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IBF network: enhancing network privacy with IoT, blockchain, and fog
computing on different consensus mechanisms
Iraq Ahmad Reshi
1
•Sahil Sholla
1
Received: 30 April 2024 / Revised: 8 September 2024 / Accepted: 21 December 2024
The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025
Abstract
An amalgamation of blockchain technology and the Internet of Things (IoT) has presented notable concerns regarding
scalability, security, and privacy, particularly in IoT contexts with limited resources. Conventional blockchains, including
traditional consensus mechanisms like Proof of Work (PoW) and Proof of Stake (PoS), meet challenges in handling many
transactions, meeting energy efficiency standards, and addressing privacy issues in blockchain-based IoT networks. This
work presents a new fog-based blockchain paradigm that integrates the benefits of Proof of Authority (PoA) and Delegated
Proof of Stake (DPoS) consensus mechanisms and a proxy re-encryption approach to guarantee improved efficiency and
system security. The proposed architecture integrates three essential operational algorithms: Fog Node Operation,
Blockchain Node Operation, and Privacy Preservation Mechanism. These algorithms manage data processing, ensure
secure transactions, and maintain privacy. Fobsim is used to conduct a series of simulations to evaluate the performance of
PoA, DPoS, PoW, and PoS. The results indicate that PoA and DPoS provide better transaction speed, energy efficiency,
and scalability than conventional consensus. As illustrated in the results, PoA stands out for its deficient energy con-
sumption, making it an ideal fit for IoT applications. This research addresses the pressing concerns of scalability, privacy,
and energy efficiency in blockchain-enabled Internet of Things (B-IoT) systems. The results lay the foundation for the
future advancement of integrated B-IoT systems that can enable extensive, real-time IoT applications.
Keywords Consensus Proof of authority Blockchain based IoT Fog computing Proxy re-encryption
Delegated proof of stake
1 Introduction
Today, the Internet of Things (IoT) has become very
popular due to the availability of affordable and powerful
devices like sensors, radio frequency identifiers (RFIDs),
and different communication technologies. This popularity
has opened up opportunities for developing home
automation systems and industrial applications, including
connected drones, connected health, smart farming, wear-
ables, and more. The IoT industry is forecasted to grow
from over 15 billion devices in 2015 to more than 75
billion devices by 2025 [1]. According to this forecast, the
average number of personal IoT devices per person on the
planet will be at least 25 [2]. The data generated by these
devices is projected to reach an astonishing 73.1 zettaby-
tes(ZB) [3]. Hence, it is crucial to provide resilient systems
that safeguard the confidentiality of users’ identities and
empower them to manage the disclosure and utilisation of
their data. Nevertheless, the swift growth of IoT networks
has brought about substantial barriers, especially in data
confidentiality, protection, and scalability. Contemporary
approaches to guarantee security and privacy in the IoT,
such as obfuscation [4], enforcement [5]), and one-time
passwords (OTP) [6], largely prioritise the safeguarding
data privacy. Nevertheless, these approaches frequently
neglect the safeguarding of user privacy and are ineffective
in preventing privacy breaches, particularly when data is
housed and managed in centralised systems [7]. Further-
more, these centralised systems are susceptible to single
&Iraq Ahmad Reshi
rshiraq333@gmail.com
Sahil Sholla
sahilsholla@gmail.com
1
Department of CSE, Islamic University of Science and
Technology, Awantipora, Kashmir, J&K 192122, India
123
Cluster Computing (2025) 28:208
https://doi.org/10.1007/s10586-024-05026-w(0123456789().,-volV)(0123456789().,-volV)
points of failure, which gives rise to issues concerning the
resilience and dependability of IoT networks as they
expand in size [8]. Given that the existing solutions are
insufficient, addressing the comprehensive spectrum of
privacy, security, and scalability issues in IoT systems
presents a substantial research challenge. The advent of
Bitcoin alongside other cryptocurrencies popularised
blockchain technology, which emerged as a revolutionary
invention with wide-ranging applications across several
industries [9,10]. The IoT is a promising field where
interconnected gadgets share data and independently per-
form tasks. Nevertheless, the widespread use of IoT devi-
ces recently generated concerns over data privacy and
security protection, which has made it necessary to provide
solid and practical solutions [11]. Notwithstanding its
immense potential, the incorporation of blockchain tech-
nology into IoT systems poses a distinct set of obstacles.
Although fundamental to blockchain technology, tradi-
tional consensus mechanisms such as Proof of Work (PoW)
and Proof of Stake (PoS) frequently encounter scalability
and energy efficiency challenges when used in IoT settings.
The resource-intensive nature of these processes may not
satisfy the scalability requirements of IoT systems, par-
ticularly when devices have limited processing capabilities.
Such insufficiency underscores the need for alternative
technologies that may expand within IoT networks while
maintaining privacy and security, solving the existing
research and application gap.
Incorporating blockchain technology in IoT systems
through fog computing has notable scalability, privacy, and
efficiency obstacles. Conventional blockchain structures,
especially those that depend on PoW or PoS, need signif-
icant resources and may not be efficient in scaling in IoT
environments where devices often have limited processing
capabilities. In addition, guaranteeing the confidentiality of
dispersed data in a decentralised network, particularly in
fog computing environments where processing occurs
closer to the network edge, introduces intricacy to the
system design.
•Scalability: With the expansion of IoT ecosystems,
there is significant growth in the number of transactions
and data points, which is growing exponentially.
Conventional consensus algorithms face challenges in
maintaining efficiency in such situations, resulting in
network congestion and high latency. These issues are
particularly harmful in real-time IoT applications.
•Privacy: The decentralised nature of blockchain leads to
the dispersion of data across multiple nodes, raising
concerns about the potential exposure of sensitive
information. In fog-based systems, traditional block-
chain implementations frequently reveal metadata that
may result in privacy breaches, mainly due to the
decentralised nature of data processing.
•System Efficiency: The efficiency of IoT systems is
sometimes hampered by confined computational power
and energy resources of devices. Therefore, it is crucial
to develop blockchain systems that reduce computing
and energy costs while ensuring security and scalability
[12,13].
