeem Javaid proposed and implemented the main idea. Ahmad
Almogren and Mariam Akbar wrote the simulation section. Mansour Zuair and Ibrar Ullah organized and reﬁned
the manuscript. All authors worked together and responded to the honourable reviewers’ comments. All authors
have read and agreed to the published version of the manuscript.
This work was supported by the Deanship of Scientiﬁc Research at King Saud University
under Grant RGP-1437-35.
Conﬂicts of Interest: The authors declare no conﬂicts of interest.
Appl. Sci. 2020,10, 488 19 of 21
The following abbreviations are used in this manuscript:
ABAC Attribute-Based Access Control
ABE Attribute-Based Encryption
ACC Access Control Contract
AES Advanced Encryption Standard
AI Artiﬁcial Intelligence
CNN Conventional Neural Network
CSI Channel State Information
DES Data Encryption Standard
DRL Deep Reinforcement Learning
DSA Digital Signature Algorithm
DSSCB Data security Sharing and Storage system based on Consortium Blockchain
DTLS Datagram Transport Layer Security
ECDSA Elliptic Curve Digital Signature Algorithm
EHRs Electronic Health Records
FBSS Fair Blind Signature Scheme
IDE Integrated Development Environment
IoT Internet of Things
JC Judge Contract
MTs Mobile Terminals
NBT Naive Bayes Theorem
PBFT Practical Byzantine Fault Tolerance
PoA Proof of Authority
PoC Proof of Concept
PoS Proof of Stake
PoW Proof of Work
RC Register Contract
SCKBS Self Certiﬁed public Key Based System
SHA Secure Hash Algorithm
XACML eXtensible Access Control Markup Language
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