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Citation: Zubaydi, H.D.; Varga, P.;
Molnár, S. Leveraging Blockchain
Technology for Ensuring Security and
Privacy Aspects in Internet of Things:
A Systematic Literature Review.
Sensors 2023,23, 788. https://
doi.org/10.3390/s23020788
Received: 16 December 2022
Revised: 5 January 2023
Accepted: 6 January 2023
Published: 10 January 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Review
Leveraging Blockchain Technology for Ensuring Security
and Privacy Aspects in Internet of Things: A Systematic
Literature Review
Haider Dhia Zubaydi , Pál Varga * , Sándor Molnár
Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics,
Budapest University of Technology and Economics, M˝uegyetem rkp. 3., H-1111 Budapest, Hungary
*Correspondence: pvarga@tmit.bme.hu
Abstract:
As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems,
the related data security and privacy issues became apparent. While the system elements and their
communication paths could be protected individually, generic, ecosystem-wide approaches were
sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains
came into the technological limelight. Blockchains offer many advantageous features in relation to
enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain
technology can provide IoT with effective and efficient solutions, there are many challenges related
to various aspects of integrating these technologies. While security, anonymity/data privacy, and
smart contract-related features are apparently advantageous for blockchain technologies (BCT),
there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate
scalability, predictability, and legal issues. This paper provides a systematic review on state-of-
the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and
privacy-related issues. The paper first provides a brief overview of BCT and IoT’s basic principles,
including their architecture, protocols and consensus algorithms, characteristics, and the challenges
of integrating them. Afterwards, it describes the survey methodology, including the search strategy,
eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the
findings of this study which illustrates different works that addressed the integration of blockchain
technology and IoT to tackle various aspects of privacy and security, which are followed by a
categorization of applications that have been investigated with different characteristics, such as
their primary information, objective, development level, target application, type of blockchain and
platform, consensus algorithm, evaluation environment and metrics, future works or open issues
(if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is
included from an architectural and operational perspective. Finally, we cover major gaps and future
considerations that can be taken into account when integrating blockchain technology with IoT.
Keywords: blockchain technology; Internet of Things (IoT); security; privacy; systematic; survey
1. Introduction
The Internet of Things (IoT) domain includes a set of rapidly emerging communi-
cation, data processing, and insight generation technologies. It involves sensors and
actuators of the physical world together with their communication means—sometimes
under resource-constrained or environmentally harsh conditions. Furthermore, it involves
data preprocessing and aggregation methods both at the network edge and in the cloud.
Regarding human-centered, application-specific needs, the overall domain of IoT also
includes methods for predictions, classifications, decision making, insight generation, and
many more. Eventually, control processes are triggered based on these decisions, which
initiate changes in the physical world, completing the working cycle of Cyber-Physical
Systems (CPS).
Sensors 2023,23, 788. https://doi.org/10.3390/s23020788 https://www.mdpi.com/journal/sensors
Sensors 2023,23, 788 2 of 43
IoT integrates appliances, services, sensors, actuators, etc., to offer connectivity so-
lutions [
1
]. It also helps to improve the system’s efficiency by processing the collected
data in real time [
2
]. However, it introduced many issues due to its resource constraints of
connected devices and decentralized architecture [
3
]. IoT covers various application areas
that revolve around people’s lives, such as the environment, healthcare, agriculture, trans-
portation, and smart home, by revolutionizing surrounding objects to improve humans’
lives [4].
IoT requires solutions in many aspects in order to be considered secure, for example,
physical security design, key management, client privacy, secure bootstrapping and trans-
mission of data, authentication, and access control mechanisms [
5
–
7
]. Many approaches
have been proposed to overcome the previously mentioned issues, such as a centralized
server–client paradigm that relies on cloud servers. However, security and privacy aspects
are still missing some pieces, and such features can be provided by blockchain technology.
We have reached a point for engineering systems where we need to answer both the
traditional requirements for system security and safety [
8
] and the newly arising need for
dynamic reorganization capabilities of supply chains and their system of systems [
9
]. It
became inevitable to present a tenable solution that addresses the above-mentioned issues
in IoT architecture to guarantee secure data exchange among IoT objects which requires
trustless authentication, security, and robustness. Blockchain technology is one of the most
trending approaches nowadays; it presents solid and robust features that can be utilized
to overcome many limitations in different domains [
10
]. IoT ecosystem transactions can
be managed securely using blockchain technology by eliminating the centralized entity
by deploying distributed and public ledgers to allow anonymity in business models [
11
].
Blockchain enables data integrity and transaction transparency through a decentralized
Peer-to-Peer (P2P) model. Many industrial and research domains are expanding their
work on top of blockchain technology which results in higher efficiency compared to the
traditional manner. An in-depth discussion is described in further sections.
Many papers have discussed the concept of blockchain technology and IoT, including
systematic reviews, applications, challenges, and solutions such as [
12
] in general, and
furthermore, in [
13
–
18
]. Moreover, further research addresses the integration of blockchain
technology and IoT, which is also heading for advanced directions, including industrial
and 5G support [
19
–
26
]. This paper will mainly focus on blockchain technology and IoT
in a systematic manner to identify new perspectives and serve as a repository for the
accumulated knowledge of these technologies in terms of research motivation, issues and
challenges, solved gaps, the performance of these technologies in transactions and end
devices, answering an important research question to identify the importance of using
blockchain technology to boost the performance of IoT, and the usage of hybrid blockchains.
Finally, the systematic manner included the recent and up-to-date approaches to identify
the research applications and areas that have been focused on by the selected studies.
The rest of this paper is organized as follows: Section 2introduces an overview of
IoT and blockchain technology in terms of their architecture, network components, charac-
teristics, and further features for each technology. Section 3describes the manner used to
include the research papers discussed in this review, including the search strategy, eligibility
criteria, selection results, and characteristics of the included articles. Section 4highlights
the findings of this study which illustrate different works that addressed the integration of
blockchain technology and IoT to tackle various aspects of privacy and security, and these
are followed by a categorization of applications that have been investigated with different
characteristics such as their primary information, objective, development level, target appli-
cation, type of blockchain and platform, consensus algorithm, evaluation environment and
metrics, future works or open issues (if any), and further notes for consideration.
Section 4
also includes a detailed discussion of all articles from an architectural and operational
perspective (Sections 4.1–4.7). Furthermore, in Section 5, we summarized the main lessons
learned, covered major gaps, and shared future considerations that can be taken into ac-
Sensors 2023,23, 788 3 of 43
count when integrating blockchain technology with IoT. Finally, the conclusion is presented
in Section 6.
2. Overview
This section provides an overview of IoT and blockchain technologies, including their
architectural design, protocols, consensus algorithms, characteristics, blockchain types, and
IoT security and privacy concerns.
2.1. IoT
2.1.1. Architecture
IoT is a combination of interconnected embedded sensors and heterogeneous devices,
where they share common features such as limited processing capabilities, small memory,
low power, and unique identifiers. IoT users can remotely provision data and access services
through deployed gateways that connect the IoT network with the outside world [2].
As stated by [
27
], “The Internet of Things allows people and things to be connected
anytime, anyplace, with anything and anyone, ideally using any path/network and any
service”. Various IoT architectures are proposed, each representing distinct perspectives
and functions. From a deep technical perspective in wireless networks, ref. [
28
] described
IoT architecture in a three-layer/tier manner, such as interfaces/services, network/ com-
munication, and perception/hardware [
29
]. However, some other applied researchers and
industrial experts consider a fourth layer. In [
30
], this is called the support layer, which par-
ticipates in fog computing, smart computing, cloud computing, etc. Another interpretation
of the fourth layer approach is depicted by Figure 1, where each layer represents different
technology approaches and the scale of architectural elements [31].
Figure 1. The generic architecture of IoT systems in a four-layered approach [31].
From a top–down perspective, the four distinguishable key parts of generic IoT
architectures are application, data processing, network, as well as sensors and actuators
layers. To accomplish various applications (for example, healthcare, smart home, and smart
transportation) of IoT devices, the application layer implements and delivers the results
of the data processing (i.e., transport) layer [
32
]. The application layer is a user-centric
layer that performs different functions on behalf of the user. The data processing layer
analyzes the data acquired in the sensing layer and determines based on the findings. The
data processing layer in various IoT devices (e.g., smartwatches, smart home hubs, etc.)
also stores the results of earlier analyses to offer a better user experience. The network
layer exchanges the results of data processing with other linked devices. The network layer
facilitates sending data from the sensing and actuators layer to other connected devices as
it serves as a communication channel. Data can be transferred across connected IoT devices
using various communication technologies, such as Z-Wave, cellular network, Bluetooth,
Wi-Fi, and Zigbee [
33
]. The primary function of the sensors and actuators layer is to identify
any events occurring in the device’s periphery and to collect real-time data [32].
Sensors 2023,23, 788 4 of 43
2.1.2. Characteristics
The Internet of Things has various advantages because of its unique characteris-
tics, such as the interconnectivity of heterogeneous systems, enormous scale, safety (e.g.,
healthcare and industrial domains), connectivity, dynamic changes, and things-related
services. Heterogeneity refers to the use of diverse devices in IoT networks and hardware
platforms; these devices are able to communicate with each other on various networks.
Inter-connectivity refers to the ability to connect everything via global information and
communication infrastructure. Safety refers to the systems affecting their external envi-
ronment, including the physical well-being of individuals and the protection of personal
data and endpoints. The enormous scale implies that the number of endpoints connected
to each other through intranets and the Internet has risen significantly, which is majorly
due to IoT devices. This growth requires further improvements in efficient data handling,
clarified semantics, and data interpretation within applications. Network accessibility and
compatibility are made possible via connectivity. Compatibility includes the control of
protocol matching and data production and consumption interfaces. Accessibility means
being able to reach the information anytime, anywhere, if authorization is provided and
the stakeholder has authenticity. When a device is asleep or waking up, connected or
disconnected, or in a specific place or at a specific speed, the state of the device changes
dynamically, and the number of devices varies dynamically: this is what is meant by
dynamic changes. Finally, things-related services include semantic coherence and privacy
protection within device restrictions or constraints, which can be completed by changing
the physical and information worlds’ technologies [34].
2.1.3. Challenges
Although IoT has numerous benefits, it introduced many challenges that must be
addressed, such as interoperability, scalability, heterogeneity, security, and privacy [
35
].
Many researchers have proposed various measures to enhance interoperability [
36
–
40
].
Interoperability describes the capacity of a system component’s technical requirements to
work together effectively, regardless of how different they are. Scalability is introduced
due to the fact that IoT is facing a tremendous issue in dealing with the rapid growth in
the number of devices. It describes the system’s ability to handle future growth without
negatively impacting its performance. Hence, when more devices are connected, scalability
must be examined to see how the system can handle it. Refs. [
41
–
43
] are examples of studies
on the scalability issue. Since the IoT network consists of a huge number of devices, it is a
prominent illustration of the heterogeneity issue. When it comes to IoT, the primary goal is
to provide a standard abstraction approach and maximize the functionality of connected
devices. Due to the rapid expansion of IoT, there is a wide range of hardware and software
configurations that the developers are striving to create an application that can work on top
of them. Some examples of prior work to address the issue of heterogeneity are provided
in [44].
New security vulnerabilities of system-of-systems appear due to the growth of IoT,
which are caused by heterogeneity, decentralization, and individual vulnerabilities of IoT
systems [
45
,
46
]. The complexity of deploying security mechanisms in resource-constrained
IoT networks [
47
] resulted in difficulty in implementing traditional security techniques
such as encryption, authentication, and authorization which might not be appropriate any-
more. Furthermore, a complex cyber-physical system-of-systems may require autonomous
approaches to handle security and safety issues [
48
,
49
]. Additionally, IoT devices are sus-
ceptible to malware activity due to the inability of security firmware to be updated on
a timely basis [
50
]. In addition to security, it is difficult to maintain data privacy. There
is a growing tendency to combine IoT with cloud computing, which provides IoT with
additional storage power and computing abilities. However, data may be compromised if
uploaded to third-party cloud servers, which are prone to privacy breaches [51].
Security and privacy aspects are the main focus of this paper because IoT has many
issues within this area, and the research on integrating blockchain technology with IoT is
Sensors 2023,23, 788 5 of 43
mainly conducted to enhance these solution aspects. Managing security and privacy risks
should be a top goal for increasing consumer acceptance of IoT applications. In addition,
as IoT devices and related apps grow increasingly common in people’s daily lives, they
must be completely secure. Security and privacy aspects in IoT may raise serious concerns
due to a lack of proper authentication and authorization procedures. IoT protocols operate
at different layers, which are a favorite target for hackers who always strive to identify new
methods to intercept IoT connections even when proper authentication tools are used [
28
].
For example, possible attacks on each protocol in a specific layer include slowloris, cross-
site scripting, HTTP flooding, DDoS, and repudiation attacks that target the application
layer. The data processing layer is targeted with exhaustion attacks and targeted malware.
The networking layer is further vulnerable to injection, smurf, SYN flooding, opt-ack, Sybil,
sinkhole, wormhole, and other attacks. Further resource consumption, byzantine, and IP
address spoofing attacks are in sight regarding the actual blockchain network. Finally, there
physical damage or destruction, access control, and the disconnection of physical links
are attacks toward the sensors and actuators layer [
52
]. Some attacks mainly target IoT
layers based on the system, such as Wireless Sensor Networks (WSN) and Radio Frequency
Identification (RFID). For example:
• When using WSN:
–Physical/Link layer: Synchronization, selective forwarding, replay attacks.
–Network/Transport layer: Sinkhole, false routing, eavesdropping attacks.
–Application layer: Buffer overflow and injection attacks.
• When using RFID:
–Physical/Link layer: Replay, sybil, passive interference attacks.
–Network/Transport layer: Eavesdropping, impersonation, spoofing attacks.