The proposed research aims to investigate and apply
innovative methods to effectively resolve the scalability,
privacy, and efficiency difficulties that are inherent in
blockchain-based IoT systems. The use of sophisticated
consensus processes, such as Proof of Authority (PoA) and
Delegated Proof of Stake (DPoS), along with a proxy re-
encryption scheme, signifies a notable improvement com-
pared to conventional approaches. This strategy improves
scalability and decreases energy usage while guaranteeing
strong privacy safeguards in decentralised networks,
specifically in fog computing environments.
The choice of blockchain, fog computing, and specific
consensus techniques such as PoA and DpoS is influenced
by the distinct needs of IoT contexts. Blockchain is the
ideal solution for eliminating single points of failure in IoT
networks, significantly enhancing security and fault toler-
ance. Nevertheless, conventional consensus procedures
such as PoW need significant resources and are unsuit-
able for resource-limited devices commonly seen in IoT
systems. Hence, PoA and DPoS were chosen because they
lessen the computational burden and enhance scalability.
Fog computing is incorporated into the architecture to
tackle the latency and bandwidth problems linked to cloud
computing [14,15]. Bringing the processing closer to the
data source is essential for ensuring real-time responsive-
ness in IoT applications. The integration of these tech-
nologies guarantees that the suggested system can
efficiently fulfil the requirements for scalability, privacy,
and efficiency in contemporary IoT environments.
1.1 Motivation
The rationale for this research is rooted in the requirement
to address the constraints of current B-IoT systems, espe-
cially regarding scalability, privacy, and decentralised trust
management. In order to tackle these problems, there is a
vital need for a novel architectural framework that can
efficiently combine blockchain technology with IoT sys-
tems. This study aims to investigate and assess alternative
consensus methods, PoA and DPoS, that could improve
scalability and efficiency in IoT environments. Further-
more, the paper proposes a tailored privacy-preserving
approach that utilises proxy re-encryption to improve pri-
vacy in these systems. By integrating fog computing with
208 Page 2 of 15 Cluster Computing (2025) 28:208
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these consensus processes, the proposed framework seeks
to deliver a robust solution to overcome the convergence
and scalability challenges currently obstructing the wide-
spread adoption of B-IoT systems.
Blockchain provides a decentralised and secure infras-
tructure for IoT systems, allowing devices to securely
exchange data and carry out transactions without depend-
ing on a central authority. The consensus methods play a
crucial role in ensuring the efficiency of blockchain in IoT
systems by ensuring that all nodes in the network reach an
agreement on the legitimacy of transactions. The purpose
of a consensus mechanism is to enable decision-making by
a decentralised authority rather than a centralised archi-
tecture. The selection of a consensus mechanism can sub-
stantially influence the efficiency and scalability of the
blockchain network, especially in IoT systems where
resources are constrained [16]. The blockchain is a highly
suitable technology that can offer a safe and decentralised
environment for IoT networks [9]. The security features
blockchain offers are unparalleled and highly motivating
[17,18]. The idea of leveraging blockchain technology to
decentralise the IoT systems garnered significant attention
in academics and industry recently [19,20]. This approach
offers some potential benefits:
•Enhances the ability to handle faults and eliminates
vulnerabilities that could lead to system failure.
•Enables implementing secure software updates on IoT
devices.
•Ensures accountability and traceability by storing IoT
data on the blockchain in a way that cannot be changed.
•Improves security by offering authentication, access
control, and confidentiality.
•Facilitates secure micro-transactions for IoT data [21].
The main goal of our study is to assess the effectiveness of
four consensus methods, specifically PoW, PoS, PoA, and
DPoS, within the framework of B-IoT systems. These
consensus methods are selected based on their pertinence
and suitability in IoT contexts. This paper also presents a
customised privacy-preserving solution for IoT systems
based on blockchain technology using proxy re-encryption.
This will enable IoT devices to securely exchange and
analyse confidential information without compromising
privacy. Fobsim is a robust simulation tool that enables us
to evaluate the efficiency of different consensus processes
and blockchain parameters in a controlled setting [22]. Our
objective is to evaluate the efficiency of these mechanisms
by analysing transaction time and encryption overheads,
taking into account the specific demands and limitations of
IoT devices.
In order to assess the efficacy of our privacy-preserving
method, we are carrying out comprehensive experiments
utilising the Fobsim simulator. We conduct a comparative
analysis of our solution while evaluating the influence of
various consensus algorithms. Moreover, we seek to
enhance the development of B-IoT systems by constructing
a customised solution that protects privacy and assessing
its performance using the Fobsim simulator. Our research
findings provide valuable insights for developing and
implementing secure and efficient B-IoT systems.This
development is anticipated to expedite the widespread
integration of these systems across various applications.
1.2 Progress in consensus mechanisms
for the integration of blockchain and IoT
with fog computing
The present research presents innovations that enhance
blockchain-based B-IoT systems, with a specific focus on
optimizing consensus processes and utilizing fog comput-
ing to boost scalability, privacy, and decentralised trust
management. The primary contributions are as follows:
1. Algorithmic approach for blockchain-iot integra-
tion via fog computing: We have devised a method-
ical technique to seamlessly incorporate blockchain
technology into IoT devices by utilizing fog comput-
ing. This algorithm encompasses:
•Initializing the fog node: Every fog node fiis
initialized to process a certain set of tasks Tij that
are assigned to users uij. Tasks are allocated
according to user-defined parameters Bfunc , in order
to optimize the distribution of burden across the
network.
•Choosing a Consensus Mechanism: The algo-
rithm chooses between fog-based miners or stan-
dalone miners based on user preferences Bplace , thus
adjusting the system to various operational
scenarios.
•Evaluation of Performance: The execution time,
denoted as Dt, is determined by subtracting the start
time of the process, Sstart, from the end time, Send .
This calculation serves as a fundamental measure
for assessing the efficiency of the system.
