Blockchain-SDN-Based Energy-Aware and Distributed Secure Architecture for IoT in Smart Cities

Abstract

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data and defend against cyber-threats. To address these issues, the authors present a distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-aware architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

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IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 1
Blockchain-SDN based Energy-Aware and
Distributed Secure Architecture for IoTs in Smart
Cities
Md. Jahidul Islam, Anichur Rahman, Sumaiya Kabir, Md. Razaul Karim, Uzzal Kumar Acharjee, Mostofa Kamal
Nasir, Shahab S. Band, Mehdi Sookhak, and Shaoen Wu
Abstract—Insecure and portable devices in the smart city’s
Internet of Things (IoT) network are increasing at an incredi-
ble rate. Various distributed and centralized platforms against
cyber-attacks have been implemented in recent years, but these
platforms are inefficient due to their constrained levels of storage,
high energy consumption, the central point of failure, underuti-
lized resources, high latency, and etc. In addition, the current
architecture confronts the problems of scalability, flexibility,
complexity, monitoring, managing & collecting of IoT data, and
defend against cyber-threats. To address these issues, the authors
present a distributed and decentralized Blockchain-Software
Defined Networking (SDN) based energy-aware architecture for
IoT in smart cities. Thus, SDN continuous observing, controlling,
managing IoT devices activities and detect possible attacks in
the network; Blockchain provides adequate security & privacy
against cyber-attacks, reduces the central point of failure issues;
Network Function Virtualization (NFV) are used to saving energy,
load balancing, as well as increasing the lifetime of the entire
network. Also, we introduce a Cluster Head Selection (CHS) algo-
rithm to reduce the energy consumption in the presented model.
Finally, we analyze the performance using various parameters
(e.g. throughput, response time, gas consumption, communication
overhead) and demonstrating the result that provides higher
throughput, lower response time, lower gas consumption than
existing works for smart cities.
Index Terms—SDN, IoT, NFV, Blockchain, SDN Controller,
Cluster, OpenFlow, Security, Privacy, Smart city.
I. INTRODUCTION
The world is getting smarter over time because of modern
technologies. People can do myriad things that could not be
Md. Jahidul Islam and Sumaiya Kabir are with the Department of Computer
Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh
e-mail: jahidul.jnucse@gmail.com, sumaiya@cse.green.edu.bd.
Anichur Rahman is with the Department of Computer Science and En-
gineering, National Institute of Textile Engineering and Research (NITER),
Dhaka, Bangladesh. e-mail: anis.mbstu.cse@gmail.com.
Md. Razaul Karim, Mostofa Kamal Nasir are with the Department of
Computer Science and Engineering, Mawlana Bhashani Science and Tech-
nology University, Tangail, Bangladesh. e-mail: razaulce15004@gmail.com,
kamal@mbstu.ac.bd.
Uzzal Kumar Acharjee is with the Department of Computer Science
and Engineering, Jagannath University, Dhaka, Bangladesh. e-mail: ukachar-
jee@gmail.com.
Shahab S. Band is with the Institute of Research and Development,
Duy Tan University, Da Nang 550000, Vietnam and Future Technology
Research Center, College of Future, National Yunlin University of Science
and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002,
Taiwan. e-mail: shamshirbandshahaboddin@duytan.edu.vn.
Mehdi Sookhak and Shaoen Wu are with School of Information Technol-
ogy, Illinois State University, Normal, IL, USA. e-mail: msookha@ilstu.edu,
swu1235@ilstu.edu.
Manuscript received - ; revised -
thought of before. To make our habitats more elegant, IoT is
lending so many contributions. Nowadays a countless number
of sensors from different categories are connected together to
move our lives forward. The IoT devices collect data from the
real-world and can send them to a processing system where the
data could be transferred into a valuable decision. The idea of
using RFID to recognize a sensor among the millions of nodes
is very impressive and effective in the field of IoT [1]. Some
studies show that the approximate amount of devices could be
75 billion by 2025 [2]. That’s why the risk of getting attacked
by online intruders became higher and it became very difficult
to manage such kind of gigantic information on time.
However, IoT devices accumulate informative data in a
scattered way as there is no system to arrange the collections
of data followed by any systematic as well as scientific
procedures. To decorate the huge amount of scattered data,
SDN is used with IoT applications with a central controller
to support the [3] network configuration and management.
It can control the behavior of the data dynamically and
change the operational procedures programmatically without
hampering the main physical architecture. When there are
some vulnerabilities, SDN can easily detect the anomalies
and can take some pre-steps to inhibit any kinds of attacks
primarily. Apart from these merits, SDN can perform their
operations with multiple centralized controllers [4].
Fig. 1. Blockchain Structure