–Application layer: Tag modification, buffer overflow, injection attacks.
2.2. Blockchain
2.2.1. Architecture
Blockchain is defined as a “set of chronologically ordered blocks” or a digital dis-
tributed ledger that maintains time-stamped transactions which are managed using unique
algorithms to keep track of all blocks on the chain [
53
]. Each computer in the network is
represented as a node where they share a duplicate copy of the data (“digital ledger”). All
nodes in the blockchain utilize the same algorithm to reach an agreement called “consen-
sus”. Blockchain technology operates in a distributed Peer-to-Peer (P2P) manner, which
offers many advantages over traditional or centralized architectures, such as eliminating a
single point of failure, which provides the network with high reliability and allows network
nodes to work in a coupled manner which increases the computing power.
Successive blocks of all transactions on the blockchain are linked together as depicted
in Figure 2, where the previous block (N
−
1) is linked with the current block (N), which
is also in turn linked to the next block that will be added to the blockchain (N+1). Addi-
tionally, blockchain technology has enabled the implementation of the “smart contracts”
concept. It can be defined as computer programs or protocols that allow an agreement to be
automatically enforced based on a set of specified conditions. The smart contracts specify
the implemented application logic, making it an ideal component for extending blockchain
technology to new domains [
54
]. Great examples of widely spread implementation for
blockchains are Ethereum [
55
] and Hyperledger [
56
], which also include the capability of
smart contract handling. In general, integrated blockchain technologies are designed to
provide the following characteristics: decentralization, anonymity, autonomy, transparency,
privacy, security, and collective verification [57].
Sensors 2023,23, 788 6 of 43
Figure 2. Blockchain structure.
2.2.2. Consensus Algorithms
Consensus algorithms are essential to specify a set of rules and perform procedures
when there is no mutual trust between network participants. By their very nature, they
incentivize participating nodes to be trustworthy and produce or add new blocks to the
blockchain. The use of consensus algorithm started and was utilized in cryptocurrency-
based systems. Then, it was further extended to incorporate various applications, since
each domain has its own set of requirements. Consensus algorithms represent the key
function, demonstrating the methodology required to achieve absolute agreement between
participants when verifying a new block. There has been increasing interest in existing
consensus and replication processes, which can be used in blockchain systems [58].
Currently, consensus algorithms are employed in a variety of applications, such as
banking and finance, supply chain management, healthcare, real estate, media, record
management, and cybersecurity. Examples of consensus algorithms include: Proof of Work
(PoW) [
59
], Proof of Stake (PoS) [
60
], Delegated Proof of Stake (DPoS) [
61
,
62
], Transactions
as Proof of Stake (TaPoS) [
60
], Proof of Activity [
63
], Proof of Capacity, Byzantine Fault Tol-
erance (BFT), Replication [
64
], Practical Byzantine Fault Tolerance (PBFT) [
65
,
66
], Delegated
BFT (DBFT), BFTRaft [
67
], Proof of Authority (PoA), Proof-of-Stake-Velocity (PoSV) [
68
],
Proof of Burn [
69
], Proof-of-Personhood (PoP) [
70
], Proof of Bandwidth (PoB) [
71
], Proof
of Elapsed Time (PoET) [
72
], Stellar Consensus Protocol (SCP) [
73
], Bitcoin-NG [
74
],
Sieve [
75
], Ripple [
76
], and Tendermint [
77
]. Further details on different consensus al-
gorithms, characteristics, advantages, and disadvantages can be found in [78–80].
2.2.3. Types of Blockchains
Blockchain technology has been employed in a variety of applications and areas. From
the access point of view, there are three types of blockchains, each of which serves a distinct
purpose for specific applications: public, private, and federated.
Public blockchains have no centralized government or regulatory entities. The public
chain has a high number of participating nodes, and its nodes’ trust level is the lowest
of the three blockchain classifications. The public blockchain is employed in various IoT
applications, including smart agriculture, smart healthcare, smart traffic, etc. A public
blockchain is described as scalable, dynamic, and decentralized, and it supports over
100,000 nodes. However, it has many drawbacks, such as high latency, low throughput,
high electricity consumption, and high computing power consumption, and it is susceptible
to 51% of attacks [81].
Private blockchain: The private organization determines this type, and network nodes
have varied permissions. The private blockchain is entirely controlled by a single entity,
which has the authority to select the final consensus [
82
]. A private blockchain can reach
consensus quickly and is able to resolve byzantine failures, but its complexity is high even
if the number of nodes is low.
Consortium blockchain (federated blockchains): Participation, read, and write per-
missions are all governed by a set of rules. The consortium blockchain has fewer nodes
than the public chain, but there is some trust among the nodes. This type is mainly used
in the financial (banking) industry [
83
] and is gaining momentum in production-oriented
supply chains [
84
]. These are becoming connected with Central Bank Digital Currencies
(CBDC) in order to facilitate flexible digital payments for industrial partners [85]. Consor-
tium blockchain solves the Byzantine failure problems and contains multiple consistency
algorithms. The main disadvantage of this type is its high complexity.
Sensors 2023,23, 788 7 of 43
2.3. Challenges of Integrating Blockchain Technology and IoT
Although blockchain technology can provide IoT with effective and efficient solutions,
there are many challenges related to various aspects of integrating these technologies
together. These include integration challenges with security-related system components,
anonymity and data privacy, smart contracts, storage capacity and scalability, resource
utilization, predictability, and legal issues.
Predictability is crucial in IoT because devices must be able to communicate with
their surroundings in real time, which implies that the amount of time it takes for things
to interact and the amount of latency between devices must be limited. Many consensus
algorithms, such as PoW and PoS, are probabilistic when finalizing a transaction in the
blockchain. At the same time, the confirmation confidence of the transaction in confusion is
also probabilistic. Including predictability concerns in the blockchain, the design remains
a key challenge. Predictability is essential for IoT-based healthcare applications [
86
]. For
both manufacturers and service providers, the blockchain presents a severe issue because it
connects individuals from diverse locations without any legal or compliance code to follow.
Problems arise when private keys are retrieved or reset and transactions are reversed
because there is a lack of rules on how to behave in such situations. It is unclear whether a
worldwide, unique blockchain for IoT devices is intended to be governed by manufacturers
or open to users in some IoT applications. Legal regulations are a crucial part when
integrating blockchain with any other technology [87].
High heterogeneity and the lack of performance of IoT devices caused security issues
at multiple levels. In addition, wireless communication and mobility are an additional
set of properties that require security. More secure IoT design is even more essential due
to the severe consequences caused by the growing number of attacks on IoT networks.
Blockchain is widely viewed as a crucial technology for improving the security of IoT.
However, the major barrier in integrating blockchain with IoT is the reliability of IoT
devices’ data, because when the content of data is changed or damaged before it arrives on
the blockchain, it will be stored as it is in the chain. Thus, blockchain is unable to identify
and verify the integrity of data. Although data can be corrupted by malicious activities,
it can be calculated in a wrong manner due to a failure in the devices themselves or any
parts of them. Thus, before integrating IoT devices into blockchain technology, they must
be properly tested to ensure that they will not cause damage to the system and must be
placed in the correct location to prevent physical damage from occurring.
Anonymity and data privacy are critical issues for many IoT applications, especially
when the device is related to a person, such as in e-health applications. In such applications,
anonymity needs to be guaranteed, which is why blockchain is considered an ideal ap-
proach. Because data are collected and progress to application and communication levels,
it is challenging to deal with data privacy even when many solutions have already been
considered. Secure data storage is challenging due to the need for cryptographic software
to be integrated into the device, which necessitates careful planning. They must take into
account the devices’ limited resources and constraints on economic viability when making
such enhancements. Because of the restrictions of IoT devices, many security protocols
must typically be implemented using less limited devices, such as gateways. In order to
accelerate cryptographic operations and reduce the burden on complicated secure software
protocols, hardware cryptographic components could be used.
There is a corresponding increase in the size of the blockchain as there are more and
more linked IoT devices that can generate massive amounts of real-time data, which results
in a higher number of transactions and processes required to validate them. This issue
might raise concerns in blockchain, since certain blockchain implementations can handle
limited transactions per second. According to academics, deleting outdated transaction
records from the blockchain’s storage can help solve the problem of scalability. In addition,
researchers are attempting to redesign blockchain approaches in accordance with IoT
constraints; for example, creating micro-blocks to store transactions and key blocks for
Sensors 2023,23, 788 8 of 43
leader election instead of the common block results in fierce competition among miners to
control the micro-block generation process.
Although smart contracts are considered the next big thing in blockchain technology,
several issues still need to be addressed. Although smart contracts may be useful in the
IoT, their implementation in IoT applications varies widely because they are stored in a
particular blockchain address as data and code. A transaction broadcasted in the network
is required to alter the contract’s current state and hence the blockchain. Transactions
must be signed by the sender and approved by the network before they can be added to
the chain. The IoT could benefit from a secure and reliable processing engine provided
by smart contracts. Using smart contracts results in secure and reliable processing. The
logic of IoT applications may be securely modeled using smart contracts, but still, a few
concerns must be addressed in the integration process. IoT’s constraints and heterogeneity
must also be considered when implementing smart contracts. Furthermore, working
with smart contracts necessitates relying on the oracles that offer real-world data in a
trustworthy manner. IoT is unstable, making it difficult to validate these smart contracts.
The use of many data sources may cause these agreements to become overburdened.
Smart contracts do not share resources to deal with massive amounts of computing and
distribution tasks, even when they are now characterized as decentralized and distributed.
Smart contracts are executed on a single node, while code execution is performed on
several nodes simultaneously. Instead of distributing tasks, this distribution is just used
for validation.
In centralized designs, the consensus is guaranteed by a trusted authority, while in
decentralized systems, a consensus is reached through voting and thus requires a lot of
resources. The properties of IoT devices include low-bandwidth wireless connectivity,
low power consumption, and low computing capabilities. Restricted resources should
be allocated to establish an agreement in IoT instances where computationally intensive
consensus procedures are unsuitable. A decentralized architecture can lower the total cost
of the IoT system as opposed to centralized systems. There is a new resource wastage
problem with blockchain, making integrating with IoT difficult. Consensus protocols in
blockchain affect the number of resources needed. In most cases, these responsibilities are
delegated to unconstrained devices that can deliver such capabilities, while other solutions
assign such responsibilities to gateways. Alternatively, off-chain technologies could provide
the functionality of transporting data outside the blockchain to alleviate the high latency.
Finally, it is crucial to mention blockchain trilemma (also called scalability trilemma),
which indicates that scalability, security, and decentralization cannot be achieved con-
currently in a public blockchain [
88
]. This issue is recognized since decentralization and
scalability are inversely proportional in a blockchain with enormous numbers of partici-
pants. However, security and scalability are proportional when decentralization is fixed.
Hence, trade-offs must be stated, since it is impossible to develop a blockchain with all
features simultaneously. For example, Bitcoin currently can only process seven transactions
per second while being secure and decentralized. Furthermore, although Hyperledger Fab-
ric blockchain offers high transnational throughput and security, it is centralized. Fast and
decentralized blockchains suffer from vulnerability to attacks. Current research efforts aim
to explore improving blockchain scalability in layer one by improving the consensus algo-
rithms (e.g., Ethereum 2.0) and using a concept called sharding. In layer 2, researchers seek
to use nested blockchains and state channels to address this issue. Although blockchain
trilemma introduces serious challenges, it is still a dominant technology due to its ability to
support the required features to design an efficient and effective IoT scheme.
3. Review Methodology
This systematic literature review follows the principles suggested by Kitchenham and
Charters [
89
] to perform the SLR to address the targeted research issues and to assure the
transparency and reliability of this study. Because of the wide variety of blockchain appli-
cations, compiling literature to obtain a comprehensive picture of its various characteristics
Sensors 2023,23, 788 9 of 43
that make it offer to protect the Internet of Things is difficult. Thus, we focused on specific
databases because exploring these large databases is partly facilitated by the number of
articles and conference proceedings that can be accessed within them. Our review focused
on the following databases:
• IEEE Explore Digital Library;
• ScienceDirect;
• SpringerLink;
• ACM Digital Library;
• MDPI;
• Wiley/Hindawi.
3.1. Search Strategy
The primary studies were gathered by using keywords to search the databases. We
obtained a wide range of results since we used generic search phrases. Between the AND
and OR operators, the principal search word is inserted. We considered the following search
terms based on population and intervention: ((“Blockchain” OR “Blockchain Technology”
OR “BC”) AND (“ Internet of Things “ OR “ IoT”) AND (“Privacy” OR “Security” OR
“Confidentiality” OR “Integrity” OR “Availability” OR “Scalability” OR “Authentication &
Data Protection” OR “Authorization” OR “Access Control” OR “Identity Verification”)).
Between 6 and 15 October 2021, we ran a search that included publications published from
2018 onwards. Filtering reduces the number of relevant results returned by running a
search query over multiple databases. Inclusion and exclusion criteria, outlined in the next
section, have been applied.
3.2. Study Eligibility Criteria
Blockchain technology is being applied to the Internet of Things to improve privacy
and security, and this study aims to summarize and assess those applications and uses. As a
result, only the following studies were eligible to satisfy the selection criteria: a blockchain-
based approach or technique that primarily aims to improve the security and privacy of
the Internet of Things. Aside from that, other restrictions were put in place regarding
publishing formats and languages used in the studies. Only peer-reviewed publications,
conference proceedings, reports, theses, and dissertations published in English between
2018 and 2022 were included. Reviews, conference abstracts, commentaries, archived
proposals, and editorials were all excluded. Finally, to provide a proper review, any article
providing security or privacy for IoT using other approaches combined with blockchain
technology was also excluded, as this study aims to leverage the benefits of blockchain
technology only.