2. Improved Scalability with Enhanced Consensus
Mechanisms: We examined and used consensus
procedures, specifically PoA and Delegated DPoS, in
the context of fog computing. The findings of our study
indicate that these consensus methods effectively
decrease the time sneeded to achieve consensus and
enhance the system’s capacity to handle transactions
per second (TPS), rendering them highly ideal for IoT
systems where prompt and efficient decision-making is
of utmost importance.
item Privacy-Preserving Proxy Re-Encryption:
Cluster Computing (2025) 28:208 Page 3 of 15 208
123
We have developed a proxy re-encryption algorithm
specifically tailored to safeguard user privacy in the
context of the B-IoT framework. This protocol enables
the secure re-encryption of data mby a proxy, while
maintaining the confidentiality of the original content.
It guarantees that only authorised entities may have
access to the information. The process of re-encryption
ensures the preservation of privacy throughout the
network.
3. Executing and confirming through simulation: The
suggested framework and algorithms were executed
and verified using the Fobsim simulator. The simula-
tions demonstrated enhancements in crucial measures,
such as the quantity of nodes supported (n), as well as
decreased execution durations, thereby validating the
efficacy of integrating fog-based blockchain.
The advancement in consensus methods, tackles major
challenges in the integration of blockchain with IoT. This
development offers a viable, scalable, and secure solution
for various IoT applications.
2 Advancing blockchain in IoT through fog
computing: a comparative and integrative
approach
The increasing incorporation of B-IoT ecosystems is a
significant advancement, offering improved security, pri-
vacy, and efficiency [15]. Nevertheless, as explained in the
convergence of recent studies, this combination faces sig-
nificant obstacles, particularly in the areas of security,
privacy, scalability, and system architecture [23].In this
section we examine the critical analyses presented in recent
related works, alongside their contributions and limitations.
Our research focuses on the integration of PoA and DPoS
consensus mechanisms, along with a privacy-preserving
solution based on proxy re-encryption, within a fog com-
puting environment. These concepts are simulated in
Fobsim.
The significance of addressing security and privacy in
fog computing frameworks is emphasized in papers
[24–26], particularly due to the distinctive issues presented
by the distributed nature of the IoT systems. The suggested
solutions, which include access control mechanisms based
on blockchain and decentralised and privacy-preserving
charging for electric vehicles (EVs), demonstrate the
capacity of blockchain and fog computing to enhance
security and privacy. Nevertheless, many studies primarily
concentrate on certain use case scenarios or neglect to
tackle the fundamental scaling challenges or the wider
suitability for various IoT situations. Our research addres-
ses this need by introducing a comprehensive framework
that not only guarantees security and privacy through
blockchain technology but also improves scalability and
efficiency through the utilization of PoA and DPoS. We
have tested this framework on different transactions and
consensus methods in Fobsim.
Researchers in [27] explores the convergence of IoT,
fog/cloud computing, artificial intelligence (AI), and
blockchain. It highlights the shift towards integrated next-
generation systems. The discussion on consensus mecha-
nisms and their impact on system performance is currently
limited, despite the useful insights they provide into
prospective architectures and applications. We aim to
contribute to the discussion on technology convergence by
providing simulated data that demonstrate the improved
performance of these mechanisms.
The primary emphasisin [28] and [29] is on decen-
tralised trust models and secure key management in public
fog nodes, highlighting the crucial requirement for
dependable and secure interactions in dispersed networks.
While suggesting novel blockchain-based solutions, these
studies partially neglect the inherent efficiency and scala-
bility problems of blockchain technology. We contribute to
the existing discussions by incorporating a privacy-pre-
serving technique based on proxy re-encryption. This
algorithm improves trust and security while maintaining
scalability and system efficiency, as demonstrated by our
extensive simulations in Fobsim.
Upon analysing the existing literature, it becomes clear
that progress has been made in integrating blockchain with
IoT through fog computing. However, there are still sig-
nificant gaps that need to be addressed, specifically in
terms of scalability, system-wide security, and privacy, in a
comprehensive manner. Our research tackles these short-
comings by implementing and testing a new combination
of PoA and DPoS consensus procedures with a privacy-
preserving approach. This demonstrates improved system
performance and usefulness in various IoT applications.
Our study not only makes a substantial addition to the area,
but also establishes the foundation for future investigations
into the scalable, safe, and efficient implementation of
blockchain technology in IoT environments.
The Table 1presents a comparative analysis that
showcases several methods of improving security, privacy,
and efficiency in IoT applications by combining blockchain
with fog computing.
3 Proposed fog-based blockchain
architecture
In this section of our article, we present a methodical way
to incorporating blockchain technology into IoT systems
by leveraging fog computing. Our methodology utilises a
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mathematical framework to articulate the structure and
operational sequence of the system, guaranteeing accuracy
and lucidity in elucidating our suggested resolution.
Mathematical expressions and functions provide a clear
and precise way to represent the algorithmic steps used in
the system’s operation as depicted in Algorithm 1.