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 2
Although SDN is one of the emerging technologies which
comes with the advantages of managing a massive amount
of raw information and primary security, being centralized
system causes another major issue into the performance. If
any problem occurs in the center point of the controllers,
the whole system will be affected and destroyed [5], [6]. On
the other hand, DoS and DDoS attacks can cause multiple
controller failures [7]. To defeat the individual point of failure,
decentralized Blockchain technology comes into action. This
decentralized technology can be integrated with SDN based
IoT applications where hash values are used to chain the
blocks together, and each and every piece of the transaction is
permanently saved, as shown in Fig. 1. Security and privacy
could be enhanced by integrating this Blockchain technology.
A block of code known as Smart Contract (SC) is run to verify
and validate a transaction. Once a data transaction is validated,
the information is stored in a storage and the index of the
data with some other overheads is stored in the block, which
is added to the public ledger. The ledger is immutable and
impossible to change it by any intruder. Thus Blockchain can
prevent the attacks from trespassers and as the transactions
are handled by the SC programmatically, once the rules and
regulations are established, there is nothing to be worried about
the transaction process.
Furthermore, energy consumption is a critical issue while
there will be billions of smart devices are connected across
the area and such kind of architecture will be applied. The
power is required to transmit the information collected by the
IoT gateway. So it is also a piece of interest to take care
of the amount of energy. Clustering the devices into various
groups and selecting one of them to transfer the data to the
processing station as the cluster head can lead the action to
decrease the power consumption. There are many factors to
select the head and some researchers have already proposed
different algorithms to choose the Cluster Head (CH). Another
modern technique to virtualize the functionality is also very
feasible in these types of smart architectures. NFV provides
communication services by turning the practicalities of IoT
devices into a virtualization [8]. It is the application system
where the system is resilient if it gets any changes in the traffic
load and failures. To provide dependable communication in
networks, different technologies are fused. So it is reasonable
to be raised the complexity whenever these technologies will
be implemented. Several solutions have been presented by
many researchers. The solutions are not capable of fulfilling
the criteria, such as some of these solutions leave more security
but not perfect in terms of reliability. Some authors proposed
a Blockchain-based SDN application, but the architecture is
not asserted [9].
Motivating by the above premises, the author presents an
energy-optimized and Blockchain-SDN based secure architec-
ture. A CHS method has proposed that selects CH among
the clusters with the highest residual energy that balances
the energy among the IoT devices. Moreover, SDN con-
trollers dynamically managed IoT device’s operational activi-
ties that increase security & privacy. In addition, decentralized
Blockchain technology is used to reduce the malicious activ-
TABLE I
TERMINOLOGIES AND DESCRIPTION IN ALPHABETICALLY ORDER ED
Notations Definition
API Application Programming Interface
BC Blockchain
CH Cluster Head
CHS Cluster Head Selection
CSP Cloud Service Provider
DDoS Distributed Denial of Service
IoT Internet of Things
LEACH Low-Energy Adaptive Clustering Hierarchy
NFV Network Function Virtualization
NFVI Network Function Virtualization Infrastructure
PoW Proof of Work
QoS Quality of Service
RPC Remote Procedure Call
SC Smart Contact
SDN Software Defined Networking
SDK Software Development Kit
TCP Transmission Control Protocol
WSN Wireless Sensor Network
ities of the network also provides security & privacy in the
proposed network.
A. Contributions of the study
The contributions of this paper are as follows:
•Design a distributed and decentralized architecture for
IoT the ecosystem which reduces the existing challenges
through the use of technologies Blockchain, SDN, and
NFV.
•Implement the SDN network on the mininet-wifi emulator
and the Blockchain network using Ethereum technology
to detect and mitigate the cyber attack in the IoT net-
works.
•Also, an CHS algorithm is proposed for energy-aware
where the highest residual energy is selected as a CH in
the cluster.
•Evaluate the performance of the proposed model with
two existing works using various simulation parameters
that are carried out (e.g., throughput, response time, gas
consumption, and communication overhead). We also
take into account the attack scenarios (DDoS) for various
applications in the proposed model.
The rest of the research has been formed as follows: we have
studied and discussed the literature review in Section II. After
that, Section III presents a distributed architecture for IoT;
CHS procedure; Security mechanism flowchart. Moreover,
evaluation of results and analysis of the proposed model are
provided in Section IV. In sum, the author’s concluded the
paper in Section V.
II. LITERATURE OVERV IE W
Some researchers have addressed in recent years based
on emerging leading technology such as Blockchain, IoT,
NFV, and SDN technologies. In this section, we are going
to represent some literature’s overview of recent works:

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 3
TABLE II
EXISTING WOR K ANALYS IS
Authors Key
Technologies
Architecture Application Issues
Address
Blockchain
Implementation
Platform
Sharma et al. [1] Blockchain,
SDN
Centralized,
Distributed
IoT
Ecosystem
Security &
Privacy
Ethereum Network
Rahman et al. [10] Blockchain,
SDN
Centralized,
Distributed
Smart City Security &
Privacy
Ethereum Network
Anich et al. [11] Blockchain,
SDN
Centralized,
Distributed
Smart City Security &
Privacy
Ethereum Network
Ghandour et al. [12] Blockchain Decentralized,
Distributed
Smart City Security &
Privacy
Hyperledger Fabric
Xu et al. [13] Blockchain,
Smart Contract
Decentralized,
Distributed
IoT Networks Security &
Privacy
-
Sharma et al. [14] SDN Centralized,
Distributed
Edge
Computing
Energy -
Sharma et al. [15] Blockchain,
SDN
Centralized,
Distributed
Smart City Security &
Privacy
Ethereum Network
Reyna et al. [16] Blockchain,
SDN
Decentralized,
Distributed
IoT Networks Security &
Privacy
Ethereum &
Hyperledger
Rahman et al. [17] Blockchain,
SDN
Centralized,
Distributed
IoT
Ecosystem
Security &
Privacy
Ethereum Network
Rahman et al. [18] Blockchain Decentralized IoT
Ecosystem
Security &
Privacy
Hyperledger Fabric
Sharma et al. [19] Blockchain,
SDN
Centralized,
Distributed
IoT Networks Security &
Privacy
Ethereum Network
Novo et al. [20] Blockchain Centralized,
Distributed
IoT Networks Scalability Ethereum Network
Gu et al. [21] Blockchain Decentralized,
Distributed
Cloud
Computing,
IoT Networks
Security &
Privacy
Ethereum &
Hyperledger
Sharma et al. [15] Blockchain Distributed Automotive
Industry &
Smart city
Security &
Privacy
Ethereum
A. IoT with Smart Cities
Stojkosk et al. [22] presented an IoT framework for a smart
home under the consideration of home energy management
and architectural challenges and solutions. They also empha-
sized on the data processing issues. They identified a holistic
and cloud-centric model integrated with different components
of IoT. The authors also analyzed the state of art IoT solutions
into their smart home system. They have:
- Analyzing the stream and next challenges for the IoT
based on results.
- Smart home defining for a holistic framework with key
characteristics and parameters
- A splendid explanation of a holistic framework based
smart home management model
In [23], the authors discussed the essential IoTs and in-
telligent structures for energetic building blocks. They also
set the direction for energy optimization and the next man-
agement systems for building propagation. Additionally, they
also dealt with some of the technical opportunities offered
and the professional disputes IoT faces in a smart building.
Mehmood et al. [24] introduced the IoT-enabled model for
categories of smart cities and networks, possible prospects,
and significant desires. Several wireless technologies such as
SIGFOX, 6LoWPAN, and IEEE 802.11p were also expended.
Furthermore, they highlighted some challenges and directions
for future smart technology research. In another research, Hui
et al. defined the significant necessities for making a smart
home management [25]. Authors remarked seven unique req-
uisite commendations for in-casing the extraordinary quality
of the intelligent home control efficiently.
B. Cluster Head Selection Approaches
Kumar et al. introduced clustering techniques, which ap-
portioned the extensive system into tiny clusters where every
cluster its cluster head (CHs) [26], [27]. Those CHs appro-
priated the method of period analysis various admittance for
fulfilling time grooves to each link. They also mentioned the
energy utilization of hops that aided the network to assist in
various times. Similarly, Angel et al. presented an Enhanced
Energy Efficient Clustering Algorithm (EEECA) for lessening
strength tuberculosis in plucking a Cluster Head (CHs) in
Mobile Wireless Sensor Networks [27], [28]. On the other
study, Al-Baz et al. addressed a special version of the LEACH
protocol named Node Ranked–LEACH, which validated the
Node Station (NS) algorithm based on system lifetime [29].
In addition, they also promised to succeed in the arbitrary
method choice as an algorithm, which in other LEACH
variants provides instant failure for different cluster heads.
Further, they have also approached varied parameters such as
throughput and performance packet ratio, reducing the packet
delay and the sensor’s energy expenditure. In a similar work,
Zhao et al. recommended [30] a transformed LEACH-based
cluster-head selection algorithm for WSNs. Additionally, they
illustrated different networking perspectives, such as network