3.3. Selection Results
There were two stages to the study selection procedure (screening title and abstracts
of retrieved studies and screening full text of the studies selected in the first stage), as
shown in Figure 3. We proceeded by screening the titles and abstracts of all the studies
that had been obtained. After that, we read the entire collection of articles. First, we fully
searched all studies that had been discovered in stage one. Consensus and discussion
were used to address any disagreements among the reviewers. A total of 139 studies were
found using our search keywords. Two publications with multiple versions were found,
reducing the total number of articles to 137. The research pool is reduced from 137 to
78 articles when inclusion and exclusion criteria are applied to the title and abstract of each
paper; thus, 59 articles were excluded. Finally, after scanning and reading full texts with
inclusion/exclusion criteria for the remaining 78 publications, 35 publications were also
excluded, bringing the total number of primary studies included in our SLR to 43 papers.
Sensors 2023,23, 788 10 of 43
Figure 3. Study selection criteria.
4. Findings
This section illustrates different works that addressed integrating blockchain tech-
nology and IoT to tackle various privacy and security aspects. We discuss the different
characteristics of all included papers, such as their primary information, objective, devel-
opment level, target application, type of blockchain and platform, consensus algorithm,
evaluation environment and metrics, future works or open issues (if any), and finally
further notes for consideration. We note that the calculations in our discussion depend only
on our findings of the included papers.
The primary information of the included papers [
90
–
132
] is shown in Table 1. Based on
the data shown in Figure 4, we can observe that 13.95% of the total papers were published
in 2018 [
93
,
103
,
111
,
116
,
120
,
124
], while 30.23% were published in 2019 [
90
,
92
,
94
,
95
,
99
,
101
,
102
,
104
,
114
,
117
,
119
,
122
,
127
], 11.64% were published in 2020 [
91
,
96
,
113
,
118
,
121
], 13.95%
were published in 2021 [
100
,
112
,
115
,
123
,
125
,
128
], and finally, 30.23% were published in
2022 [
97
,
98
,
105
–
110
,
126
,
129
–
132
]. We note that blockchain technology research is consid-
ered new as it will require further development and testing in a real-time environment.
In addition, the implementation of such systems is not easy. It requires more time than
traditional architectures, which explains why blockchain-based approaches are not widely
used in some research areas. However, many approaches that include blockchain tech-
nology in their designs are achieving promising results. Overall, 37.21% of the proposed
schemes originate from China [
92
,
94
–
97
,
102
,
104
,
105
,
113
,
114
,
119
,
124
,
125
,
128
,
129
,
132
], and
62.79% are distributed between 14 countries [
90
,
91
,
93
,
98
–
101
,
103
,
106
–
112
,
115
–
118
,
120
–
123
,
126
,
127
,
130
,
131
]. Most of the included papers are published in journals, which result in
79.07% [
90
,
91
,
94
,
96
–
100
,
102
,
104
–
115
,
118
,
119
,
121
–
123
,
125
–
132
], while 16.28% are published
in conferences [
92
,
93
,
95
,
101
,
103
,
116
,
120
], and 4.65% are published in book and sympo-
siums [
117
,
124
]. We included a detailed description of the publishers (name of journal
or conference) to address the common databases used. Overall, 51.16% are published in
the IEEE database [
91
,
92
,
96
,
97
,
100
–
102
,
104
–
108
,
110
,
111
,
116
,
119
,
120
,
123
,
124
,
127
,
128
,
132
],
20.93% are published in Elsevier [
94
,
103
,
112
–
114
,
117
,
118
,
121
,
122
], 13.95% are published in
Sensors 2023,23, 788 11 of 43
MDPI [
98
,
99
,
109
,
115
,
130
,
131
], 6.98% are published in Springer [
93
,
95
,
125
], and 6.98% are
published in Wiley/Hindawi [90,126,129].
Figure 4. Distribution of the number of published articles by year of publication.
Table 1. Primary Information of the Included Papers.
Ref # Year Country Publication Type Publisher
[90] 2019 UK Journal Wiley–Hindawi Wireless Communications and Mobile Computing
[91] 2020 India Journal IEEE Internet of Things
[92] 2019 China Conference IEEE Fourth International Conference on Data Science in Cyberspace
[93] 2018 USA Conference Springer International Conference on Computational Social Networks
[94] 2019 China Journal Elsevier Future Generation Computer Systems
[95] 2019 China Conference Springer International Conference on Smart Blockchain
[96] 2020 China Journal IEEE Wireless Communications
[97] 2022 China Journal IEEE Internet of Things
[98] 2022 Saudi Arabia Journal MDPI Electronics
[99] 2019 Poland Journal MDPI Sensors
[100] 2021 India Journal IEEE Internet of Things
[101] 2019 Iran Conference IEEE Canadian Conference of Electrical and Computer Engineering
[102] 2019 China Journal IEEE Internet of Things
[103] 2018 Egypt Conference Elsevier The 9th International Conference on Emerging Ubiquitous Systems and Pervasive
Networks (EUSPN)
[104] 2019 China Journal IEEE Network
[105] 2022 China Journal IEEE Internet of Things
[106] 2022 Morocco Journal IEEE Transactions on Computational Social Systems
[107] 2022 Nigeria Journal IEEE Systems Journal
[108] 2022 Pakistan Journal IEEE Access
[109] 2022 South Korea Journal MDPI Sensors
[110] 2022 Algeria Journal IEEE Access
[111] 2018 Taiwan Journal IEEE Access
[112] 2021 Serbia Journal Elsevier Energy Reports
[113] 2020 China Journal Elsevier Information Processing and Management
[114] 2019 China Journal Elsevier Future Generation Computer Systems
[115] 2021 Portugal Journal MDPI Sensors
[116] 2018 Mexico Conference IEEE International Smart Cities Conference (ISC2)
[117] 2019 Cyprus Book Elsevier Smart Cities Cybersecurity and Privacy
Sensors 2023,23, 788 12 of 43
Table 1. Cont.
Ref # Year Country Publication Type Publisher
[118] 2020 Australia Journal Elsevier Computers and Security
[119] 2019 China Journal IEEE Transactions on Industrial Informatics
[120] 2018 Vietnam Conference IEEE International Conference on Advanced Computing and Applications
[121] 2020 India Journal Elsevier Future Generation Computer Systems
[122] 2019 Australia Journal Elsevier Journal of Parallel and Distributed Computing
[123] 2021 Australia Journal IEEE Access
[124] 2018 China Symposium IEEE Symposium on Service-Oriented System Engineering (SOSE)
[125] 2021 China Journal Springer Cluster Computing
[126] 2022 Italy Journal Wiley Concurrency and Computation: Practice and Experience
[127] 2019 UAE Journal IEEE Access
[128] 2021 China Journal IEEE Systems Journal
[129] 2022 China Journal Wiley / Hindawi Wireless Communications and Mobile Computing
[130] 2022 USA Journal MDPI Future Internet
[131] 2022 Australia Journal MDPI Systems
[132] 2022 China Journal IEEE Internet of Things
The objective and development level are presented in Table 2. Based on the data shown
in Figure 5, our results show that 27.9% of the included studies focused on the healthcare
domain [
99
–
110
], while 18.6% focused on proposing generic approaches
[91–98]
. From the
total percentage of the included articles, 23.25% aimed to target smart environments appli-
cations divided into 9.3% for smart home applications [
120
–
123
], 6.98% presented systems
designed to target smart cities [
116
–
118
], and there were 2.32% of each of the following:
smart factory [
119
], smart traveling [
124
], and smart agriculture [
130
]. Furthermore, the
IoT device gateway [
111
,
112
], IoT information systems [
113
,
114
], management systems
[
131
,
132
], cloud environment [
128
,
129
], and fog computing [
125
,
126
] have carried out 4.65%
for each separate application. Finally, the rest (
≈
6.99%) aimed to address edge computing
[90], mobile IoT applications [115], and reputation systems [127].
Figure 5. Article distribution of the included studies by target application.
Regarding the development level, we used Technology Readiness Level (TRL) as the
base concept to describe the development level of the included articles. TRL includes nine
Sensors 2023,23, 788 13 of 43
values where each value represents the technical maturity of a technology; every three
values describes a particular research phase. TRL phases are defined as research, develop-
ment, and deployment. In order to reduce the complexity and initiate an understating of
measuring what level each system is developed, we determined an appropriate value for
each phase. For the research phase, 2 is selected, which indicates that the concept has been
formulated. For the development phase, 5 represents the validation process in a relevant
environment. Finally, for the deployment phase, 7 refers to the demonstration of a proto-
type in an operational environment. Overall, 79.09% proposed implemented design models
(evaluated using simulation tools)
[91,92,94–98,100,102–115,118,120–123,125–128,130–132]
,
18.6% proposed a concept formulation or use cases with the further intention to implement
and evaluate them [
90
,
93
,
99
,
101
,
116
,
117
,
124
,
129
], and only one approach was implemented
in a real-time environment [
119
]. The included approaches aimed to achieve various
goals to enhance certain aspects (security or privacy) of IoT. For example, ref. [
90
] investi-
gated the delay concerns, [
93
] discussed access control using a firmware update approach,
ref. [
94
] presented a Privacy-preserving Thin-client Authentication Scheme (PTAS) to ad-
dress privacy issues, heterogeneity and scalability issues are also discussed in [
96
], whereas
ref. [
99
] aimed to address the access control for EHRs, a multi-tier blockchain framework
for privacy-preserving EHRs is presented in [
103
], a data integrity check approach that
does not require trusted third parties is designed using blockchain, bilinear pairing, and
the Lifted EC-ElGamal cryptosystem in [
113
]. Some approaches focused on introducing
lightweight techniques such as [114,121,122].
Table 2. Objective and Technology Readiness Level.
Ref # Objective TRL
[90]TrustChain is an innovative privacy-protecting blockchain-based network to overcome the issues associated with existing IoT
networks and investigates how to eliminate privacy and delay concerns while preserving IoT network resources 2
[91]
A detailed analysis is provided, including enabling technology and IoT technology integration. In a smart IoT system, a case study
is implemented using an Ethereum-based blockchain technology 5
[92]In this study, the authors presented a blockchain-based decentralized IoT system. They developed various access strategies and
implement them using smart contracts 5
[93]
In order to ensure that users have full control over their data and can track how it is used by third-party services, a system model is
developed. They also proposed a blockchain-based firmware update approach that helps prevent IoT device tampering from
causing fraudulent data 2
[94]
Employing private information retrieval to present a Privacy-preserving Thin-client Authentication Scheme (PTAS) for IoT devices
5
[95]Propose an efficient and secure authentication private protection scheme using blockchain technology and the AES algorithm to
encrypt the original IoT information; this study provides an IoT information security protection strategy that may effectively
address IoT data storage issues 5
[96]Deal with the heterogeneity and scalability of IoT networks by proposing a three-dimensional architecture with unique data
structures. In addition, they propose the 3D-GHOST consensus mechanism for spacechain 5
[97]Propose a trust evaluation mechanism, PoT consensus algorithm, and privacy protection mechanism based on the commitment
scheme and rign signature 5
[98]This paper addresses the scalability, privacy, and security issues by introducing a multi-layer-blockchain-based solution. It
eliminates the need of Trusting Third Parties (TTP) through multiple-chaincode-based access control 5
[99]Propose a novel patient-centered electronic medical record access control framework based on modified blockchain models that
take into account the IoT’s resource constraints 2
[100]Propose a dual-layer blockchain-IoT privacy-preserving approach based on swarm exchange techniques to support the seamless
and secure transmission of user data via secure swarm nodes of peer-to-peer communications 5
[101]Propose a blockchain-based architecture for e-health applications that provides an efficient privacy-preserving access control
mechanism 2
[102]
Healthchain is proposed which is a privacy-preserving scheme designed for the healthcare domain, this approach is large-scale and
is used to conduct fine-grained access control for health data. Furthermore, introduce a distributed file system called InterPlanetary
File System (IPFS) 5
[103]
Proposing a novel protocol named Pseudonym-Based Encryption with Different Authorities (PBE-DA), this paper aims to achieve
perfect privacy-preserving EHRs in a multi-tier blockchain framework 5
Sensors 2023,23, 788 14 of 43
Table 2. Cont.
Ref # Objective TRL
[104]This paper presents a privacy-protected blockchain-based system for medical image retrieval using layered architecture, a threat
model, and a customized transaction structure 5
[105]This work offers an architecture designed for IoMT and other possible applications. In addition, authentication schemes are
introduced based on ECC and PUF to ensure the system’s privacy 5
[106]
A system that secures IoT devices in the healthcare domain using IPFS and blockchain technology. This system is designed to allow
continuous monitoring for patients with chronic diseases 5
[107]GaliMediChain is a healthcare system based on blockchain technology and garlic routing to enable secure sharing of health data
(COVID-19). Furthermore, a new consensus mechanism (PoEoI) is proposed for blocks generation and miner selection processes 5
[108]A medical Hyperledger Fabric-enabled blockchain-based architecture called BIoMT is proposed to increase the medical
environemnt’s reliability while reducing the consumption of networking resources. Moreover, a customized consensus algorithm is
designed to increase security and privacy 5
[109]
An implementation for critical systems to enhance privacy and security through combining Information Hiding Techniques (IHT),
IoT, and blockchain technology 5
[110]DSMAC is a decentralized system proposed to preserve the security and privacy of sharing medical records using Verifiable
Credentials (VC), Decentralized Identifiers (DIDs), Attribute-Based Access Control (ABAC), Role-Based Access Control (RBAC),
Self-Sovereign Identity (SSI), and blockchain 5
[111]This approach aims to develop a blockchain-connected gateway for IoT devices that can adapt and securely retain the privacy
preferences of users; security and privacy preferences can both be ensured by the proposed digital signature mechanism (PDSS) 5
[112]This article discusses how the security gateway architecture of an IoT device can be provided with a basic interface using
blockchain to allow decentralization and authentication. This offers IoT infrastructure with the required anonymity and versatility
5
[113]By leveraging blockchain, bilinear pairing, and a lifted EC-ElGamal cryptosystem, this paper proposes a novel remote data
integrity check approach for IoT information management systems that preserves privacy without requiring trusted third parties 5
[114]
This study presents a lightweight IoT data-sharing security framework based on a double-chain paradigm that combines data and
transaction blockchains. To prevent malicious local dominance behavior, a dynamic game method of node cooperation is presented.