Table 1 Comparative analysis of approaches to blockchain and fog computing integration in IoT
Study focus Proposed solution Limitations IoT scenario Consensus
mechanism
Security and
privacy in fog
computing
Blockchain-based access control for IoT
devices
Scalability issues; Limited to specific
IoT scenarios
General IoT
applications
Not
specified
Efficiency in
carpooling
Blockchain-assisted vehicular fog
computing for privacy-preserving
carpooling
Matching challenge; Increased
overhead
Carpooling in smart
transportation
Not
specified
Charging system for
EVs
decentralised, privacy-preserving
charging using blockchain and fog
computing
Communication overhead;
Vulnerability to cyber-attacks
Electric Vehicle (EV)
charging
Not
specified
Digital
transformation
Convergence of IoT, fog/cloud
computing, AI, and Blockchain
Absence of consensus on reference
models; Adoption in infancy phase
General next-
generation systems
Not
specified
Trust in public fog
nodes
decentralised reputation management
using Ethereum blockchain
Security, performance, and cost
considerations
Public fog services
for IoT
Ethereum
blockchain
Secure key
management
Blockchain-based group key management
in fog systems
Requires secure analysis and
performance evaluation
Secure
communications
among fog nodes
PoW
1: P←LoadP arameterC onf iguration(”Sim parameters.json”)
2: R←InitializeRunI nstance()
3: (Bfunc,B
place)←GetU serP r eferences()
4: for each i∈{1,2, ..., n}do
5: fi←I nitializeF ogN ode(i)
6: for each j∈{1,2, ..., m}corresponding to fido
7: uij ←InitializeU ser(j, fi)
8: AssignT asks(uij ,B
func)
9: end for
10: end for
11: if Bplace =1then (Fog-based)
12: M←I nitializeM inersAtF og(F)
13: else
14: M←InitializeS tandaloneM iners()
15: end if
16: C←CreateGenesisBlock(M)
17: Sstart ←GetC urrentT ime()
18: DistributeT ask s(U, F )
19: ExecuteC onsensus(M, G)
20: Send ←GetCompl etionT ime()
21: ElapsedT ime =Send −Sstar t
Algorithm 1 Blockchain-IoT integration via fog computing
Cluster Computing (2025) 28:208 Page 5 of 15 208
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3.1 Resolving issues of scalability, privacy,
and efficiency:
The fog-based blockchain framework is purposefully
developed to tackle the pressing issues of scalability, pri-
vacy, and efficiency that are crucial in IoT environments.
As stated in the introduction, conventional blockchain
consensus algorithms such as PoW and PoS are frequently
inappropriate for IoT systems due to their significant
computational and energy requirements. This section
showcases how the suggested architecture effectively
addresses these problems by utilizing fog computing and
unique consensus procedures.
•Scalability: The architecture utilizes fog computing to
shift computational duties nearer to the network edge,
thus lessening the burden on the central blockchain
network and facilitating efficient system scalability.
The algorithm presented in Algorithm 1 enables the
system to effectively handle an increasing number of
IoT devices and transactions while maintaining optimal
performance by dynamically creating and assigning fog
nodes.
•Privacy: In order to deal with the privacy concerns that
are naturally present in decentralised networks, the
architecture includes a PRE mechanism. This approach
guarantees that while data is dispersed among numerous
nodes, only authorised entities have the ability to
decode and retrieve the information. This ensures the
privacy of confidential information, which is especially
crucial in IoT systems where personal and possibly
sensitive data is often transferred.
•Efficiency: The suggested design improves system
efficiency by improving the allocation of duties
between local processing at fog nodes and blockchain
validation. The system efficiently manages computa-
tional activities by either processing them locally or
passing them to the blockchain, based on predefined
criteria. This approach avoids unnecessary computa-
tional overhead and reduces energy consumption. The
utilization of the dual-pathway processing technique, as
described in Algorithms 2 and 3, guarantees the
system’s functionality even in IoT situations with
limited resources.
The process commences by configuring system parameters,
represented as P, which are retrieved from the established
configuration file named ‘‘Sim_parameters.json’’. This
configuration includes crucial information required for the
initiation and functioning of the B-IoT infrastructure. The
parameters of the system consist of the quantity of fog
nodes (n), the total amount of users (m), as well as par-
ticular operational variables such as the blockchain feature
(Bfunc) and the placement strategy (Bplace ). A distinct
occurrence of a run, denoted as R, is established to
supervise and uphold the simulation state. This guarantees
that every instance of the system is both replicable and
uniform. Afterwards, the system captures the user’s spec-
ified preferences for the functionality and location of the
blockchain (Bfunc;Bplace ). These preferences then influence
the setup and operating phases of the entire system. Fig-
ure 1depicts the architecture of proposed system.
In the subsequent stage, the network infrastructure is
dynamically created. This involves setting up fog nodes,
denoted as F¼ff1;f2;...;fng, and assigning users to these
nodes, denoted as U¼Sn
i¼1Uiwhere Ui¼
fui1;ui2;...;uimgrepresents the group of users connected
to fog node fi. Every user, denoted as uij , is initialised and
allocated a set of tasks according to the defined blockchain
functionality, which mimics the creation and distribution of
IoT data within the fog layer.
Miners, denoted as M, are initialised in accordance with
the blockchain placement method (Bplace). This initializa-
tion is crucial for establishing the decentralised consensus
process that is vital for the functioning of blockchain
operations. The genesis block, represented as C, is created
and sent to all miners that are part of the network. This
marks the beginning of the blockchain and establishes a
shared foundation for all participants in the network.
Once the infrastructure is established, the system enters
its operational phase, during which tasks generated by
users are distributed to the blockchain through fog nodes.
The operational phase is defined by the start (Sstart ) and end
(Send) timestamps. During this phase, the main blockchain
operations of validating and adding transactions are carried
out. These operations are performed by miners through the
consensus mechanism.
The efficiency and scalability of the proposed system are
assessed by measuring the elapsed time,
ElapsedTime ¼Send Sstart. This score measures the sys-
tem’s ability to efficiently process and record IoT data
transactions on the blockchain. It highlights the benefits of
combining blockchain technology with IoT through fog
computing to overcome existing challenges.
4 Operational algorithms and their impact
on system architecture
In this phase we explore the operational details of fog
nodes, blockchain nodes, and the privacy preservation
technique. Here we demonstrate how these algorithms
work together to create a strong, efficient, and safe system
for combining blockchain technology with IoT through fog
computing. The operational algorithms are designed to
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address the core challenges of scalability, privacy, and
efficiency, enabling a robust, efficient, and secure system
for managing IoT data.
4.1 Pivotal algorithmic innovations
The working algorithms have made significant novel con-
tribution.The operational algorithms presented in this part
are crucial to the novel nature of the proposed fog-based
blockchain architecture. These solutions are crucial for
integrating blockchain with IoT systems and directly
tackling the major obstacles of scalability, privacy, and
efficiency that impede the mainstream use of blockchain in
IoT.