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 4
continuance, energy maintenance, and data volume during the
simulation phases.
C. IoT with SDN
Kalkan et al. considered the security of various SDN inven-
tions and based on security demands, and they suggested the
most appropriate security mechanism in [31]. The authors also
discoursed future challenges in the area of IoT environment
with a role-based comptroller for preserving security appointed
as Rol-Sec for SDN. Moreover, Bull et al. introduced an
SDN-based security scheme for the IoT network in [32]. The
authors also proposed architecture that unmistakably described
the discovery and impediment of the technique of flooding
assails for TCP and ICMP. Then, In [33], the authors refreshed
different aimed SDN architecture and security solutions for
IoT from 2012 to 2016. They analyzed and compared various
existing solutions on SDN based on the IoT. Another secure
IoT structure based on SDN has been depicted in [34].
Next, In [35], for secure Black SDN-IoT authors proposed a
distributed architecture for smart cities with the NFV concept.
To improve accessibility, protection, and privacy, they used
multiple distributed SDN controllers in their proposed archi-
tecture. Vandana et al. aimed at a security-based framework for
applying SDN to the IoT ecosystem. In addition, Mukherjee
et al. presented an SDN basis of disseminated IoT network
using NFV execution for smart cities in [3]. The authors also
implemented NFV into SDN-IoT architectural network to train
cost-efficient, reliable, and springy intelligent cities. Further-
more, they mentioned the cluster head selection procedure and
addressed the multiple controllers in the SDN environment.
Secure mechanics, introduced by Liu et al. [36] has proposed
for handling various assails. The authors proposed SDN for the
Middlebox arrangement and flow table capacity constraints .
Additionally, they also demonstrated the experimental results
of the suggested M-G model and improved the overall safety
and perceptive constancy in the IoT network.
D. IoT-SDN with Blockchain
Rahman et al. proposed “DistBlockSDN” architecture for
smart cities in [10]. Furthermore, they presented a cluster head
selection approach for collecting sensors data with low energy
dissipation. Besides, the authors evaluated the performances in
different parameters such as throughput and packet arrival rate
carefully using Blockchain technology. In another research,
Sharma et al. [19], through the use of Blockchain technology,
proposed an efficient cloud architecture to improve security.
However, the proposed model with distributed cloud infras-
tructure provides the computing infrastructures with secure,
minimal cost access. Furthermore, they evaluated their pro-
posal using some performance metrics. But, their performance
is not yet the absolute one. The implication of Blockchain
with cloud-based IoT is reviewed in [37]. After describing
the Blockchain method and classification, the authors dis-
cussed the need for Blockchain before implementation in IoT.
They analyzed when an organization should use Blockchain
in IoT applications and provided an optimized architecture
for IoT. After analyzing the IoT based applications security
TABLE III
EXISTING REC ENT WO RKS : IS SUE S & CHALLENGES
Articles Issues & Challenges
Yazdinejad et al.
(2020) [41] •Digital Security
•Resource Management
•Energy Consumption
Singh et al.
(2020) [42] •Digital Security
•Scalability
•Energy Consumption
Aujla et al.
(2020) [43] •Storage
•Centralized minor
•Interoperability
•Digital Security
Hameed et al.
(2020) [44] •Scalability
•Delay and Response Time
•Load Balancing
•Digital Security
Sharma et al.
(2020) [15] •Transaction Speed & Cost
•Legal Framework
•Simplicity & Integrated Platform
Pradip et al.
(2019) [45] •Co-integration with IoT platform
•Virtual ecosystem
•Dynamic expandability
•Standardization
Rathore et al.
(2019) [46] •Co-integration with IoT platform
•Virtual ecosystem
•Dynamic expandability
•Standardization
issues, challenges, efficiency, and feasibility, the authors found
Blockchain as a solution in [38] and [39]. Moreover, Dorri et
al. analyzed the smart home’s functions and core components
based on Blockchain for IoT for security and privacy purposes
[40]. They used a local and private Blockchain that provides
IoT gadgets with secure access control and keeps a time-
ordered transaction history for each smart home tier.
III. PROP OS ED BLOCKCHAIN-SDN BASED DISTRIBUTED
ARCHITECTURE
Taking the above problem into consideration, the authors
propose a distributed Blockchain-SDN based architecture for
smart city, as shown in Fig. 2, we consider the emerging
technology such as IoT, SDN, and Blockchain to specify the
smart cities. The proposed model consists of several layers,
including the perception layer where the smart device provides
data for users. Then, the edge layer produces a report on
the data that is provided by the perception layer and also
processing data efficiently. Furthermore, the cloud layer is used
to store the desired data, also provide services securely by the
Cloud Service Providers (CSPs). Furthermore, the SDN and
Blockchain performances entirely depend on the performance
of every layer successfully.