This approach aims to improve privacy protection, data registration efficiency, and the PBFT consensus algorithm 5
[115]The primary goal is to improve the privacy of users and their data by implementing user-controlled privacy using the
anonymization characteristics of blockchain. This paper proposed an architecture to address privacy and security issues of IoT
applications 5
[116] A blockchain-based control access system integrated with IoT to improve smart cities services and performance 2
[117]This research offered a smart city hybrid model that included five core features that the authors consider necessary to provide
security and privacy 2
[118]PrivySharing: a framework for secure, private IoT data sharing in smart cities via blockchain technology. By separating the
blockchain network over multiple channels, data privacy is ensured 5
[119]Transform the standard IoT architecture by introducing multi-center security and privacy blockchain architecture. Design the
architecture’s data interaction and algorithmic processes. The specific solution is discussed using an automated manufacturing
platform 7
[120] A smart home-based IoT-Blockchain (SHIB) approach is proposed to address privacy, security, and authentication challenges 5
[121]ELIB is a smart home model that is based on an overlay network that validates dedicated security and privacy by merging
resources with high levels of capability into a public BC. This approach consists of three levels: DTM scheme, CC model, and
consensus algorithm 5
[122]This paper proposes a lightweight scalable blockchain (LSB) approach that focuses on achieving decentralization and optimizing
the performance for IoT requirements through overlay networks. A time-based consensus algorithm is also proposed to reduce the
delay and mining processing overhead 5
[123]In order to develop a robust framework for smart home systems, they present an authentication model that integrates
attribute-based access control with smart contracts and edge computing. In addition, they designed a Stochastic Gradient Descent
(SGD) algorithm 5
[124]In response to the need for data mining and analytic activities in IoT, a privacy-protected and inter-cloud data fusion platform
based on JointCloud blockchain is proposed in this paper 2
[125]
Blockchain, fog computing, and the alliance chain concept are used in this research to provide a distributed access control solution
for IoT data security that relies on LSB (Least Significant Bit) and MLNCML (mixed linear and nonlinear spatiotemporal chaotic
systems) techniques 5
[126]This works presents a cross-domain access control scheme that provides reliability and security. The distributed access control is
achieved using blockchain and NetwOrked Smart object (NOS) middleware 5
[127]A decentralized trust model is proposed in this research in order to maintain the reputation of publicly available fog nodes. The
public fog nodes’ reputation is preserved by the opinions of previous users and their interactions with them 5
Sensors 2023,23, 788 15 of 43
Table 2. Cont.
Ref # Objective TRL
[128]Secure the data sharing in cloud environments using a proxy re-encryption approach by combining PRE with blockchain
technology, information-centric networking, and identity-based encryption 5
[129]
This article proposed a privacy protection scheme based on a combination of smart contracts and zero-knowledge proof in order to
provide an effective use of data while maintaining data privacy and validity. Furthermore, in order to share the data safely and
offer consistency between owners and cloud service providers, a proxy re-encryption technology is enclosed 2
[130]A smart agriculture prototype is designed using cloud and blockchain to provide remote monitoring and alert mechanisms to
farmers in real time 5
[131]In order to overcome certain security and privacy issues in IoT ID management systems, this paper proposes a proof-of-concept
blockchain-based modeling prototype 5
[132]A distributed identity management system called SmartDID is proposed to address the lack of a systematic proof system and
resource limitations for IoT devices 5
The used blockchain platform, blockchain type, consensus algorithm/protocol, evalu-
ation environment, and metrics are presented in Tables 3–7. Different platforms are used
for each application: 37.21% of the included studies focused on using a generic platform or
Ethereum [
91
,
93
,
95
,
97
,
99
,
104
,
106
,
111
,
113
,
115
–
117
,
120
,
123
,
127
,
130
], 20.93% were implemented
using Hyperledger Fabric [
93
,
98
,
105
,
108
–
110
,
118
,
125
,
128
], while 23.25% focused on special
or other types of blockchains
[90,92,94,96,100,102,121,122,124,132]
. Note that some studies
allow or use more than one platform [
93
,
102
,
110
]. It indicates that generic platforms do not
always fit the requirements of specific approaches; thus, other platforms are required to
ensure that the proposed architecture achieves its optimal performance. Blockchain type is
also based on the domain requirements and the design itself. Overall, 41.86% of the stud-
ies are using private blockchain [
90
–
93
,
96
,
100
,
101
,
106
,
109
,
111
,
115
,
118
–
120
,
123
–
126
], which
indicates that it is more suitable for the desired applications since it offers more control
and security for the network, while 25.58% are using a public blockchain [
91
,
94
,
97
,
102
,
115
–
117
,
121
,
122
,
127
,
130
]. In addition, 37.21% of the approaches allow the implementation
using both types or a combination of both (consortium blockchain) [
91
,
95
,
98
,
102
,
103
,
105
–
108
,
110
,
114
,
115
,
126
,
128
,
131
,
132
]. From our perspective, private blockchain offers the most
needed features that are required to reach a high level of security and privacy.
Although PoW is the most common consensus algorithm, we can observe that only
25.58% of the total approaches used it [
94
,
96
,
102
,
108
,
115
,
117
,
119
,
120
,
123
,
127
,
130
] since it
has many disadvantages. We note that some studies used a modified version to offer a bet-
ter solution, such as [
96
], or added another algorithm to support it, such as
[96,102,108,115]
.
Note that modifying the common algorithms such as PBFT can result in an efficient
model [
114
]. Some approaches used a time-based consensus algorithm to achieve higher
security and privacy [
121
,
122
]. It can be observed that there is still a need to propose
new consensus algorithms to address the drawbacks of IoT because the requirements for
this technology are not similar compared to other technologies. Thus, it is vital to tackle
this issue; for example, one of the included studies designed their consensus algorithm
named “Three-Dimensional Greedy Heaviest-Observed Sub-Tree (3D-GHOST)” and im-
plemented it with a modified PoW [
96
], Proof-of-Epidemiology-of-Interest (PoEoI) was
used in [
107
], and other studies used Zero-Knowledge Proof (ZKP) [
110
], time-dependent
consensus algorithm [
121
], Distributed Time-based Consensus algorithm (DTC) [
122
], and
combined RBFT and Raft [
129
]. Furthermore, BFT, PBFT, and its modified versions are
commonly used nowadays [
90
,
98
,
101
,
102
,
109
,
114
,
126
,
128
,
132
] due to the advantages of-
fered; for example, PBFT reduces energy usage and eliminates the need for confirmation to
perform transactions.
Sensors 2023,23, 788 16 of 43
Table 3. Generic approaches articles: blockchain characteristics, evaluation environment, and metrics.
Ref # Blockchain Platform Type of Blockchain Consensus
Algorithm /Protocol Evaluation Environment Performance Evaluation Metrics
[91]Generic Ethereum
(Extension) Public or Private PoBT Solidity platform, Ethereum’ web3.js, different types of
sensors connected to Raspberry Pi N/A
[92] EOS Private DPoS
EOS system, desktop computers as gateways, gateways com-
plied contract with C++ language Max, min, and average time for contract deploy and execution,
transaction package and validation
[93]Ethereum or
Hyperledger Private
(Hyperledger) 6N/A N/A
[94] Certcoin Public PoW
On a mobile phone with specific hardware parameters, these
operations are tested and programmed using JAVA
Functionality, Computational overhead of thin-client and full
node users, Communication overhead
[95] Ethereum Consortium IPFS used in data
storage module
Performance test on AES with a specific hardware
specifications desktop, private IPFS network for information
storage, Lena image, smart contracts are deployed on
Ropsten Testnet test network
AES encryption and decryption rate, Delay of node joining the
network
[96] Spacechain Private 3D-GHOST
Modified PoW
Python 3, simulator: Compiled automatic transaction
generator, testbed: Multi-miner P2P network test consist of
50 cloud virtual machines
Defense effect of selfish mining and DDoS attack, Network perfor-
mance, Network throughput (block creation rate and block size
limitation), Scalability
[97] Ethereum Public Proof of Trust (PoT) Solidity platform, Ethereum virtual machine (EVM), Remix
IDE, network with 20 nodes (four types of nodes)
Trust evaluation mechanism, Running time, Expected mining cost
[98] Hyperledger Fabric Permissioned
(Consortium) PBFT
Hyperledger Fabric (v1.4.4) as a blockchain platform,
Docker engine (v19.03.8, build afacb8b7f0) for runtime,
Docker-compose (1.25.0) for image configuration, Node
(v10.24.0) to create clients, Golang language (go1.16.2) for
smart contracts creation, Hyperledger Caliper [133]
Throughput, transaction latency
Table 4. Healthcare Articles: Blockchain Characteristics, Evaluation Environment, and Metrics.
Ref # Blockchain Platform Type of Blockchain Consensus
Algorithm /Protocol Evaluation Environment Performance Evaluation Metrics
[99] Generic 6 6 N/A N/A
[100] Swarm Private IPFS GnuPG, IPFS, Golang, Five types of IoT-based health
sensor nodes Time of loading, exchange, listening, announcement, and
availability, IoT elements activity
[101]6Private PBFT N/A N/A
Sensors 2023,23, 788 17 of 43
Table 4. Cont.
Ref # Blockchain Platform Type of Blockchain Consensus
Algorithm /Protocol Evaluation Environment Performance Evaluation Metrics
[102]Userchain
Docchain Public (Userchain)
Consortium (Docchain) PoW (Userchain)
PBFT (Docchain)
Simulate the user node with a smart phone, The experiment is
built on the platform Android 7.1.1, Java is used for IoT
transaction and key transaction, OS Windows 7 is used to
measure doctor nodes and mining nodes
Python is used for programming Docchain and Userchain
Effectiveness and feasibility (computation and
communication costs for user transactions generation
[103] Multi-tier platform
Public (it can be also
considered a consortium,
since it contains
constrained and
unconstrained nodes)
PBE-DA MIRACL Library (security tools )
Linux Ubuntu 12.10 on a computer machine Processing time from different sources and destinations
[104] Ethereum 6 6 Geth is used as the Ethereum client Transaction generation time with varying transaction
capacities and image retrieval time
[105] Hyperledger Fabric Consortium 6
Edge server (Ali cloud platform), SD or EU simulators
(smartphone), Fabric platform with multiple nodes is used to
evaluate the performance of a smart contract
Computation cost, Communication cost, Time cost of
smart contract
[106] Ethereum Private (in experiments)
Consortium (in problem
formulation)
Proof of Authority
(PoA)
Private Ethereum Clique Blockchain (PC) Private IPFS
network (PC), Raspberry Pi 3 Model B, Smartphone (DApp
interfaces), JSON-RPC protocol Processing time for different operations
[107]6Consortium
Proof-of-
Epidemiology-of-
Interest
(PoEoI)
The implementation environment is available online
Total cost (time) to evaluate the system’s efficiency,
adaptability, and robustness. Elapsed time to request or
respond, total utility, probability (availability attack),
consensus protocol evaluation: computation cost and
number of nonces.
[108] Hyperledger Fabric Consortium Customized
lightweight PoW
Docker (Hyperledger Fabric), Applying a resource limit
mechanism on network nodes, Hybrid network topology,
Experiment over P2P network
Frequency evaluation of CPU usage, Computational
fluctuations (cost), Ratio between number of medical
transactions and the total number of connected devices,
Rate of throughput, duty cycle, delay, and response
[109] Hyperledger Fabric Private Proposed PBFT Smart city network models using network simulator-3 (ns-3),
Network topology using Python, GO-Ethereum
Network throughput using PBFT compared with the
classical algorithm, Latency of fault peers, General latency
of execution over the network
[110]Hyperledger Indy [134]
Hyperledger Aries [
135
]
Permissioned
(Consortium)
Zero-Knowledge Proof
(ZKP) [38]
Hyperledger Indy for identity management, Hyperledger
Aries for digital credentials, Solidity and Hyperledger
Ethereum to run smart contracts, ACA-Py as cloud agent,
VON-network as a ledger browser, Docker community edition
Transaction time (DSMAC evaluation), Transaction
throughput, Transaction latency, Cryptographic
computations, Scalability, Sustainability
Sensors 2023,23, 788 18 of 43
Table 5. Smart Environments Articles: Blockchain Characteristics, Evaluation Environment, and Metrics.