•Fog Node Operation Algorithm (Algorithm 2):This
algorithm presents an innovative method for handling
IoT tasks in a decentralised framework. The technique
enhances the utilisation of local computational
resources and reduces the load on the central block-
chain network by evaluating and handling jobs at the
fog node level in a dynamic manner. This strategy
effectively addresses the issue of scalability by allowing
the system to efficiently manage a larger number of
transactions, dispersing the workload throughout the
network.
•Algorithm 3: The algorithm plays a vital role in
ensuring the security and integrity of IoT data as it
undergoes processing within the blockchain. The algo-
rithm guarantees the reliability and security of the
decentralised consensus process by systematically ver-
ifying transactions and grouping them into blocks. This
improves the system’s capacity to expand while
upholding strong security measures, demonstrating the
uniqueness of the concept in offering a decentralised,
scalable solution.
•Algorithm 4: The incorporation of PRE into the
suggested framework is an innovative approach to
address the privacy issues that are inherent in IoT
systems. This technique enhances privacy protection by
encrypting transactions at the fog node level and
restricting decryption access to authorised recipients.
This strategy is highly novel in the context of integrat-
ing blockchain and IoT, as existing methods often fail
to adequately protect sensitive data over decentralised
networks.
The Fog Node Operation Algorithm (Algorithm 2) is cru-
cial for coordinating the interaction between IoT devices
and the blockchain through fog computing. When the
system starts, the algorithm Fbegins by retrieving the
configuration parametersCfrom ‘‘Sim_parameters.json’’,
which contains the overall architectural plan. The param-
eters specified here determine the operational behavior of
the system, such as the total count of fog nodes (n), client
entities (m), and the operational characteristics of the dis-
tributed ledger. Upon receiving tasks T, each individual
task tis assessed based on the storage policy SP and the
processing capabilities FNC of the fog node. The fog node
directly handles tasks that are appropriate for local pro-
cessing, utilizing its computational resources to ensure
timely and efficient data handling. On the other hand, jobs
that require blockchain validation or those that surpass the
fog node’s processing limit are automatically sent to the
blockchain B. This guarantees data integrity and security
Fig. 1 Simplified
flowchart illustrating the
integration process of
blockchain technology with IoT
through fog computing
Cluster Computing (2025) 28:208 Page 7 of 15 208
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by using decentralised consensus processes. The utilization
of a dual-pathway method in task processing enhances the
efficiency of the system by distributing the workload
between local computation and blockchain integration.
Algorithm 3is the core component of the system’s
decentralised validation mechanism. The algorithm Bis
essential for preserving the integrity of IoT data transfers.
The algorithm begins by initializing with the current state
of the blockchain CBS and a collection of pending trans-
actions PT . It then proceeds to methodically validate each
transaction tx against the existing records of the block-
chain. The network’s miners Mmine a new block by
aggregating valid transactions, following the blockchain
placement technique Bplace. The validated transactions are
encapsulated in a mined block, which is subsequently
added to the blockchain, resulting in an update of the
current state of the blockchain CBS to a new state NBS.
This procedure not only ensures the security of transac-
tional data but also strengthens the system against harmful
operations by using cryptographic consensus.
Algorithm 3 Blockchain node operation algorithm
Ensuring the secrecy and privacy of interactions within
this unified framework is the Privacy Preservation System
as depicted in Algorithm 4. This approach Pleverages
PRE to encrypt transactions Tfor secure transmission over
the network. For each transaction tand recipient recipient’s
public key PK, the algorithm secures the data utilizing the
sender’s private key SK, yielding a collection of encrypted
transactions ET . This encryption technique protects the
data from illegal access, guaranteeing that only designated
recipients can decrypt and access the transactional infor-
mation. Implementing Privacy Enhancing Technologies
(PRE) in the fog computing framework strengthens the
security of the system, offering a strong layer of privacy
protection for transactions involving IoT data.
1: function FogNodeOperation(T,B,SP,FNC)→TS
2: C←LoadConfiguration
3: for all t∈T do
4: if SP(t) = “local” & FNC ≥Required(t)then
5: ProcessTaskLocally(t)
6: else
7: ForwardTaskToBlockchain(t, B)
8: end if
9: end for
10: TS←MonitorAndManageTasks(T)
11: return TS
12: end function
1: function BlockchainNodeOperation(PT ,CBS)→NBS
2: M←InitialiseMiners(Bplace)
3: GenesisBlock ←GenerateGenesisBlock
4: for all tx ∈PT do
5: if IsValidTransaction(tx, C BS)then
6: AddTransactionToBlock(tx)
7: end if
8: end for
9: NewBlock ←MineNewBlock(M,CBS)
10: NBS ←AddBlockToBlockchain(NewBlock,CBS)
11: return NBS
12: end function
Algorithm 2 Fog node operation algorithm
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Algorithm 4 Privacy preservation mechanism of transactions
1: function PrivacyPreservation(T,PK,SK)→ET
2: PRE ←InitialisePRE
3: for all t∈T do
4: for all r∈PK do
5: ET ← ET ∪ {EncryptTransaction(t, r, SK)}
6: end for
7: end for
8: return ET
9: end function
4.2 Impact on system architecture
and performance
The integration of these algorithms yields a system design
that is not only exceedingly efficient but also possesses the
ability to scale and maintain security. The proposed solu-
tion is a considerable improvement over current methods as
it offers a full framework that combines blockchain and
IoT in an affordable and sustainable way. The utilisation of
fog computing as an intermediary layer is notably ground
breaking, as it enables the efficient allocation of jobs and
resources, hence improving the overall efficiency of the
system.
5 Evaluation and discussion
We conducted a thorough analysis where we used the
Fobsim simulator to assess the performance metrics of four
prominent blockchain consensus mechanisms: PoW, PoS,
PoA, and DPoS. This analysis sought to determine the
influence of these techniques on the duration it takes to
reach a consensus, the rate at which transactions are pro-
cessed, and the efficiency of cryptographic processes in a
blockchain environment specifically designed for IoT
applications.