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 5
Fig. 2. Proposed Architecture for Smart Cities
However, we separate various steps for explaining our
proposed architecture. At first, we provide a energy efficient
cluster head selection algorithm to select a cluster head (CH)
with energy consumption in the perception layer environment,
as shown in Fig. 3. In the SDN environment, the SDN has
been organized into two convenience planes, such as the data
plane and the control plane. In addition, we also present the
SDN-IoT architecture for passing raw data through SDN-IoT
standard gateway protocol data layer. Besides, we use the
NFV that provides physical specification and decreases energy
consumption effectively. Also, it comprises load balancing,
conservation of energy and electronic network scale [35], [47].
Further, FloodLight is SDN controller that helps to forward
the filtering data to the control layer in the SDN environment
efficiently using OpenFlow based SDN routing protocol. In
addition, controllers ensure that all data is filtered by the data
layer of the SDN platform.
After that, we address Blockchain technology [48] with a
distributed ledger for accomplishing the transaction process
one block to another through the communication channel. This
process can be capable of providing security and privacy to the
proposed architecture more confidentially. Most importantly,
the block data is stored by the cloud layer conditionally. If
the data is valid, then the data is placed into data centers
accordingly, as depicted in Fig. 4. After completing the
Blockchain transaction, cloud data is going through the de-
sired applications, including the smart home, traffic, building,
hospital, payment system, and so on.
A. Energy Efficient Cluster Head Selection Method
IoT devices can forward data with the help of common SDN
gateways. Also, the dynamic SDN controller can filter the
IoT data. As the proposed architecture will provide security
Fig. 3. Clusters Head Selection Procedure
and confidentiality to the data that helps to transmit the data
to the cloud layer (security process is discussed in the later
section). But, it performs efficiently if the IoT devices can
be able to select energy-efficient cluster heads. In this part,
the authors have proposed an energy-efficient cluster head
selection algorithm in this section, which is shown in Fig.
3.
1) Cluster Head Selection:Cluster heads selection one of
the essential part of the proposed scheme. For increasing the
network’s lifetime, Cluster Heads (CHs) are evenly distributed
among the system. At the beginning of the algorithm, sort-
ing each node according to their energy values ((𝐸𝑁
𝑖)) and
choosing the highest energy node that is primarily considered
as a CH among the cluster and other nodes are considered as
the member of each cluster. After that, calculate the distance

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 6
Fig. 4. Edge Enable Service Architecture
TABLE IV
NOTATIO NS OF T HE CHS METHOD
Notations Definition
𝑛𝑖Number of IoT devices
𝐶𝑖Clusters of the IoT devices
𝐸𝑟𝑒 𝑠
𝑖IoT devices residual energy
𝐸𝑡ℎ
𝑖Threshold value of energy
𝜁Cluster Head
𝐸𝑁
𝑖Sorted list o f nodes based on energy
DDistance between the nodes
BS Base Station
DBS Distance from the Base Station
NDBS Net Distance from the Base Station
𝐶𝐸𝑚 𝑎𝑥
𝑖Maximum energy of the clusters
from one to the other nodes using Euclidean Distance. Also,
computing the sum of the distance of all nodes from one
node. Then, calculating the distance of all nodes from the Base
Station (BS) and also calculates the net distance (NDBS) from
the base station to all nodes. Then, compute the residual energy
of the IoT devices based on the min(NDBS) and maximum
energy 𝐶𝐸𝑚 𝑎𝑥
𝑖respectively. In addition, 𝐸𝑟 𝑒𝑠
𝑖indicates the
residual energy, and 𝐸𝑡 ℎ
𝑖represents the threshold energy, which
means that CH has considerably more energy to carry out its
operation. Further, before going to sleep, each node transmits
data to the CHS. Finally, data sent to the base stations after
collecting the data by the CHs through a standard SDN
gateway. Only can eligible CHs get permission for routing
into an efficient path.