Ref # Blockchain Platform Type of Blockchain Consensus
Algorithm /Protocol Evaluation Environment Performance Evaluation Metrics
[120] Ethereum Private PoW Ganache, Remix, web3.js
Smart contract, Data privacy, Usage of tokens, Updating the
policies, Misbehavior Judging
[121]Simulated platform on
NS3 Public Time-dependent
consensus algorithm Cooja, Network Simulator 3 (NS3), C++ programming
language POW processing time, Time overhead, Energy
consumption, Packet overhead
[122]Simulated platform on
NS3 Public DTC NS3, MinerGate
POW processing time, Request/response delay, Impact of
the number of OBMs on security and packet overhead,
Impact of PTV on the ability to detect appending attacks,
DTM in the overlay
[123] Ethereum Private PoW Two sensors (temperature and LED), Python in google colab
environment Resource usage for single transaction, K-fold
cross-validation, Accuracy
[116] Generic Public 6N/A N/A
[117] Ethereum Public PoW, PoS, PoA, and
Proof of Vote (PoV)
were investigated N/A N/A
[118] Hyperledger Fabric Private SOLO and Kafka Oauth 2.0, ClientApp, REST API, Hyperledger
Composer-Playground, Hyperledger Caliper Validation of ACL rules, Performance efficiency, Average
commit time, Average throughput, Average latency
[119]6Private PoW Four industrial robots, 3B Raspberry Pis (two sensing layers),
Intel I5 platform (management hubs) Real-time performance testing
[124] JointCloud Private 6N/A N/A
[130] Ethereum Public (on Rinkeby
Etherscan) PoW Arduino Sensor Kit, ESP32, AWS cloud, Ethereum Rinkeby
Test Network Latency of: Device-to-Cloud, Cloud-to-Blockchain,
Blockchain-to-Client-Console, Alert Total
Sensors 2023,23, 788 19 of 43
Table 6. IoT Device Gateway, IoT Information Systems, and Management Systems Articles: Blockchain Characteristics, Evaluation Environment, and Metrics.
Ref # Blockchain Platform Type of Blockchain Consensus
Algorithm/Protocol Evaluation Environment Performance Evaluation Metrics
IoT Device Gateway
[111] Ethereum Private Ethereum-like
PDSS: Raspberry PI III (Debian 8), Java 8 for ARM, Eclipse 3.8,
BC gateway: Desktop (Ethereum network), NVIDIA Shield
TV as gateway, LG Nexus 5X as client application, Smart
contract management: Asus ZenBook, JDK 8u151, Java EE 7
Computation cost for PDSS, Practical potential for BC
gateway, Average time for smart contract management
[112]6 6 6 Node.js
AES, DES, and Triple DES are used to evaluate memory usage
IoT Information Systems
[113] Generic 6 6 Python 3.7.1, Key size = 32 bits, ECC encryption using
ElGamal algorithm
Probability of the illegality behavior detection, Average time
of key generation compared to the size of key, Average time of
six other elements
[114]6Combination of public,
alliance, and private chains Improved PBFT CentOS 7, JDK version is 1.80, Threshold signature (THS) Throughput, Latency, Determination time, Transactions per
second (TPS), Node density, Routing protocol performance in
blockchain IoT low-speed environment
Management Systems
[131]6Federated (Consortium) 6Solidity, Kaleido platform Transaction monitoring, CPU time and utilization
[132] FISCO BCOS Consortium PBFT Java 1.8, Fisco Bcos platform, 28.5 Mbps /11.21 Mbps
bandwidth, 10 ms average communication delay
Credentials generation, Proof generation, Proof time, Time per
type of credential, Time for range credential, Block generation
time, Credentials size, Average verification time, Time for
credentials verification
Sensors 2023,23, 788 20 of 43
Table 7. Other Articles: Blockchain Characteristics, Evaluation Environment, and Metrics.
Ref # Blockchain Platform Type of Blockchain Consensus
Algorithm/Protocol Evaluation Environment Performance Evaluation Metrics
Cloud Environments
[128] Hyperledger Fabric Consortium PBFT jPBC library [136] for pairing, A super-singular curve, elliptic
curve cryptography to implement group-based schemes, NIST
P-256, web3.js to generate transactions
Computation cost, Encrypted data confidentiality, Data
encryption computation time, Transaction latency
[129]6 6 Combined RBFT
and Raft N/A N/A
Fog Computing
[125] Hyperledger Fabric Private 6Go language, JetBrains developer tools, MATLAB,
Lena picture
Bit error rate with chaos coding parameter, Bit error rate with
encrypted image pixels
[126]6Permissioned (private or
federated/consortium) BFT replication
NOS architecture, 14 data sources, Data rate between 10 and
20 packets per second, Rate change policy, Block dimension
and generation time, Raspberry Pi platforms, MQTT broker,
Smart home testbed
Storage overhead, Computing effort (CPU load), Latency
Edge Computing
[90] TrustChain Private Proof of Trust (PoT),
Trust+BFT N/A Object trust model, Data trust model, Privacy trust model,
REK: Reputation, Experience, and Knowledge
Reputation Systems
[127] Ethereum Public PoW MythX, Sercurify analyzer, SmartCheck, Oyente, Remix IDE
using solidity Cost (of transaction), Performance analysis, Security analysis
Mobile IoT Applications
[115] Ethereum Private and Public Private blockchain:
PoA, Public
blockchain: PoW
Python’s time library, NetHogs (version 0.8.6), psutil (version
5.8.0) python library Time overhead, Bandwidth consumption, CPU and
memory usage
Sensors 2023,23, 788 21 of 43
In the evaluation environment, we aimed to cover software tools and certain impor-
tant hardware devices used in the implementation and experimentation phases. Different
tools are used such as Solidity [
91
,
97
,
110
,
127
,
131
], Web3.js [
91
,
120
,
128
], various types of
sensors
[91,100,123,130]
, gateways [
92
,
111
], smart devices [
94
,
102
,
115
,
126
], Node.js [
112
],
and NS3 [
121
,
122
]. C++ is used as a programming language in [
92
,
121
], Go and MAT-
LAB are used in [
98
,
100
,
125
], Python is used in [
96
,
102
,
109
,
113
,
115
,
123
], and Java is used
in [
94
,
102
,
111
,
132
]. For detailed information, refer to Tables 3–7and the references listed for
each environment. Regarding the metrics used to evaluate each study, time was used as
one of the parameters in
[92,97,100,103–107,110,111,113–115,118,121,122,128,131,132]
, while
throughput is used in [
96
,
98
,
108
–
110
,
114
,
118
]. Many approaches evaluated their ap-
proach by testing it with more specific parameters such as AES encryption and decryption
rate [
95
,
112
], validation of ACL rules [
118
], effectiveness and feasibility [
102
], computation
cost for PDSS [
111
], functionality [
94
], probability of the illegality [
113
],
latency [
114
,
118
], bandwidth consumption [
115
], IoT elements activity [
100
], bit error
rate [
125
], cost [
97
,
102
,
105
,
107
,
108
,
127
,
128
], K-fold cross-validation [
123
], DTM in the over-
lay [
122
], sustainability [
110
], different latency metrics [
130
], and calculations based on
credentials [132].
As mentioned earlier, we focused on the target application when implementing the
taxonomy of this study. The included studies proposed different solutions to address
IoT applications’ privacy or security aspects. In addition to the detailed information in
the above tables and figures, in the following subsections, we will discuss each study
from an architectural and operational perspective to ensure that this review provides a
comprehensive overview.
4.1. Generic Approaches
Multiple studies focused on proposing generic solutions to address certain features
within IoT. An Ethereum-based approach was proposed to address layer-wise security
issues and device authentication in IoT applications [
91
]. This work focused on specific
aspects, including eliminating the idea of localizing authorization and authentication inside
the IoT network and eliminating latency issues on the IoT network. Blockchain operation
is only required when adding a new user or device is needed. Blockchain is the only entity
that allows creating permissions regarding the scaling of IoT networks.
In [
92
], the authors create a safe, fine-grained access control strategy for users, devices,
and data. Then utilize smart contracts to implement the strategy. System design includes
an access control strategy, user registration and authorization, device’s safe insert, database,
smart contract design, and transaction design. The access control strategy consists of three
tables defining the access rights: the user access table, device resource table, and role table.
The device’s safe insert ensures that the device’s hardware includes embedded identity
information. The database scheme stores data collected by devices and data related to the
behavior of the user, device, and gateway. Three types of smart contracts are designed in
this scheme: user access, device insert, and log. The design of transactions describes the
details that must be encapsulated in the message to be sent.
Another approach focused on proposing a mechanism to address the access control
management of how users deal with their data [
93
]. This model consists of the main
blockchain network, off-chain storage, aggregators (publish), subscribers (subscribe), and
vendors. Aggregators publish data and define how third-party can access the data. Third-
party, known as subscribers, can access that data through transactions. Off-chain storage
stores the data published by aggregators through a scheme called content-based address-
ing. Manufacturers of IoT devices are known as vendors, and they are responsible for
distributing official firmware. In order to manage access permissions and update new
firmware, the blockchain network is equipped with two smart contracts: FirmwareUpdate
and AccessControl.
The PTAS scheme [
94
] is proposed to ensure privacy using private information re-
trieval and security using (m-1)-private PTAS to protect against a collision of network nodes.
Sensors 2023,23, 788 22 of 43
This scheme allows thin clients to function normally as full-node users by hiding user iden-
tity in k indistinguishable identities. Security and functional comparisons are conducted to
highlight this scheme’s high level of security and comprehensive functionality compared
to other schemes. However, PTAS improves safety while sacrificing little efficiency.
IoTChain [
95
] is a scheme proposed to protect the security of IoT information based on
blockchain technology characteristics and the AES encryption algorithm. The large-scale
secure storage of IoT information data can be provided by IoTChain, which can authenticate
and grant access to authorized users. As a result, the researchers in this study proposed
efficient and secure authentication, privacy protection, and multi-signature conditional
traceability solutions based on blockchain technology.
Spacechain [
96
] is a blockchain architecture with a three-dimensional ledger that deals
with the scalability and heterogeneity of IoT networks. They also proposed a consensus
algorithm called 3D-GHOST to improve network performance and security. Macro-blocks
are used to create Directed Acyclic Graph (DAG) to provide the system with the third
dimension aspect. DAG consists of a vertex, edge, ack-edge, and ref-edge, which illustrates
the operation of this foundation. In the data structure design, the validation process occurs
in three steps: consensus algorithm validation and verification using PoW, header_hash
validation, and timestamp validation. For the consensus algorithm, the blockchain is
divided into the main-chain and side-chain to ensure better performance. A novel DWD
mechanism is used for dynamic weight distribution with many metrics, such as Cardinal
Value (CV), Data Validity (DV), and Contact Degree (CD). This architecture is implemented
and evaluated; it results in a better performance than the NKC scheme.
A study proposed a blockchain-based privacy-preserving and trust-centric approach
and Proof-of-Trust (PoT) consensus algorithm to tackle the challenges related to trustwor-
thiness and create an affordable and lightweight consensus mechanism [
97
]. This study
included a trust evaluation mechanism, PoT consensus algorithm, and privacy protection
mechanism. The commitment scheme and ring signature combine to create a robust privacy
protection mechanism. On the other hand, PoT is designed by connecting the trust value of
network miners with mining difficulty. The design of the proposed DSA system included
four phases: individual sensing, sensing fusion, spectrum allocation, and spectrum access.
This system offers decentralization, transparency, automation, and flexibility. The proposed
consensus algorithm increased the scalability and reduced computation cost.
With blockchain technology, a lightweight multi-chaincode model is proposed to
address central authority management issues that lead to a lack of privacy, low scalability,
and single point of failure [
98
]. The proposed system includes various layers, such as Con-
sortium Blockchain Manager (CCBCM) for access control, an Aggregated Edge Blockchain
Manager (AEBCM) layer for communication purposes, and Edge Blockchain Managers
(EBCMs) that contain network devices. To achieve the required scalability, low latency, and
high throughput, a hierarchical permissioned blockchain is used. EBCM is used within the
cluster to manage the data securely. This model eliminates Trusting Third Parties (TTP)
by incorporating self-executed smart contracts. The authors provided a security analysis
discussion on how the proposed model offers availability, integrity, and confidentiality.
4.2. Healthcare
Dealing with big data in the healthcare domain can raise security and privacy issues,
endangering the patient’s life. A novel privacy-preserving framework to secure the analysis
and management of healthcare data is proposed [
99
]. This study addresses the IoT devices’
constraints and how to resolve the issues requiring extra computational power, high
bandwidth, and computation cost. The proposed framework consists of healthcare wearable
IoT devices, smart contracts, healthcare providers, cloud storage, and an overlay network.
Asymmetric and ARX symmetric encryption schemes are both used. Signature correctness
and signers’ anonymity are achieved using lightweight ring signature technology. Further
work can be completed to implement this framework in a testable environment and provide
more security guarantees.
Sensors 2023,23, 788 23 of 43
BIoTHR [
100
] is an EHR management system based on private blockchain to ensure
the timely monitoring of reliable and secure data transmission. This scheme supports full
EHR utilization, a swarm exchange network for IoT-based implementation, UML activ-
ity modeling, and trusted parties. Five sensor nodes are employed to aggregate patient
information to provide the network with heterogeneous features. The authors included a de-
tailed discussion of a novel swarm exchange paradigm to create a tamper-proof and robust
system. Different algorithms were used to create the private blockchain, swarm listening,
address announcement of swarm local listening, address announcement of swarm interface
listening, swarm connection opening, and connection closing. This study provides protec-
tion of data privacy, protection against fraud, security and transparency, interoperability,
access control, pseudonymity, full decentralization, high availability, design simplification,
and reduced cost. This design can be improved to function in a large-scale network, API is
required, and a proper manner of mining is needed.
Another study that addresses access control is proposed using blockchain architecture
for e-health applications [
101
]. The general blockchain structure was modified to make
this approach fit the healthcare domain, reduce data redundancy by clustering network
miners, and reduce transaction size to reduce network overhead; a pseudonym is assigned
for each patient, and the data are stored in the nearest location to address security and
privacy challenges. This model consists of sensors, Personal Digital Assistance (PDA) or a
smartphone, the IoT Health Manager (IHM), a central server responsible for managing the
data, healthcare institutions, a blockchain network, and miners. Further implementation
and experiments are required to evaluate this model.
To eliminate the issue of large-scale networks, Healthchain [
102
] is a privacy-preserving
scheme proposed for large-scale health data to achieve fine-grained access control. This
scheme consists of IoT devices, user nodes, doctor nodes, accounting nodes, storage nodes,
Userchain, and Docchain. This design aims to provide high efficiency, privacy preserva-
tion, accountability, and on-demand revocation. The blockchain network (referred to as
Healthchain in this design) is divided into Userchain and Docchain (called subblockchains).