5.1 Optimizing blockchain consensus
In the dynamic realm of the blockchain, identifying the
most suitable consensus mechanism that matches the
unique requirements of IoT applications has become of
utmost importance. PoW and PoS are traditional consensus
mechanisms that have established the fundamental security
and operational frameworks for blockchain networks. As
we explore the incorporation of blockchain into IoT
ecosystems, it becomes more apparent that specific
challenges presented by these systems, such as limited
energy resources and the requirement for fast data pro-
cessing, necessitate customised solutions. This under-
standing has spurred the investigation of alternate
consensus procedures, particularly PoA and DPoS. These
mechanisms aim to overcome the constraints of their pre-
decessors by providing improved scalability, energy effi-
ciency, and throughput, which makes them particularly
well-suited for IoT applications. This section aims to
conduct a comparative analysis to uncover the factors
contributing to the superior performance of PoA and DPoS
in the context of integrating blockchain with IoT as
observed from simulation results in Figs. 2and 3.
5.2 PoW
PoW consensus process, which was originally employed by
Bitcoin, necessitates nodes (miners) to solve intricate
cryptographic problems in order to authenticate transac-
tions and generate new blocks. Although this procedure is
secure, it consumes a large amount of energy and faces
scaling challenges because of the substantial computational
power it demands.
Fig. 2 A graphical representation showing the relationship between
the number of network nodes and the time required for consensus,
comparing PoW, PoS, PoA, and DPoS mechanisms
Cluster Computing (2025) 28:208 Page 9 of 15 208
123
Equation for PoW scalability limitation:
TPoW ¼1
DS
Here, TPoW represents the rate at which transactions are
processed, Drefers to the complexity of the cryptographic
challenge, and Sdenotes the time it takes to generate a
block.
5.3 PoS
PoS consensus mechanism chooses validators based on the
amount of stake they hold in the network, rather than their
proficiency in solving cryptographic challenges. This
greatly decreases energy usage but also brings about
potential centralization concerns, as organizations with
larger stakes have a greater likelihood of being selected as
validators.
Validator selection probability in PoS:
Pv¼Sv
TS
Let Pvrepresent the probability of being chosen as a val-
idator, Svrepresent the stake of the validator, and TSrep-
resent the total stake in the network.
5.4 PoA
PoA is dependent on a restricted set of pre-authorised
validators, which makes it particularly suitable for private
or consortium blockchains where all players are identified
and have established trust. This technique facilitates effi-
cient processing of large volumes of transactions with
minimal delay, making it well-suited for applications that
demand swift data processing.
Throughput enhancement in PoA:
TPoA ¼N
L
where TPoA is the transaction throughput, Nis the number
of transactions, and Lis the latency or the time it takes to
add a new block.
5.5 DPoS
DPoS improves upon PoS by implementing a voting
mechanism in which stakeholders select a small group of
delegates to verify transactions. This system achieves a
harmonious equilibrium between decentralization and
efficiency, enabling a large number of transactions to be
processed quickly while ensuring security through the
supervision of stakeholders.
Scalability Factor in DPoS:
TDPoS ¼NV
D
where TDPoS is the transaction throughput, Nis the number
of transactions, Vis the number of validators (delegates),
and Dis the duration of the block generation cycle.
The simulations and accompanying analysis highlight
the notable disparities in performance among various
blockchain consensus techniques, with PoA and DPoS
demonstrating great potential for applications that demand
extensive scalability and streamlined transaction process-
ing. Proxy re-encryption was employed to enhance security
measures, guaranteeing transaction confidentiality while
maintaining processing efficiency. These insights are
essential for blockchain stakeholders to make well-in-
formed judgements about choosing a suitable consensus
method that is customised to meet unique operational needs
and network conditions. Future study will focus on inves-
tigating the incorporation of more sophisticated encryption
methods and evaluating their influence on the overall net-
work performance in various blockchain situations.
The graphical analysis of consensus time depicted the
relationship between the number of nodes and the time
needed to achieve consensus as outlined in Fig. 2. As seen,
PoW had a notable rise in time as the number of nodes
involved increased, indicating its computational and scal-
ability constraints. However, PoA and DPoS demonstrated
significantly shorter durations, indicating improved scala-
bility and efficiency, making them more suitable for bigger
networks. PoS emerged as a compromise solution, offering
superior performance compared to PoW but falling short of
the efficiency achieved by PoA or DPoS in terms of
achieving agreement rapidly.
The second visualisation examined the efficacy of each
consensus mechanism in terms of processing transactions
per block and the corresponding time taken as depicted in
Fig. 3. DPoS has emerged as the most efficient method,
validating transactions more quickly than the others, with
Fig. 3 Efficiency comparison of PoW, PoS, DPoS, and PoA
consensus mechanisms by transactions per block versus time taken
208 Page 10 of 15 Cluster Computing (2025) 28:208
123
PoA coming in a close second. Both mechanisms surpassed
PoS and PoW, with PoW falling behind due to its greater
processing requirements. These findings emphasise the
potential of stake-based and authority-based procedures in
improving the ability to process transactions within
blockchain networks.
Another result on the durations required for encrypting
and decrypting transactions to ensure anonymity as
depicted in Fig. 4. PoA demonstrated the shortest dura-
tions, hence delivering exceptional efficiency for applica-
tions that necessitate prompt and secure transaction
capabilities. This study incorporated proxy re-encryption to
guarantee transaction privacy, which demonstrated signif-
icant efficacy across all evaluated consensus processes.
5.6 Energy efficiency analysis of consensus
mechanisms
The boxplot in Fig. 5, effectively illustrates the disparities
in energy usage among the four consensus mechanisms,
considering different numbers of nodes, ranging from 2 to
32. Out of these options, PoA is the most energy-efficient
method, since it has the lowest average energy consump-
tion and a narrow interquartile range (IQR), which suggests
very little fluctuation in energy usage. PoA’s great effi-
ciency makes it well-suited for environments like IoT
systems, where energy conservation is crucial.Moreover,
below is the analysis energy efficiency of each consensus
mechanism.