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 7
TABLE V
COMPARISON OF PROPOS ED ALGORITHM WITH EXISTING WO RKS
Authors IoT Nodes sorting
with energy values
Distributed Node
Density
Optimization Reliability Energy
Aware
Rahman et al. [10] X X × × × X
behera et al. [49] ×X×X× ×
Aslam et al. [50] ×X X X X X
Anich et al. [11] X X X ×X X
Farman et al. [51] ×X X ×X X
Kumar et al. [52] ×X X ×X X
Renugadevi et al. [53] ×X× × × X
Proposed X X X X X X
Algorithm 1: Proposed Energy Aware Cluster Head
Selection Algorithm
1Require:
•𝑁𝑖:𝑁𝑢𝑚𝑏𝑒𝑟 𝑜 𝑓 𝐼𝑜𝑇 𝑑𝑒𝑣𝑖𝑐𝑒𝑠
•𝐶𝑖:𝐶𝑙 𝑢𝑠𝑡𝑒𝑟𝑠 𝑜 𝑓 𝑡ℎ𝑒 𝐼 𝑜𝑇 𝑑𝑒𝑣𝑖 𝑐𝑒 𝑠
•𝐸𝑟 𝑒𝑠
𝑖:𝐼𝑜𝑇 𝑑𝑒 𝑣𝑖𝑐𝑒𝑠 𝑟𝑒𝑠𝑖 𝑑𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔 𝑦
•𝐸𝑡 ℎ
𝑖:𝑇 ℎ𝑟 𝑒𝑠 ℎ𝑜𝑙 𝑑 𝑣𝑎 𝑙𝑢𝑒 𝑜 𝑓 𝑒 𝑛𝑒𝑟𝑔 𝑦
Ensure: 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 𝐻𝑒𝑎𝑑 𝑠𝑒𝑙𝑒𝑐𝑡𝑖 𝑜𝑛 (𝜁)
𝐼𝑛𝑖𝑡𝑖𝑎𝑙𝑖𝑧𝑒 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 𝐻 𝑒𝑎𝑑 𝜁 ←0
while (1)do
for 𝑖←1to 𝑁−1do
𝑚𝑖𝑛 =𝑖
for 𝑗←𝑖+1to 𝑁do
if (𝐸𝑗< 𝐸𝑚𝑖 𝑛)then
𝑚𝑖𝑛 =𝑗
end
𝑠𝑤𝑎𝑝(𝐸𝑗, 𝐸𝑚𝑖 𝑛)
end
𝑆𝑜𝑟 𝑡𝑒𝑑 𝑙𝑖𝑠𝑡 𝑜 𝑓 𝐼 𝑜𝑇 𝑑𝑒𝑣𝑖 𝑐𝑒 𝑠 𝑒𝑛𝑒𝑟𝑔 𝑦 𝑣𝑎 𝑙𝑢 𝑒𝑠 (𝐸𝑁
𝑖)
end
return
𝑆𝑜𝑟 𝑡𝑒𝑑 𝐿𝑖 𝑠𝑡 𝑜 𝑓 𝑁 𝑜𝑑𝑒𝑠 𝐵𝑎𝑠𝑒𝑑 𝑜𝑛 𝐸 𝑛𝑒𝑟𝑔𝑦 (𝐸𝑁
𝑖)
for 𝑖←1to 𝑁do
for 𝑗←1to 𝑁do
𝑑𝑖 𝑗 ←𝐷(𝐸𝑁
𝑖)
𝐷𝑖←𝐷𝑖+𝑑𝑖 𝑗
end
end
for 𝑖←1to 𝑁do
𝐷𝐵𝑆𝑖←𝐷(𝐵𝑆𝑖, 𝐶𝑖)
𝑁 𝐷 𝐵𝑆𝑖←𝑁 𝐷 𝐵𝑆 𝑖+𝐷𝑖
end
for 𝑖←1to 𝑁do
𝐸𝑟 𝑒𝑠
𝑖← [𝑚𝑖𝑛(𝑁 𝐷 𝐵 𝑆𝑖)&& (𝐶𝐸𝑚 𝑎𝑥
𝑖)]
𝐶𝑜 𝑚 𝑝𝑢𝑡 𝑒 𝑟 𝑒𝑠𝑖 𝑑𝑢 𝑎𝑙 𝑒 𝑛𝑒𝑟 𝑔𝑦 (𝐸𝑟 𝑒𝑠
𝑖)
if (𝐸𝑟 𝑒𝑠
𝑖≥E𝑡 ℎ
𝑖)then
𝜁←𝐸𝑟 𝑒𝑠
𝑖
end
end
return 𝐶𝑙𝑢𝑠𝑡 𝑒𝑟 𝐻 𝑒𝑎𝑑 (𝜁)
end
B. Enhancing security of the proposed architecture through
Blockchain and SDN
We have heterogeneous IoT devices that ability to exchange
information, execute transactions but need to be secure com-
munication. Therefore, a secure data transmission method
needs to be present, shown in Fig. 5. Blockchain and SDN
can provide secure data transmission in the overall networking
system. Where the SDN controller provides security and
network services to IoT devices. Since the IoT devices in the
perception layer reveal gigantic data, it is not very tough to
be under attack by the third party. Apart from this, the data
is unstructured, so what SDN does is rearrange the data in
meaningful ways and find out the destination of a particular
data. It can monitor the behaviour of the data as well as
the user who is requesting for a service. Thus SDN can
detect some anomalies and third party attacks in the network.
Also, discards the malicious packets from the SDN domain.
Moreover, enhancing the safety and decreasing the energy con-
sumption of the IoT devices. In addition, Blockchain provides
security, privacy, confidentiality, integrity, etc by storing the
identity of the IoT devices in an immutable public ledger.
The distributed and decentralized architecture of Blockchain
ensures the security of billions of IoT devices. Moreover, the
security mechanism is presented in the flowchart, as depicted
in Fig. 6. In flowchart, IoT devices request for SDN controller,
then devices are registered in the SDN controller, and an IP
address for each IoT device is allocated. Even, all IoT device
operational activities & transactions are monitored for security
purposes in the SDN controller. Furthermore, IoT devices are
blocked by the SDN controller if any malicious behavior is
found. Then, IoT devices IP addresses are registered in public
Blockchain networks to prevent them from registering in other
clusters.
C. The role of Network Function Virtualization (NFV) into
SDN-IoT System
Networking devices can offer users different types of ser-
vices. Every user always expects the networking devices to
provide secure data. The authors have presented SDN-IoT
architecture with NFV in this section from that expectation.
In architecture, Network Function Virtualization Infrastructure
(NFVI) provides the SDN-IoT infrastructure virtualization
facilities, as shown in Fig. 7. Also, an SDN has effectively
organized [54] as two distinct planes, such as a data plane
and control plane with SDN multiple controllers.

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 8
Fig. 5. Secure Data Transmission through Blockchain and SDN Domain
Fig. 6. Security Mechanisms Flowchart
Besides, the data layer receives data from the clustering do-
mains; also, SDN-IoT standard gateway protocol can manage
the smart devices data efficiently. After that, the control layer
provides some SDN controllers such as security, application,
packet, key, intrusion, and crypto controller. The security
controller, which aims to perform all security issues for a
desirable model. Again, the packet controller provides the
networking packet management strategy into the network, and
Fig. 7. SDN-IoT on NFV Architecture [35].
other controllers perform their operation in a particular way for
the presented architecture. Finally, In the NFVI environment,
the control plane also conveys the different virtual services like
virtual storage, virtual networking as well as virtual computing
[3].
D. Distributed Blockchain Procedure for Smart City
IoT ecosystem supplies integrity, availability, confidentiality,
authentication, a non-repudiation & access control through
Blockchain Technology [55]. Also, Blockchain contains some
key components such as decentralization, transparency, au-
tonomy, immutable, anonymity, as well as the open-source
system. We have presented an SDN-IoT based model with
NFV using a distributed Blockchain approach. This distributed
Blockchain is utilizing the distributed multiple controllers.
Blockchain is a decentralized grouped ledger with no autho-
rization or no individual control the ledger. Every striving
leads or confirmed the ledger. Miners verify the latest activities
and so placing all of them inside the global ledger. Usually,
every 5 to 10 minutes a block is surely extricated—Miners
attempt to create a terrifying statistical perplexity based on
a cryptographic hash algorithm. The found solution is called
the Proof-of-Work (PoW). Appended to the arrangement is the
newly mined block. When creating the particular block by the
miners, it proceeds to add to the several BC. After adding a
block in the BC, it is stimulating to work in remodeling data
through the block chiefly because it requires turning all of the
subsequent blocks. As well as, any particular block which will
get matched to the BC, the part requires consensus in any the
vast preponderance nodes from the various networks.