Userchain is implemented to prevent tampering with users’ transactions; it contains IoT
transactions and key transactions. Diagnostic transactions are the only ones supported by
Docchain and are secured by the diagnostic key assigned to each user. Keys were decoupled
from encrypted data to make key management more flexible. In order to protect users’ pri-
vacy, they can revoke doctors’ access to their records at any moment. This scheme provides
privacy preserving, accountability, and revocability. This study meets the standard security
criteria according to its security analysis. The performance evaluation results suggest that
Healthchain is a feasible and efficient solution.
Pseudonym-Based Encryption with Different Authorities (PBE-DA) [
103
] is a novel
protocol that allows patients to manage their EHR data securely and provide the perfect
privacy preserving. The proposed architecture consists of three tiers, namely network
nodes (constrained and unconstrained), number of authorities (medical institutions and
organizations), and EHRs cloud providers (servers). PBE-DA is designed in a multi-tier
blockchain framework that uses Elliptic Curve Cryptography (ECC). The fog or access layer
is the initial tier to connect devices and patients using a gateway. The ledger distribution
and communication of different EHRs are analyzed in the second tier. Finally, compliance
issues between EHR providers are examined. MIRACL security tools are used to evaluate
the framework for various security functions.
A study discussed how the volume of medical imaging is increasing, which might
affect the diagnosis and treatment because these images must be retrieved first [
104
]. This
study presents a threat and a layered architecture based on blockchain that selects feature
vectors to handle large-size images. To ensure the privacy of medical images and their
features, a customized transaction structure was designed in addition to the feature vector.
This study focused on three types of threats from a security perspective: data forgery, data
tampering, and privacy disclosure. The system design includes five entities: hospital, third
party, image retrieval service, regulatory authority, and miner. Transaction generation,
Sensors 2023,23, 788 24 of 43
image feature encryption, and image feature extraction are the main components of the
transaction layer. Based on the encrypted image characteristics, the service layer provides
crucial functionality for similarity measurement and image retrieval.
Introducing the medical field to IoT has led to reduced cost, increased accuracy, and
improved efficiency; security and privacy aspects are still essential concerns due to the
heterogeneous network that contains various entities and a large amount of data. One pos-
sible solution is to introduce an IoMT authentication framework integrated with blockchain
technology to create a general architecture that can eliminate the issues mentioned ear-
lier [
105
]. Elliptic Curve Cryptography (ECC) and Physically Unclonable Functions (PUFs)
are authentication schemes between system components. Five phases are included in the
proposed schemes: revocation phase, password and biometrics update, login and authenti-
cation, registration, and system initialization. Multiple procedures are performed in these
phases, such as creating a blockchain network, setting up the cryptographic parameters,
registering the entities with the Register Center (RC), initializing authentication between
entities, updating certain information, and summarizing the actions to be performed when
a private key is lost or compromised. The proposed scheme achieves the desired security
and operational requirements based on the security and performance analysis.
Regular and remote monitoring of patients with chronic diseases is critical due to
their unpredictable health conditions. Metrics such as scalability, processing time, and
security are essential when implementing a blockchain-based and proxy re-encryption
healthcare system [
106
]. The proposed system architecture comprises hospitals, physicians,
and patients linked with the ministry of health through the blockchain network. IPFS is
used to store the collected and encrypted health data. Patients are supported with IoT
medical devices that collect health data and a smartphone that acts as a bridge with the
medical entities. To speed up the consensus process and data storage, the Clique PoA
algorithm is implemented in the system. Compared to the state-of-the-art methods, the
proposed system offers high security.
GarliMediChain [
107
] is a health data-sharing anonymous system that ensure privacy,
anonymity, and low latency by integrating blockchain technology with garlic routing. In
addition, to maximize institutions’ payoffs, a coalition system is introduced. Fictitious play
is used to enforce trust among coalition groups. Furthermore, Proof-of-Epidemiology-of-
Interest (PoEoI) is a new consensus algorithm proposed to select miners and generate blocks
based on an addition number game. The proposed system consists of five components:
the fictitious play, a learning paradigm, a coalition group, a consortium blockchain, garlic
routing that hides the identities of communication entities, and edge nodes to connect
smart devices. The simulation results demonstrate that the proposed system is robust
against attacks and efficient.
To achieve distributed consistency in a peer-to-peer (P2P) environment, an architecture
called BIoMT [
108
] is proposed, which consists of consortium blockchain built on top
of Hyperledger Fabric to provide provenance, transparency, integrity, and security for
serverless P2P. Distinct operational controls are implemented using different protocol types
to reduce resource consumption costs. Moreover, a new lightweight consensus algorithm
is proposed based on PoW; the proposed algorithm utilizes the predefined policies of
Hyperledger Fabric to reduce the transmission bandwidth and the required computation
power. The proposed system architecture contains a serverless network to manage network
resources required to complete a process. The BIoMT node is responsible for managing the
records until submitting them to the filecoin, representing immutable storage belonging to
a third party. On-chain and off-chain designs are provided for the communication protocols.
The Hyperledger Fabric expert handles real-time medical transactions. Two storage designs
are included to eliminate any capacity issues, primary and secondary. The experimental
results demonstrate that BIoMT reduced the resource constraints.
Hiding sensitive data from malicious parties requires advanced methods which can
be utilized from Information Hiding Techniques (IHT). When combining IHT with smart
contracts and blockchain technology to create a framework for the medical supply chain,
Sensors 2023,23, 788 25 of 43
security and privacy aspects are enhanced [
109
]. This study proposes a different method of
encrypting the information into other auxiliary messages using improved steganography
techniques. Multiple pre-authenticated healthcare providers are merged into a private
cluster in the blockchain network, and only entities inside the network are allowed to
communicate and participate in the processes. Using smart contracts, one-time secret
keys are securely created and distributed among related parties. The proposed frame-
work comprises cluster pre-selection, hash key registration, and smart contract phases.
The proposed system architecture is divided into cloud, fog, edge, and healthcare IoT
device layers. This approach ensures lower execution time with higher security than other
classical approaches.
A model that combines Self-Sovereign Identity (SSI), Verifiable Credential (VC), De-
centralized ID (DID), Attribute-Based Access Control (ABAC), Role-Based Access Control
(RBAC), and blockchain technology called Decentralized Self-Management of data Access
Control (DSMAC) [
110
] is proposed to allow patients to control their medical data. For
emergency cases, advanced access control techniques are implemented using verifiable
credentials and decentralized identifiers. In addition, role-based access control policies are
conducted by leveraging smart contracts. A DID document is used to create an attribute-
based access control mechanism. The proposed framework comprises three layers: the
user layer, the F2C layer, and the IoMT devices layer. Based on performance evaluation,
the proposed framework is efficient and scalable regarding cryptographic computations,
latency, throughput, and execution time.
4.3. Smart Environments
4.3.1. Smart Home
SHIB [
120
] is a smart home based on the IoT-Blockchain that addresses the challenges
related to the ability of extension, trust access control, and data privacy. Only the creator
of ACC can add new policies, update existing ones, or remove privacy policies from the
ACC blockchain. In order to use the SHIB architecture, a smart homeowner must have
agreed to a smart contract with the other parties involved. Using defined policies, smart
contracts are able to restrict access requests when misbehavior is sensed in the network to
increase the security and privacy of home data. Compared to other existing models, this
design contains a Judge Contract (JC) that can perform judgment and impose penalties
on misbehavior.
ELIB [
121
] is a model proposed to eliminate specific issues associated with blockchain
technology, such as high bandwidth, limited scalability, and high computation complexity,
and implement an efficient smart home design that fits IoT necessitates. Smart homes
with limited resources benefit from a centralized manager that produces shared keys
for data transmission and processes every incoming and outgoing request. An overlay
network is generated as shown in the current ELIB model; high-equipped resources can
merge with a public BC that guarantees devoted security and privacy. The suggested
ELIB model includes three optimizations: a Distributed Throughput Management (DTM)
strategy, certificateless cryptography, and a lightweight consensus algorithm. Based on the
experiments with several parameters, ELIB demonstrated excellent performance.
LSB [
122
] refers to a “Lightweight Scalable Blockchain” that utilizes overlay networks
to achieve decentralization and end-to-end security. Network nodes are grouped into
clusters using a clustering algorithm (similar to [
137
]). A Cluster Head (CH) is elected in
each cluster; it represents the node with maximum coverage (neighbors). CHs are called
Overlay Block Managers (OBMs) because they manage the blockchain network. A genesis
transaction must be created by overlay nodes using one of the following approaches:
certificate authorities and Burn coin in Bitcoin. A genesis transaction is broadcasted from
one OBM to another after verification. In order to reduce delay and mining processing
overhead, they designed a consensus algorithm called distributed time-based. Cluster
heads are responsible for efficiently employing the distributed trust approach among
network nodes to verify new blocks. A distributed throughput management algorithm is
Sensors 2023,23, 788 26 of 43
used to ensure that the network throughput is stable enough (based on specific parameters).
LSB is designed to fulfill IoT fundamental requirements such as connectivity and mobility
and real-time applications. It is implemented in different scenarios that include high-
resource devices and low-resource devices. The authors analyzed and discussed further
aspects of LSB, such as OBM reward, auditability, and complexity. According to a security
assessment, LSB is highly fault-tolerant and secure to a wide range of attacks. Further
development is required to evaluate this model in real-world settings.
A privacy-preserving authentication scheme is proposed to illustrate how data are
collected and shared in smart home applications [
123
]. The proposed scheme combines
three base concepts to create a secure framework: edge computing, smart contracts, and
attribute-based access control. Data are transferred to the cloud securely and privately
using a differential privacy method which offloads systems’ heavy processing; eventually,
the system scalability is increased. The proposed system architecture consists of end
users, IoT devices, multi-edge servers, and the cloud. Two types of contracts are used
in the attribute-based access control: register contract and access contract. The authors
explained how transactions are being carried out; four phases are used: chain transaction,
state delivery, request control, and initialization. The differential privacy enhancement
mechanism includes a plain algorithm, private algorithm, dataset, and implementation. The
proposed approach performs better than the existing scheme; it provides efficient security,
privacy, resiliency against attacks, fine-grained access control, and less computing cost.
4.3.2. Smart City
A use case is presented in [
116
] to address privacy exposure and security threats of
cyberinfrastructure in a smart city. This study discusses IoT-based access control first using
two primary models; Discretionary Access Control Models (DAC) and Mandatory Access
Control Model (MAC). DAC explains how to transmit the rights of the object from one to
another; MAC refers to classifying objects in the system and how to regulate access among
them. This study compares the difference between implementing this model in traditional
and blockchain-based architectures. The process of exchanging data starts between actors
(user and organization or two users where one is outside the infrastructure), the data
are transmitted to IoT cyberinfrastructure, which is followed by the private cloud and
blockchain network. The user needs to encrypt only the part of the data that can be shared.
This study is still in the early research phase and must be investigated further. A study
on the smart city security model is illustrated in [
117
], covering the theoretical aspects.
The authors started discussing data management and distribution, which were followed
by communications, private key management, securing third parties, smart contracts
(automation of procedures), and protocols.
A privacy-preserving innovative framework called PrivySharing [
118
] is proposed to
secure IoT data in a smart city environment. Privacy is preserved as each channel has a
finite number of approved organizations and processes a specific type of data (financial,
health, energy, etc.). Private data collection and encryption are used to isolate further
and secure data within a channel. A private data collection methodology is adopted to
ensure the privacy of critical data by sending the data directly to the authorized requesting
node (NMSP). A Membership Service Provider (MSP) defines the access rights and which
RCAs/CAs are trusted. A different Ch is used for each data type to ensure decentralization,
scalability, and privacy. This design provides the concept of “right to forget” regarding user
data, efficiency in terms of computational requirements and energy consumption, user-
defined fine-grained access control, allowing users control over their data while providing
an auditable network operation, blockchain access through API, and reward system for
data sharing.
4.3.3. Smart Factory
A multi-center blockchain-based security and privacy model is proposed to reshape
traditional IoT architecture for smart factories [
119
]. The proposed architecture consists
Sensors 2023,23, 788 27 of 43
of five layers: application layer, firmware layer, storage layer, management hub layer,
and sensing layer. Users are provided with different services by the application layer.
The firmware layer is used to connect all layers through underlying implementation
technologies, data are stored in a distributed form in data centers represented by the storage
layer, the process of managing the data and creating blocks is completed by the management
hub layer, and finally, the process of obtaining data and preprocessing occurs in the sensing
layer using sensors with microprocessor (computing power). This architecture is divided
into intranet and extranet; the first deals with data collection and storage, and the latter
aims to offer users different services by utilizing the data. This model is designed with a
private blockchain where all nodes are trusted initially; thus, it does not include a reward
mechanism or competition. The block structure is created with two parts: block body and
header (stores structured data and its attributes). Finally, the authors combined two models:
Biba and Bell-La Padula (BLP) to ensure CIA requirements.
4.3.4. Smart Traveling
Due to the massive data generated and the vast scale of IoT networks, data fusing
and privacy are still significant challenges. Thus, an inter-cloud data fusing and privacy-
protected platform based on JointCloud is proposed to address the analytic activities and
data mining of IoT [
124
]. The authors discussed two main platforms implemented on single
clouds: Baidu and Amazon AWS. Then, they presented their design based on JointCloud
Computing (JCC) because it is more suitable for constructing complex applications. This
framework can be broken down into three tiers. The first tier is made up of a variety
of sensors that are linked to several clouds. In the second tier, JointCloud Collaboration
Environment (JCCE) links clouds together. Services are located in the third tier, based on the
JCC, and each user is provided with an application and personalized service. This platform
offers enhanced security because data are stored in a private cloud. In addition, it eliminates
privacy disclosure and prejudice because, in JCCE, trades are automatically executed.