PoW exhibits the most significant aggregate energy
usage, varying between 10J and 50J. The box plot
demonstrates a significant range, suggesting a substantial
degree of variability in energy use with an increasing
number of nodes. The unpredictability in energy use is
mostly caused by the computationally demanding nature of
the PoW algorithm. Mining blocks involves solving intri-
cate cryptographic riddles, which results in substantial
energy usage, particularly as the network expands.
PoS is a consensus mechanism that requires less energy
compared to PoW, with energy usage varying from 8J to
40J. The PoS box represents a distribution that is more
moderate, indicating that the energy consumption of PoS is
less variable. Due to its reliance on staking instead of
computational work, PoS leads to reduced energy demands
overall. Nevertheless, the existence of certain fluctuations
emphasises that as the network expands, the energy
requirements may rise, albeit not to the same degree as
PoW.
PoA is a highly energy-efficient technique, with energy
consumption estimates ranging from 5J to 25J. The limited
range of the box plot suggests that there is very little
variance in energy usage, even when the number of nodes
grows. The reason for this is because PoA depends on a
predetermined group of trusted validators, which removes
the necessity for computationally intensive calculations.
PoA is extremely appropriate for IoT applications due to its
low energy consumption, which is crucial for ensuring
energy efficiency.
DpoS is a consensus mechanism that is moderately more
energy-intensive than PoA, however it remains efficient,
consuming between 6J and 30J of energy. The box plot
illustrates a little wider range of values compared to the
PoA, suggesting the presence of variability in energy usage
as additional nodes are included. DPoS employs chosen
delegates to authenticate blocks, leading to reduced energy
usage in comparison to PoW and PoS, albeit slightly higher
than PoA due to the additional burden of managing the
delegation mechanism.
The box plot unambiguously demonstrates that PoA is
the most energy-efficient consensus method, making it very
well-suited for settings such as IoT where energy limita-
tions are crucial. DPoS is a close follower, while PoS
provides a balance between energy efficiency and scala-
bility. PoW, on the other hand, is the most power-intensive
process, requiring significant energy usage and fluctuation,
Fig. 4 Comparative analysis of encryption and decryption times for
consensus mechanisms PoW, PoS, DPoS, and PoA
Fig. 5 Box plot of energy consumption for different consensus
mechanisms across various node counts
Cluster Computing (2025) 28:208 Page 11 of 15 208
123
especially as the number of nodes grows. This analysis
illustrates that PoA and DPoS are more suitable for energy-
efficient applications, such as decentralised IoT networks.
In contrast, PoW may not be sustainable in these situations
due to its substantial energy requirements.
5.7 Comparative analysis
PoA and DPoS consensus mechanisms present significant
advancements in scalability and energy efficiency com-
pared to PoW and PoS due to their less demanding com-
putational consensus procedures and streamlined selection
processes for validators. In contexts such as the IoT, where
devices frequently function with limited energy resources
and necessitate prompt data processing, PoA and DPoS
offer notable benefits. These principles facilitate the
development of scalable and efficient blockchain networks
that can handle the high-volume and fast transaction pro-
cessing required by modern IoT applications. The exami-
nation of PoA and DPoS as alternative methods for
reaching consensus has revealed their significant benefits
compared to typical consensus, especially in the context of
IoT applications. Proof of Authority and Delegated
Proof of Stake address the operational needs of IoT
ecosystems by focusing on energy efficiency, scalability,
and transaction speed. Additionally, they contribute to
creating a more sustainable and efficient blockchain envi-
ronment. The implementation of these methods represents
a significant advancement in addressing the existing diffi-
culties related to blockchain technology, signaling the
arrival of a new era of IoT solutions powered by block-
chain. As we explore the challenges of incorporating
blockchain technology into the B-IoT, the knowledge
gained from this comparative examination of consensus
mechanisms emphasizes the significance of choosing the
suitable consensus model. This model should be in line
with the specific requirements and limitations of the given
application. By doing this, we move closer to fully ful-
filling the potential of blockchain technology in empow-
ering IoT systems. This ensures that the systems are not
just safe and decentralised, but also efficient and scalable.
We utilised the PoA mechanism in our comparison
analysis due to its superior performance in our system.
When assessing the time it takes to encrypt and decrypt
data of different sizes (ranging from 5 KB to 50 KB), the
PoA-based method consistently performed better than
alternative approaches in state of art study, such as LYW
[30], ZYL [31], LWY [32], SYY [33], YZZ [34], and BPF
[35].The Figs. 6and 7depict our analysis. The PoA
methodology shown substantial enhancements in process-
ing durations, especially when dealing with bigger data
quantities. In comparison to other options, the suggested
approach consistently achieved reduced encryption and
decryption durations. Integrating the PoA mechanism into
our system offers scalability and efficiency advantages,
making it the best solution for IoT applications that need
quick and safe data exchanges.
6 Future research insights
Our research explores the combination of blockchain
technology and the Internet of Things using fog computing.
We have developed a foundation that combines the effi-
ciency of PoA and DPoS consensus mechanisms with the
improved privacy provided by proxy re-encryption. In the
future, there are various possibilities for further enhancing
and broadening this integration:
•Advanced privacy mechanisms: Expanding on the
proxy re-encryption architecture, future studies could
investigate more advanced cryptographic methods, such
as homomorphic encryption or zero-knowledge proofs,
to enhance privacy while maintaining system effi-
ciency.In addition, the exploration of differential
Fig. 6 A comparative analysis of encryption times for different
algorithms, highlighting the performance of the proposed method
across varying data sizes
Fig. 7 Decryption times for multiple algorithms, showing the
improved efficiency of the proposed method as data size increases
208 Page 12 of 15 Cluster Computing (2025) 28:208
123
privacy strategies may improve privacy assurances,
especially in IoT scenarios in occurrences of sensitive
user data.
•Exploring the development of adaptive consensus:
Algorithms that can dynamically select the best
suitable consensus mechanism depending on current
network conditions, transaction volumes, and privacy
needs is an attractive research area in the field of
consensus mechanism selection. Alternative lightweight
consensus mechanisms like proposed in [36–38] pro-
vides a significant platform for further enhancements.