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 9
Fig. 8. Distributed Blockchain Approach for Smart Cities
TABLE VI
SIMULATION ENVIRONMENT
Parameters Name Values
General
Parameters
Packet Analyzer Wireshark
Cloud storage platform OpenStack
SDN
Parameters
No. of SDN Controllers 6
OpenFlow switches 5
Gateways 2
Types of SDN Controllers FloodLight, OpenDayLight
SDN Routing Protocol OpenFlow
Blockchain
Parameters
Blockchain platform Ethereum
Number of transactions Variable
Block size 4 bytes
Block header 80 bytes
Proof type Proof of Work (PoW)
Others
Parameters
Simulation Area 3000m X 3000m
Number of IoT devices 100
Simulation Times 350s
Data Rate 12 Mbps
Initial Energy Values of IoT devices 12-15 j
Initial Trust value 5 j
Node Transmit Packet Size 512-1024 bytes
After that, we have addressed the decentralized Ethereum
Blockchain network approach in a wide tremendous network-
ing systems, which is shown in Fig. 8. This architecture allows
the transmission of data from the IoT devices to the SDN
Gateways. Then, the information is stored in the database and
sent to the Smart city for further decision. But before making
any changes to the storage, the transaction are validated by the
Smart Contract following some mining procedures under the
Ethereum Blockchain Network system. Ethereum will store
the data into the cloud storage after the verification and stores
the indices by means of blocks. Through the REST or JSON
RPC following some other API and SDK which will help to
run the Smart Contracts to verify and validate the data request
information, Smart City will be able to interact with the cloud
storage.
E. Smart City on Cloud Application
Approachability, usability, data safety & security have been
provided by a smart city. These cities can be capable of uti-
lizing smart education, energy, health, farming, environment,
home, hospital, transport, and building, etc [10]. Our presented
model can be capable of controlling several smart pieces of
equipment like a smart door, window, light, fan, AC as well
as smart phone by using Blockchain with SDN-IoT gateway.
When any request of access is captured which is unauthorized,
it is inhibited by the Firewall. The information is preserved
into the cloud to make the data availability easier. Smart trans-
port decreases vehicle traffic collision. Furthermore, intelligent
fans, light can save the electricity and in the smart home,
including stylish door & window can protect our home from
intruder suitably. In a similar way, a smart city application
has been improved our lifestyle. Besides, IoT enabled devices

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 10
Fig. 9. Cluster Head Ratio with Respect to the No. of Nodes Fig. 10. Power Consumption (mW) with Respect to the No. of Nodes
are the key components for a smart city model. Finally, the
main goal of a smart city on a cloud storage are fashionable
civilization, society, governance as well as smart technology
[56].
IV. EVALUATION RESULTS A ND ANALYS IS
A. Simulation Environment setup
To assess the efficiency of the proposed model simulation
parameters are shown in Table VI with their corresponding
values. Where author has used network emulator (Mininet
2.2.1) for topology setup. Among the general parameters,
Wireshark is used for the packet analyzer. In SDN platform,
there are 6 Floodlights SDN controllers and 5 OpenFlow based
switches are used through a couple of Gateways. Moreover,
Ethereum as a Blockchain platfrom with dynamic number of
transactions is used where block size is 4 bytes. Some other
parameters are also highlighted in the table including no. of
IoT devices, devices speed, data rate, packet sizes, energy &
trust values of IoT devices. Moreover, we have used Ubuntu
16.04 LTS(Linux) OS, Intel(R) Core(TM)-i5-10210U CPU @
160GHz-2.11GHz, 8.00GB RAM, 1TB ROM, and 512GB
SSD for getting the expected results.
B. Cluster Head Selection Algorithm Performance Analysis
1) Cluster Head Ratio (CHR): Fig. 9 shows the CHR with
respect to the no. of IoT nodes. It will be better if we select
fewer cluster heads from the cluster areas because of CHs have
more links. We have designed an algorithm that automatically
chooses the fewer cluster heads as well as considers the energy.
To evaluate the performances of the algorithm authors compare
the proposed algorithm with SUSTEC [57]. In the beginning,
when the no. of IoT nodes is 10, the proposed algorithm
and SUSTEC cluster head selection are almost similar. After
that, when the no. of nodes are reached at 25, the proposed
algorithm performed better than SUSTEC (selects fewer CHs
compared to the existing works). Furthermore, for 70 nodes
the SUSTEC selects fewer CHs (almost similar), but after
increasing with the no. of nodes (over 80 nodes) the proposed
algorithm performs better.
2) Power Consumption: Moreover, Fig. 10 shows the power
consumption (mW) with respect to the no. of nodes. It is
clearly notified that the proposed algorithm performs better
than the LEACH mobile [53] approach protocol. For less no. of
nodes (<3), proposed alg. performances are much better than
LEACH mobile protocol. It also provides a better result for
(20 to 25) nodes compared to the existing works like LEACH
mobile and LEACH protocols.
C. Overall Architecture Performance Analysis
1) Throughput: We have evaluated the throughput (i.e.,
network throughput relates to how many transaction requests
can be transmitted from origin to destination within a rea-
sonable period) of the proposed model, as shown in Fig. 11.
However, we have observed that the throughput starts from
100 transaction requests for each of the systems such as core,
“DistArch-SCNet” [58], and proposed model efficiently. When
the number of requests is 400, all model performances are
almost the same. Furthermore, when the number of transaction
requests is reached at 1200, the proposed architecture shows
much more throughput than the core model. For 2400 requests
proposed model shows higher throughput compared to the
existing model. In addition, with increasing the number of
requests, throughput also increases and the proposed system
much better performance than DistArch-SCNet and Core
model in the network.
2) Response Time: The time duration needed to get the very
first reply from the system is cited as response time. It is more
beneficial to have lower latent period comparing to the existing
scheme. The response time of the proposed model is presented
in Fig. 12 which also draws the superiority of this system
over the core model and DistArch-SCNet. At the beginning,
when the number of transactions is about 400, DistArch-SCNet
and proposed model take same period of time to respond, but
the core model starts to show higher response time from the
starting point. It’s clearly noticed that the core model needs
considerably more time to reply with the action of a request
with the increasing number of transactions. If the requests