4.3.5. Smart Agriculture
A blockchain-based application is implemented to store malicious information to
prevent future attacks for a smart-farm security monitoring framework [
130
]. The appli-
cation consists of three layers: the smart farm layer, the cloud layer, and the blockchain
layer. The smart farm layer consists of different sensors to collect data, and the cloud
layer is responsible for processing sensors’ events and retrieving the required information.
The blockchain layer comprises the Ethereum blockchain with smart contracts to check
environmental conditions and store farming data. Ethereum nodes perform the mining
process; an entity or individual controls these nodes. The cloud layer consists of an AWS
cloud, Anomaly Lambda Function, and Infura Ethereum API that runs smart contracts and
connects the middle layer with blockchain layer nodes. This framework is implemented
to work with only one consensus algorithm: Ethereum proof-of-work (POW). Based on
performance evaluations, this prototype resulted in nominal network latency.
4.4. IoT Device Gateway
A blockchain-based connected gateway design is presented in [
111
] for BLE-based
devices to address privacy preferences in IoT networks. Each user has to consent to data
access by any third party using the gateway to prevent privacy leakage. Furthermore,
a robust digital signature technique is presented to facilitate the secure management
and authentication of privacy preferences. The proposed blockchain gateway consists
of gateway administrators, end-users, and administrators or owners. The administrator
stores the information of all devices in the network and their privacy policies: for example,
device features, manufacturer information, unique device name, and other attributes. The
architecture of the blockchain gateway consists of the user interface and administrator
interface and the internal components that can be managed from these interfaces. In the
proposed gateway design, device binding refers to the administrator’s registering or adding
Sensors 2023,23, 788 28 of 43
a new device. Later, a new Proposed Digital Signature Scheme (PDSS) is proposed based on
robustness and intractability using bilinear pairing and ECDLP. PDSS is realized using six
phases. Furthermore, this study discussed the privacy preference preserving concept and
intelligent access control on IoT devices. Detailed evaluation scenarios are implemented in
this study for PDSS, blockchain gateway, and smart contract management.
Another approach addressed the authentication and decentralization of the IoT device
gateway by implementing a basic interface using blockchain technology [
112
]. In addition,
this architecture supports IoT infrastructure with lacking versatility and anonymity within
its design. In addition to the interface, IP mapping for network nodes is included. The
design environment consists of a customized hub, wired connections, and distributed
ledger, preventing direct communication with the internet (only through the home server)
and allowing the server to run on any device using a programming language (Node.js). The
home server conducts the process of obtaining data (collection) and monitoring devices.
The proposed design consists of four parts: smart device, home router, home server, and
remote service. The process starts when data are generated from a smart device and passed
to the home router for port forwarding. The data are transmitted to the home server; in
this step, the data are parsed, and the request is appropriately encrypted. Unused data
by remote services are removed, and the home router receives the request. It allows data
to be sent to the remote service, and the remote service parses the incoming data and
decides the proper action. The home router receives the data using port forwarding from
the allowed service and transfers the action to the smart device to be performed. Further
considerations can be made to improve the security by providing a flexible interface from
the manufacturers, and a list of IP addresses must be included to identify legitimate access
requests. Further experiments can be conducted to determine how robust this design is
against possible IoT infrastructure attacks.
4.5. IoT Information Systems
Data integrity is one of the main areas researchers focus on when designing security
and privacy models. A novel privacy-preserving model is proposed to address this area
in IoT information systems using blockchain, bilinear pairing, and a Lifted EC-ElGamal
cryptosystem [
113
]. This work is completed in a cloud environment for outsourced data
integrity. To support the aim of this study, they proposed a protocol that achieves correct-
ness, privacy, security, dynamic updating, and public verification. This scheme includes
a data-checking model that consists of a Data Owner (DO), Key Generate Center (KGC),
cloud server, and auditors. Any part can act as the auditor for data integrity checking
and receives a reward, but it has to own enough capabilities and expertise to perform this
task. The outsourced data are represented by the high volume of data that the DO has that
forces the DO to request cloud storage. A trust model is also used to check the integrity
of outsourced data. This model assumes that the DO, cloud servers, and auditors are
semi-honest, making it suitable for practical application. Compared to other approaches,
this scheme supports dynamic auditing; it performs remote data integrity checks without
needing a third-party auditor, eliminating data privacy leakage.
Another study presented a framework for a sharing security mechanism for IoT infor-
mation systems by adapting and combining transaction blockchain and data blockchain
(double-chain model) [
114
]. Using partial blind signature algorithms, privacy protection
and transaction efficiency can be enhanced. A node cooperation technique based on the
dynamic game method is proposed to prevent any local dominance of malicious behavior.
To reach Bayesian equilibrium, the node’s institutional reputation value is reported to
estimate the state of the unknown node; it is also used to identify malicious nodes and
correct their overall report. The authors improved the PBFT algorithm to eliminate the
issues found in the common consensus algorithms, such as requiring intensive resources
and being computationally time consuming. In the total number of nodes that calculates
consensus,
f= (n−
1
)/
3 must be the maximum number of error nodes. They considered
reputation and computing power as one solution for accounting nodes or legal currency
Sensors 2023,23, 788 29 of 43
(digital currency) as the other. A coin center is set up using the cloud service to avoid
multiple payment issues and privacy breaches. A distributed accounting system is added
to the chain to allow the traceability of bills.
4.6. Management Systems
In any IoT ecosystem, it is crucial to compute the assets (operations, users, devices,
etc.) and their provenance. Thus, a secured ID management system based on blockchain
technology is proposed to tackle current security and privacy issues [
131
]. The authors
developed a proof-of-concept prototype in a business case scenario. This prototype consists
of three stages; the first stage discusses how the data can be managed and modeled to be
stored securely on the blockchain. The second stage explains different rules developed
using smart contracts for any agreement and monitoring these rules while the core trans-
actions are also being processed. The third stage manages identity verification to address
security and privacy aspects. Four types of smart contracts’ rules are used: computer
ID management, software ID management, user ID management, and data backup ID
management. The proposed prototype can be further explored for large-scale businesses in
terms of adaptability and extendability.
SmartDID [
132
] is a distributed identity management system proposed due to a lack
of a systematic proof system and issues in IoT, such as privacy, security, and resource limita-
tions. The authors constructed a distributed identity system to support certain features, for
example, supervisability, unlinkability, and Sybil attacks resistance. The authors considered
the following: to hide privacy information, cryptographic credentials and plaintext are
used to create a dual-credential model. In addition, a zero-knowledge scheme is used as a
verification mechanism, and a commitment scheme is used for encryption to secure crypto-
graphic credentials. However, there is a possibility of a Sybil attack, and such methods are
unable to hide attribute linkage. Thus, a distributed system with multiple pseudonymous
userIDs and one unique masterID is designed to address the mentioned issues. The system
model is comprised of supervisors, verifiers, issuers, holders, and a consortium blockchain.
IoT devices can act as verifiers to verify credentials and publish access policies, while the
committee acts as a credential issuer to sign and issue credentials. SmartDID was evaluated
and compared with two other approaches in terms of proof generation and credential
generation times, and SmartDID achieved the best performance.
4.7. Other Works
4.7.1. Cloud Environments
Protecting the data-sharing process in cloud environments is crucial to eliminate any
leakage or breach. A secure data-sharing scheme that integrates blockchain technology,
Information-Centric Networking (ICN), Identity-Based Encryption (IBE), and Proxy re-
encryption (PRE) is proposed [
128
]. PRE allows transforming a file from delegator to
delegatee by encrypting the file with the delegator’s public key. In IBE, the email (identity)
is used as the public key for encryption. Confidentiality is achieved using a secure access
control framework; security and privacy are ensured using a complete protocol based on
the PRE scheme; network bandwidth utilization and enhanced data delivery are ensured
through proxy nodes. The designed platform consists of data producers responsible for
generating the data, cloud service providers (CSPs), data owners to whom the data belongs,
and data users representing the recipients of the information. Performance analysis and
comparison indicate that the proposed scheme is efficient compared to other works.
It is possible to convince the verifier that a particular assertion of information is correct
without involving any valuable information; this is called zero-knowledge proof, which can
be generated using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge
(zk-SNARKs) tool. This approach utilizes zero-knowledge proof combined with smart
contracts to allow trusted sharing between semi-trusted cloud servers, Cloud Service
Providers (CSPs), and data owners [
129
]. Proxy re-encryption technology is integrated into
this model to provide authorized CSPs with a secure data-sharing model. The proposed
Sensors 2023,23, 788 30 of 43
system model contains six entities: the blockchain network, smart contracts, private key
generator (PKG), semi-trusted cloud server, cloud service organization, and data owner.
Performance analysis is completed for content privacy, identity privacy, data validity, and
traceability. This model is still in the research phase and has not been implemented yet.
4.7.2. Fog Computing
Using alliance chain and fog computing, a blockchain-based distributed access control
system is proposed to offer security for IoT networks using a combined LSB and MLNCML
encryption scheme [
125
]. This system utilizes fine-grained and dynamic access control
to eliminate a single point of failure. In the proposed system architecture, edge nodes
connect smart devices to the internet and allow resource requestors to access the services
in the smart devices. The alliance chain is formed using all blockchain nodes, including
the edge nodes. Thus, edge nodes offer a distributed manner when performing access
control decisions. In the proposed encryption scheme, the first encryption is performed
by the chaotic series, and then the LSB algorithm obtains the encrypted image. Finally,
secondary encryption is performed by the MLNCML. In the proposed access control model,
ABAC, LSB, and chaotic encryption is used, and the authors included a detailed workflow
of this model. Using alliance blockchain limits access to increase the security and safety
of the system; it also ensures the validity of the data and prevents any tampering because
the data are distributed on the alliance chain. From a security perspective, the authors
discussed data privacy, data theft prevention, anti-data tampering, and file storage security.
By comparing this work with other approaches, it offers dynamic access control as well
as fewer start-ups and nodes; in addition, it does not require paying remuneration, the
proportion of voting rights is not artificially needed, less computing power is required, and
it features enhanced management. This system can be improved by including a lightweight
consensus protocol, avoiding a single point of failure by including smart contracts for
distributed access control and ensuring the security and credibility of edge nodes using
trusted computing technology.
An IoT distributed middleware layer is integrated with permissioned blockchain to
construct a lightweight and robust system for managing IoT data [
126
]. In the proposed
system, the IoT management layer handles IoT data instead of assigning end devices
or a central authority to handle this task. This system adapts a networked smart object
(NOS) to manage heterogeneous sources’ data and evaluate it, since it is a flexible and
cross-domain middleware approach. NOS adapts the MQTT protocol working mechanism
(publish and subscribe) for information exchange. NOS can enforce access control rules
using a sticky policy mechanism. In the investigated scenario, low power consumption,
low latency, and a limited number of users are required; thus, a Byzantine Fault Tolerant
(BFT) replication consensus algorithm is used since it fits such requirements. Evaluation
experiments are completed to recognize the proposed system’s robustness regarding access
control, confidentiality, integrity, and performance under malicious attacks.
4.7.3. Edge Computing
A study addressed the access control issue in edge computing applications called
TrustChain [
90
]. TrustChain aims to eliminate centralized processing delays and privacy
problems while preserving IoT networks’ resources. TrustChain is based on the following
characteristics: interoperability, compatibility with different models, enhanced scalability
and privacy, reduced delay and operation with an efficient mining scheme. This model
describes various services offered using this model, including trust services (validator
management, trust management, auditing and accountability, and trust data repository),
blockchain services (consensus management, distributed ledger, ledger storage, P2P pro-
tocol, cryptographic services, and IPFS storage), smart contract services (registry, secure
container, and life cycle), membership services (registration, ledger identities, and resource
identities), and policy services (privacy, access control, and consent management).
Sensors 2023,23, 788 31 of 43
4.7.4. Reputation Systems
To address the issues of a single point of compromise and failure, a decentralized
trust model is proposed [
127
]. Using past interactions with public fog nodes, this approach
allows for maintaining the reputation of network users. The architecture of this model
consists of the following components. (1) The fog node has a unique Ethereum address that
associates with a list of evaluations that contains information such as generic reputation
score, cost of service, storage, and latency. (2) The IoT client provides feedback after
every interaction with any fog node, and the credibility factor measures the fairness
and honesty using several provided ratings, consistency, and trustworthiness. (3) Five
types of smart contracts are used, namely custom reputation, credibility, reputation, node
management, and client registration. The authors included a diagram to illustrate the
workflow of computing the reputation of fog nodes. The process is discussed in detail
between the blockchain platform, off-chain fog nodes, and front-end DApps. This approach
is implemented using the Ethereum blockchain to ensure network security, immutability,
and validation. Based on the evaluations for common security vulnerabilities, the results
show that the smart contracts are safe against them, and there are no major security issues
when running software that identifies security vulnerabilities.
4.7.5. Mobile IoT Applications
A blockchain-based architecture is explicitly proposed for mobile IoT applications
to enhance the security and privacy of users’ data by implementing user-controlled pri-
vacy [
115
]. This architecture consists of Storj, MQTT, and blockchain. The latter is used
to exchange information and store metadata and non-sensitive information. MQTT is a
lightweight communication protocol that serves as middleware between the blockchain
core and IoT devices. Finally, sensitive data are stored in a storage system represented by
Storj. To enhance the security of this model, two types of blockchains (each with its smart
contracts) are used: public and private. Smart contracts and the proxy are used to transfer
requests from public to private blockchain. Access Control Lists (ACLs) perform the ac-
cess control process for authorized nodes. Specific nodes, referred to as bridging nodes,
are participating in handling this process to allow intermediation and handle requests in
both blockchains.