By integrating AI-based decision models, such algo-
rithms have the potential to greatly enhance system
performance and optimise resource utilisation in real-
time. It therefore has the capacity to improve both
effectiveness and adaptability in the selection of
consensus mechanisms, especially for dynamic IoT
situations.
•Cross-chain interoperability: is crucial as blockchain
ecosystems grow, enabling smooth integration between
various blockchain networks and consensus procedures.
Future research should prioritise the development of
standardised protocols or frameworks to optimise cross-
chain transactions, guaranteeing effective data sharing
and coordination among heterogeneous blockchain
systems. Moreover, utilising multi-chain architectures
could allow for the concurrent execution of jobs over
numerous chains, hence enhancing overall scalability.
•IoT-specific blockchain designs: Creating blockchain
architectures specifically designed for IoT applications,
while considering the unique limits and requirements of
IoT devices, has the potential to significantly enhance
scalability, security, and efficiency. Potential future
investigations may delve into the utilisation of light-
weight consensus approaches, sharding, or sidechains to
guarantee swift and efficient processing for IoT net-
works, specifically in contexts with limited resources
such as smart cities or remote monitoring systems.
•Emerging technologies integration: In addition to
blockchain, further research can explore the integration
of quantum computing to improve cryptographic secu-
rity. Alternatively, the use of distributed ledger tech-
nologies (DLT) beyond blockchain, such as Hashgraph
or Tangle, can be considered, as they provide different
consensus models that may be better suited for specific
IoT needs. In addition, the integration of 5 G networks
with edge AI could enhance the proposed fog comput-
ing architecture by offering extremely low latency and
real-time processing capabilities for IoT devices. The
combination of blockchain and 5 G technology has the
potential to facilitate more efficient and decentralised
frameworks for the IoT, allowing for faster interchange
of data.
•Comprehensive performance benchmarking:To
prove that the proposed method succeeds, full perfor-
mance benchmarking will include both detailed simu-
lations and real-world deployments in a number of
different IoT scenarios. This would allow for the
assessment and comparison of the system’s perfor-
mance, scalability, security, and energy consumption in
relation to current systems. These efforts would
produce valuable metrics for validation and reveal
insightful information, which would assist in further
refining the framework.
7 Conclusion
This study presents a comprehensive solution to the chal-
lenges encountered by blockchain-enabled IoT systems. It
suggests a fog-based blockchain structure that improves
scalability, privacy, and energy efficiency. Our architecture
combines PoA and DPoS consensus processes with a proxy
re-encryption approach for anonymity. This integration is
precisely tailored to meet the requirements of modern
B-IoT systems. The outcomes of Fobsim simulations
emphasise the advantages of PoA and DPoS compared to
conventional processes such as PoW and PoS. Analysing
the results from Sect. 5, the energy consumption and
transaction throughput, showcase PoA constantly outper-
forms other consensus mechanisms. This makes it well-
suited for IoT contexts with constrained resources. PoA
surpasses PoW and PoS in all key performance measures.
However DPoS too provides, a robust balance between
scalability and energy efficiency. Proxy re-encryption
enhances data privacy while maintaining system perfor-
mance and safeguarding sensitive IoT data throughout the
network. Data confidentiality is crucial, especially in real-
time IoT systems involving distributed devices. Overall,
the suggested system represents notable progress in com-
bining blockchain and IoT through fog computing. Our
research demonstrates that PoA and DPoS are extremely
effective consensus techniques for addressing IoT ecosys-
tems’ scalability and privacy requirements. Future research
should prioritise the development of adaptive consensus
techniques that enhance performance by considering net-
work conditions. Additionally, it is essential to investigate
the potential impact of new technologies like 5 G, quantum
cryptography, and multi-chain architectures. By persis-
tently pursuing advancements in this field, we can unleash
the complete capabilities of combining blockchain and IoT,
resulting in enhanced security, scalability, and efficiency of
IoT applications for smart cities, autonomous systems, and
other domains.
Cluster Computing (2025) 28:208 Page 13 of 15 208
123
Acknowledgements The research presented in this article was sup-
ported by J&K Science Technology and Innovation Council,
Department of Science and technology, under grant No. JKST&IC/
SRE/836-40. We are grateful for the financial support provided,
which enabled us to conduct the experiments and analyses reported in
this paper.
Author contributions The work in this paper was conducted under the
supervision of SS. IAR conducted conceptualization, experimenta-
tion, and analysis of results, while SS provided guidance, critical
feedback, and oversight throughout the research process.
Funding The research presented in this article was supported by J&K
Science Technology and Innovation Council, Department of Science
and technology, under grant No. JKST&IC/SRE/836-40. We are
grateful for the financial support provided, which enabled us to
conduct the experiments and analyses reported in this paper.
Data availability Not applicable.
Code availability Available on request.
Declarations
Conflict of interest The authors have not disclosed any competing
interests.
Ethics approval Not applicable.
Consent for publication Not required.
Materials availability Not applicable.
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Publisher’s Note Springer Nature remains neutral with regard to
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Springer Nature or its licensor (e.g. a society or other partner) holds
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terms of such publishing agreement and applicable law.
Iraq Ahmad Reshi is a Research
Scholar at the Department of
Computer Science and Engi-
neering, Islamic University of
Science and Technology,
Awantipora, Kashmir, J&K,
India. He has pursued his
B.Tech. from National Institute
of Technology Srinagar, India,
and M.Tech. from Central
University of Kashmir, India.
His research focuses on Cryp-
tography, Blockchain, AI, and
Internet of Things.
Sahil Sholla is an Assistant Pro-
fessor at the Department of
Computer Science and Engi-
neering, Islamic University of
Science and Technology,
Awantipora, Kashmir, J&K,
India. He has received a Ph.D.
from the National Institute of
Technology Srinagar, India. His
research focuses on technology
ethics, Blockchain, Security, AI
and Internet of Things.
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