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 11
Fig. 11. Throughput with Respect to the Number of Transaction
Requests
Fig. 12. Response Time with Respect to the Number of Transaction
Requests
Fig. 13. Gas Consumption with Respect to the Time(ms) and Number of
Transactions
Fig. 14. Communication Overhead(%) Comparisons with Respect to
the No. of IoT Devices
are limited to 1200 approximately, the proposed model and
DistArch-SCNet show almost same performance, but if the
amount of requests raises over 1200, the proposed model
responds with a shorter time equating to the SCNet. With
increasing the transaction requests(Over 1600 requests) Core
model performance worst than other model where DistArch-
SCNet and proposed architecture performs around similarly in
the network.
3) Gas Consumption: Fig. 13 shows the no. of transactions
request with respect to the time(ms) and gas consumption.
Gas consumption is the price that must be paid for valid
transaction of the minor nodes. At the beginning of the small
no. of transactions, the gas consumption is not so high. After
that as the no. of transactions increases, the gas consumption
increases linearly with the time. However, the cost of the
transactions is necessary to illustrate in the proposed model.
The experimental result shows how the gas consumption varied
in terms of time(ms) when the no. of transaction changes.
4) Communication Overhead: We analyzed the communi-
cation overhead comparisons are shown in Fig. 14. For less
no. of nodes(10) core, proposed, and DistArch-SCNet model
communication overhead is almost similar. Moreover, for 30
nodes, both proposed model and DistArch-SCNet are still
perform identically. However, with increasing the number of
nodes, overhead is also progressing. Further, it is clearly shown
that proposed architecture better performance shows a linear
way compared to the DistArch-SCNet and core model. After
comparing the overhead communication comparisons effec-
tively in the presented system model based on the proposed
and DistArch-SCNet scheme, we clearly identified that our
proposed model shows better performances compare with the
DistArch-SCNet system.
5) CPU Utilization during DDoS Attack : Now, for analyz-
ing CPU utilization, we have applied the DDoS attacks in our
projected environment during various application are running
continuously.
We utilized a learning method for recording CPU utilization

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 12
Fig. 15. CPU Utilization Scenario during DDoS Attack
TABLE VII
ATTACK COMPARISONS OF EXISTING WORK W ITH PR OPO SE D WORK
CPU Utilization (%)
15 Apps 21 Apps
Time
(s)
Sharma
et al.
[1]
Proposed Sharma
et al.
[1]
Proposed
0.3 0.2 0.1 0.2 0.3
0.6 0.5 0.2 0.8 0.3
0.9 1.2 0.4 2.7 1.1
1.2 16.7 13.1 24.8 20.2
1.5 10.3 8.2 27.6 24.1
1.8 11.8 10.3 15.5 12.1
2.1 7.1 6.2 13.7 11.3
2.4 1.9 0.2 3.1 1.2
2.7 1.5 0.2 2.1 1.2
3.0 0.8 0.1 0.7 0.1
during a DDoS attack. Fig. 15 shows average output of
the CPU utilization in different applications based on prop
scheme, when DDoS attacks are performed. This attack started
at 0.9s of the simulation, with increasing the time, the attack
rate also climbs up. In continuing the attack after a certain
period of time (1.5s), we have noticed that DistB-SDIoT
provides adequate protection against this attack efficiently
and the CPU can get back into the stable situation after a
fluctuation. The vulnerability of CPU utilization lasts for a
short period and depends on the number of applications opened
at that moment. Moreover, Table VII shows the average CPU
utilization for attack with the presence of 15 & 21 applications.
Finally, our presented system enhances the performances of
different application, safe from other numerous attacks as well.
V. CONCLUSION
Blockchain and SDN technologies are yet unfledged in
recent research, and it’s all services and performances that are
in the growing as yet. These fields have a lot of challenges,
risks, and threats. Moreover, a few numbers of researchers
have addressed these threats and try to overcome these chal-
lenges. To overcome these problems, the author presents
a Blockchain-SDN based distributed model for smart cities
with NFV. Also, the author introduces an energy-optimized
cluster head selection algorithm to select a cluster head in an
efficient procedure. Moreover, the SDN controller monitors
and manages the activities of the IoT devices; Blockchain are
used to detect & reduce the cyber-attacks in the IoT networks.
Finally, the experimental result shows that the proposed archi-
tecture performs better compared to the existing architecture
(Core and DistArch-SCNet) in terms of throughput, response
time, gas consumption, communication overhead, which no-
tably increases the throughput and reduces the response time,
overhead, and gas consumption. Though we have considered
various parameters nevertheless there are several limitations
still remain. The author does not consider the end to end
delay, network bandwidth, other network vulnerabilities, and
various passive & active attacks (including Flooding attack,
Sybil attack, MITM, and etc.). In the future, we intend to
develop an energy-efficient edge computing model with the
help of SDN, NFV and Blockchain technology taking such
ongoing problems into consideration.
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Md. Jahidul Islam received the B.Sc. and M.Sc.
degrees in Computer Science and Engineering from
Jagannath University (Jnu), Dhaka, in 2015 and 2017
respectively. Currently, he is working as a Lec-
turer and Program Coordinator (Day) at Computer
Science and Engineering (CSE), Green University
of Bangladesh (GUB), Dhaka, Bangladesh since
May 2017 to present. He is a member of Com-
puting and Communication and Human-Computer
Interaction (HCI) research groups, CSE, GUB. His
research interests include Internet of Things (IoT),
Blockchain, Network Function Virtualization (NFV), Software Defined Net-
working (SDN), 5G, Industry 4.0, Machine Learning, HCI, and Wireless Mesh
Networking (WMN).
Anichur Rahman received the B.Sc. and M.Sc
degree in Computer Science and Engineering from
Mawlana Bhashani Science and Technology Uni-
versity (MBSTU), Tangail, Bangladesh in 2017 and
2020 respectively. Currently, he is working as a Lec-
turer at Computer Science and Engineering (CSE),
National Institute of Textile Engineering and Re-
search (NITER), Savar, Dhaka, Bangladesh since
January 2020 to present. His research interests in-
clude Internet of Things (IoT), Blockchain (BC),
Software Defined Networking (SDN), Image Pro-
cessing, Machine Learning, Vehicular Ad-Hoc Networking (VANET), 5G,
Industry 4.0 and Data Science.
Sumaiya Kabir received the B.Sc. and M.Sc.
degrees in Computer Science and Engineering
from Patuakhali Science and Technology University,
Barisal, and East West University, Dhaka, respec-
tively. Currently, he is working as an Assistant Pro-
fessor and Program Coordinator (Day) at Computer
Science and Engineering (CSE), Green University
of Bangladesh (GUB), Dhaka, Bangladesh since
May 2013 to present. His research interests include
Semantic Web, Web 3.0 Architecture, Ontology De-
signing, RDF Clustering Agent based web mining,
Building intelligence in web mining, Data Science.
Md Razaul Karim received the B.Sc. degree in
Computer Science and Engineering from Mawlana
Bhashani Science and Technology University (MB-
STU), Tangail, Bangladesh in 2020. The main inter-
ests of his research are Machine Learning, Computer
Vision, and Image Processing. He is also keen on
Blockchain.
Uzzal Kumar Acharjee obtained his Ph.D degree
in Applied Physics, Electronics & Communication
Engineering from University of Dhaka, Bangladesh
in 2014. He received the M.Sc. in Computer Science
from the University of Dhaka, Bangladesh, in the
year 2000. He is serving as Professor in the Depart-
ment of Computer Science and Engineering, Jagan-
nath University, Bangladesh. His research interests
include the area of Artificial Intelligence, Neural
Networks, Deep Learning, Data Mining etc.
Mostofa Kamal Nasir Professor of Computer Sci-
ence and Engineering of Mawlana Bhashani Science
and Technology University, Tangail, Bangladesh.
He has completed his PhD from University of
Malaya, Kuala Lumpur, Malaysia in the field of
Mobile Adhoc Technology in 2016. Before that
he has completed his BSc and MSc in Computer
Science and Engineering from Jahangirnagar Uni-
versity, Bangladesh. His current research interest
include VANET, IoT, SDN and WSN.
SHAHAB S. BAND received the M.Sc. degree in
artificial intelligence from Iran, and the Ph.D. degree
in computer science from the University of Malaya
(UM), Malaysia, in 2014. He was an Adjunct As-
sistant Professor with the Department of Computer
Science, Iran University of Science and Technology.
He also severed as a Senior Lecturer with UM,
Malaysia, and with Islamic Azad University, Iran.
He participated in many research programs within
the Center of Big Data Analysis, IUST and IAU.
He has been associated with young researchers and
elite club, since 2009. He supervised or co-supervised undergraduate and
postgraduate students (master’s and Ph.D.) by research and training. He has
also authored, or coauthored papers published in IF journals and attended to
high-rank A and B conferences. He is an Associate Editor, a Guest Editor,
and a Reviewer of high-quality journals and conferences. He is a professional
member of the ACM.
Mehdi Sookhak (M’12, SM’19) received the Ph.D.
degree in computer science, major in information
security, from the University of Malaya (UM), in
2015. He was an Active Researcher with the Center
of Mobile Cloud Computing Research (C4MCCR),
UM. From 2016 to 2017, he was with Carleton
University, Canada, as a Postdoctoral Fellow. He
is currently an Assistant Professor of cybersecurity
with Illinois State University, Normal, IL, USA. He
has authored more than 40 articles in high ranking
journals and conferences. He serves as an editor of
several ISI journals, such as vehicluar communications, IEEE Access, and
Electronics. He also served as a chair of several conferences, such as ICCE
2019-2020. His areas of interest include cloud and mobile cloud computing,
fog computing, vehicular cloud computing, the IoT and smart cities, compu-
tation outsourcing, access control, network security, wireless sensor & mobile
Ad Hoc network (architectures, protocols, security, and algorithms), big data
security and analytic, distributed systems, and cryptography and information
security.

IEEE INTERNET OF THINGS JOURNAL, VOL., NO., MARCH 2021 15
Shaoen Wu (M’14- SM’16)) received the Ph.D.
degree in computer science from Auburn University,
Auburn, AL, USA, in 2008. He is currently a State
Farm Endowed Chair Professor with Illinois State
University, Normal, IL, USA. He was an Asso-
ciate Professor of Computer Science at Ball State
University, an Assistant Professor with the School
of Computing, University of Southern Mississippi,
Hattiesburg, MS, USA, a Research Scientist with
ADTRAN Inc., Huntsville, AL, USA, and a Senior
Software Engineer with Bell Laboratories, Qingdao,
China. His current research interests include IoT, wireless and mobile network-
ing, and cyber security. Prof. Wu has served on the chairs and the committees
of various conferences, such as the IEEE INFOCOM, ICC, and GC, and an
Editor for several journals including IEEE Transaction on Multimedia, and
IEEE IoT Journal. He was a recipient of the Best Paper Award of the IEEE
GC 2019, IEEE ISCC 2008 and the ANSS 2011. He serves as the Vice Chair
for North America on IEEE MMTC 2020-2022.