In terms of the cryptography scheme, this architecture utilizes the advantages of the
Elliptic Curve Integrated Encryption Scheme (ECIES) framework with secp256k1 [
138
].
Many interactions and processes are discussed in detail in this study; for example, the
sensor controller performs the process of sending ACL, exchanging keys is completed by
the MQTT broker, registering ACL occurs within the PBVU smart contract, the proxy sets
the destination node and encryption key, event creation is completed using smart contracts
of a public blockchain, and finally, public nodes are responsible for parsing and decryption
procedures. Many concepts have discussed how this model achieves authorization, authen-
tication, data confidentiality, user-controlled privacy, user anonymity, and location and data
privacy. Based on the evaluations, all components can offer enhanced performance and
deliver high security and privacy. It is possible to enhance this architecture by eliminating
the disclosure of ACL using a privacy-preserving mechanism, investigating the single
point of failure, and high availability mechanisms. This approach can be implemented
and evaluated in a cloud environment and with other implementations to explore how it
affects network throughput. Finally, a mechanism can be integrated into this approach to
allow adding new nodes to the system. This mechanism must include policies and rules to
handle such operations.
?
Further considerations, including open issues (future works) and remarks, are
highlighted in Table 8. We can note that 79.07% of the included studies discussed open
issues for further consideration, while 20.93% did not discuss if any further work is required.
Some studies are only noted with “this approach is not implemented and evaluated yet”
because it was presented as a use case [
90
,
93
,
99
,
101
,
116
,
117
,
124
,
129
]. We used two marks
to differentiate between prons and cons:
H
and
v
, respectively. One of the most important
Sensors 2023,23, 788 32 of 43
features for security is to keep the system robust against attacks [
95
,
101
,
114
,
122
,
123
] and to
handle any misbehavior [
120
]. Requiring less computing power [
125
] is also essential, since
using blockchain technology might result in high power consumption. Supporting parallel
computing makes the proposed model more efficient [
95
]. In terms of scalability, one
model could offer an enhanced scalability [
90
], while the other model caused a scalability
issue [112]. The detailed description is illustrated in Table 8.
Table 8. Future Considerations.
Ref # Future Works
(Open Issues) Notes
[90]7
HEfficient mining scheme
HSignificantly small mining delay compared to PoW
HEnhanced scalability
HCompatibility with IoT business models
HInteroperability among several TrustChains
vNetwork overloading due to excessive exchange of messages between devices and server
vThe centralized server is used for storage
vThis approach is not implemented and evaluated yet
[91]7 v It does not support dynamic access control
[92]3 H It supports the fast and secure insert of the device in the perception layer
[93]3
HImproved access tracking
HProvided efficient access control and data transparency
vThis approach is not implemented and evaluated yet
[94]7 v (m-1)-private PTAS sacrifices little efficiency in exchange for safety improvement
[95]3
HSimple
HSupport parallel computing
HError not passing
HNot easy to attack
[96]3vIn order to achieve anonymity, public keys alone are not enough
vZero-Knowledge Proofs
[97]7
HRobust against several types of attacks
HLow expected computation cost
HModerate scaling
HTransparency and verifiability
HGood resiliency
vDoes not include trust and reputation management
[98]3
HThe proposed system includes auto-policy enforcement, on-chain policy management
HIt provides security, fee-less, trustworthy (without TTP), and scalability
v
In order to eliminate network congestion and reduce the latency, machine learning algorithms can be integrated
[99]3
HIncreased security due to the hybrid apporach that combined many lightweight cryptographic primitives with
public and private keys
vThis approach is not implemented and evaluated yet
[100]3
HProtection of EHR against fraud
HInteroperability of EHR data formats
HSimplification of current paradigms
HLow cost
HIoT data aggregator and sensor heterogeneity
vNot feasible for a large-scale network
vBlock is mined instantaneously by the virtual nodes itself; thus, miners are needed
[101]3HHigh resiliency against public blockchain modification and DoS, modification, appending, and 51% attacks
vThis approach is not implemented and evaluated yet
[102]7 H This approach offers on-demand rescission, accountability, and improved privacy
[103]3HUsing file and data sharing, this approach improved intersectoral collaboration
vIn terms of development and administration, it requires further accountability
Sensors 2023,23, 788 33 of 43
Table 8. Cont.
Ref # Future Works
(Open Issues) Notes
[104]3
HLow latency
HHigh feasibility
HEnhanced image size
vPrivacy concerns are still an issue when implementing in real-time environment
[105]3
HThe proposed architecture is not limited to healthcare domain only
HEfficient and pairing-free authentication scheme
HGuaranteed user anonymity with satisfied security requirements
vCertain security properties and efficiency metrics can be improved
[106]3HThe proposed system offers confidentiality, integrity, privacy, and access control
v
The authors suggests adding a fog layer between different system entities in order to process and filter the data
[107]3
HNew consensus protocol is introduced while maintaining the system’s robustness, efficiency, and adaptability
HThe proposed consensus protocol requires less computational cost than PoW and PoA
vOverall computation cost is not considered when this approach is implemented
[108]3
HReduces computational cost
HEnhanced node transactions performance
HThis design offers provenance, transparency, security, and integrity
[109]3
HThis work promises higher security levels and lower execution time
HThis approach achieves consistency, security, availability, integrity, and transparency
vTested for medical supply chain-based scenario only
vNo description of encryption and decryption of OTH
[110]3
HAs compared to other models, DSMAC overcomes the others as it provides scalability, sustainability, data
privacy, and emergency case
HDSMAC includes access control methods such as identification, authentication, and authorization
[111]3HUsing such an access control approach provides a non-repudiation feature and allows users’ preferences and
device policies to be preserved without tampering.
[112]3
HThe authors included the advantages and several important remarks that can be added to the proposed
approach to improve its performance
HFlexibility to use all encryption algorithms
HIntrusion prevention
HAdding a new layer of security
vThis approach will be considered useless if the database is corrupted in any form
vScalability issue: the processing performance decreases as the number of smart devices increases
[113]3
HSupports dynamic auditing
HSatisfies the public verification and correctness
HThe used storage method offers many advantages such as reducing cost
[114]3
HAnti-attack capability (10 types of attacks)
v
When this system is implemented in certain industries, its performance must be improved, as well as the risk of
privacy leaks must be addressed.
[115]3
vSingle points of failure (due to using smart contract proxy and MQTT)
vReduced throughput
vIncreased latency by the evaluation scenario
[116]3 v This approach is not implemented and evaluated yet
[117]3 v This approach is not implemented and evaluated yet
[118]3
HThis approach offers better scaling than a single Ch blockchain system
vMassive resource requirement
vIoT device integrity mechanism is required
[119]3HEnhanced scalability and flexibility
vIt might introduce large communication overhead
[120]7HHigh extension ability
HAbility to handle misbehavior
[121]3
HReduced processing time
HLow energy consumption
vRequires extra cost due to cloud usage
vLow scalability
[122]3HDecreases processing time and bandwidth compared to traditional blockchains
vUsing DTC in small networks can make the network vulnerable to Sybil attack
Sensors 2023,23, 788 34 of 43
Table 8. Cont.
Ref # Future Works
(Open Issues) Notes
[123]3HResilient against modification, linkage attacks, data mining, and DoS attacks
vExtra added noise (trade-off between accuracy and privacy) which might result in reduced data accuracy
[124]7 v This approach is not implemented and evaluated yet
[125]3
HThe proposed approach requires fewer start-up and running nodes
HLess computing power is required
HIt does not need to pay remuneration
HOffers effective management of the rights
[126]3
HThis approach supports confidentiality, integrity, and resistance to attacks
vThe proposed approach needs further testing with the common blockchain platforms such as Ethereum or
Hyperledger Fabric with more complex environments in order to compare its performance with other approaches
[127]7 v Further evaluations can be performed using throughput and power consumption
[128]7
HIt offers confidentiality, decentralization, auditability, and low overhead for data owners
vHigh proxy overhead
vDoes not include mutliple proxies
vSplitting re-encryption key scheme can be included
[129]3
HOffers content and identity privacy, data validity, verfiability, and traceability
vThe proposed scheme relies on trusted third paties
vThis approach is not implemented and evaluated yet
[130]3
HThe proposed approach offers mininal network latency
vIoT gateway is not implemented
vThis approach can work with Ethereum and PoW only
[131]3
HAuthentication and secure identity are provided
HThis prototype is cost-effective
vScalability issue, this prototype is not designed to handle large scale operation, adaptability and extendibility
can be further investigated
[132]3
HThe proposed system supports Credential Nested Verification
v
PBFT caused communication overhead between nodes which leads to limited reliability due to increased latency
and bottlenecks in network transmission
Additionally, the following remarks can be taken into consideration:
•
It is important to realize a mechanism to handle exchanging messages among network
nodes to reduce the overhead.
•
Eliminate using a centralized server that conflicts with the decentralization feature
which blockchain technology offers.
•
When designing a BIoT scheme, the scalability and confidentiality of data must be
proven to consider the approach efficient.
•
In several studies, the blockchain side is neglected, and there is insufficient information
about the type of blockchain, platform, and consensus algorithm.
•
It is crucial to evaluate and validate the proposed approach regarding security and
resiliency against different attacks.
•
The computation cost and communication overhead must be deeply investigated
and studied.
•
Although some studies enhance network privacy and security, it affects network
performance metrics negatively, such as throughput and latency.
•
A standardized evaluation manner needs to be followed; a public blockchain platform
can be used to compare the performance of different proposed approaches.
5. Lessons Learned
A wide range of sectors has benefited from the use of blockchain technology. IoT’s se-
curity and privacy concerns are still being explored when blockchain technology addresses
these aspects. In addition, blockchain allows a variety of security and privacy-preserving
models for the Internet of Things applications, offering decentralization, anonymity, au-
tonomy, transparency, privacy, security, collective verification, and many more. Since IoT
Sensors 2023,23, 788 35 of 43
devices are constrained with low capabilities, they require further consideration when
designing an approach to enhance their performance. The integration of blockchain tech-
nology and IoT has also introduced some limitations that must be considered: for example,
scalability, storage capacity, resource utilization, the method to deploy smart contracts, and
legal issues.
Blockchain and IoT factors are discussed in depth in this study through a systematic
literature study. It also offers several existing solutions and blockchain applications for
various IoT areas. Decentralization, auditing attributes, anonymity, and persistence are
just a few of the advantages of blockchain technology that made academic research and
industrial domains very attractive. This study has opened new doors for future research to
address many important issues that need further investigation.
As shown in the above tables, blockchain technology targets many applications and
sectors. However, a missing standard needs to be initialized when implementing such
approaches because an IoT network requires many parameters to be considered in the
pre-built or theoretical part design. For example, it is tough to decide what model is more
efficient than others when there is no public platform on which these technologies can
be integrated and built. Hence, it will require a new type of blockchain, for example,
consortium blockchain, with extra features to handle IoT demands. In our opinion, con-
sensus algorithms are one of the main limitations or drawbacks in such models because
using generalized algorithms does not allow the system to operate at full capabilities (the
performance level it was designed to operate at). In [
117
], the authors investigated the per-
formance with different consensus algorithms, which offers an advantage in determining
which algorithm results in higher security and performance.
In terms of the evaluations, we can note that there are still many distinct parameters
that have been used; this means that it offers various measurements that can be used
to evaluate the proposed models, and different programming languages can be used.
However, there is still a huge gap between each study, making it tough to address specific
issues because various tools might result in different values for each evaluation metric.
Many studies still miss the evaluation environment; these approaches can be investigated
in different environments and various metrics.
Many issues in the included research papers have been addressed in our study. For
example, we can investigate how to exchange messages between devices and servers using
a lightweight mechanism, eliminate a single point of failure, design a model to handle
large-scale networks, assign the mining process to specific nodes to achieve an efficient
mining scheme, propose a consensus algorithm that is specifically designed to handle
blockchain and IoT integration, eliminate privacy concerns when implementing the model
in a real-time environment, reduce resource requirements, improve data accuracy through
noise reduction, and handle legal issues when implementing an approach in a domain that
require high privacy such as the healthcare sector.
Finally, this study aims to provide an overview and further directions to researchers
interested in the BIoT concept. In addition to the systematic literature, we offered a technical
perspective on different studies included in this review. Based on the data collected, our
findings demonstrate that from 2018 until 2022, researchers are primarily interested in
designing approaches for the healthcare domain, followed by smart environments and
generic approaches. Such aspects include universal and endless possibilities for researchers
to improve and enhance current designs. From our perspective, future research seeks
business and industry sides as it grants the researcher the ability to implement and validate
the work in a real-time environment. In addition, healthcare and finances are critical in
BIoT applications. EHRs can be managed remotely and securely while preserving patients’
privacy and creating a decentralized government that controls cryptocurrencies and the
prediction marketplace. After all, many issues have not been addressed yet in which
the combination of blockchain and IoT can offer the optimum solution; however, proper
decisions must be taken into consideration for each target application, such as blockchain
type, platform, consensus algorithm, power consumption preferences, and network latency
Sensors 2023,23, 788 36 of 43
requirements. Further details on possible research directions can be found in the discussion
of Table 8.
6. Conclusions
IoT dramatically facilitates people’s daily lives by exchanging data and making com-
plete decisions. However, it raises sensitive issues of security and privacy at the same
time. Security and privacy concerns in the Internet of Things (IoT) could be efficiently
addressed by blockchain technology. This paper conducts a systematic literature review
of the state-of-the-art blockchain technology achievements that have been proposed to
enhance IoT’s security and privacy aspects. In this review, we discussed the basic prin-
ciples of technologies, including their architecture, protocols and consensus algorithms,
characteristics, and the challenges of integrating them. We overviewed the methodology
of our review, including the search strategy, eligibility criteria, and selection results. Our
findings are presented in a systematic literature manner based on the characteristics of
included papers. Our results (mainly focused on the targeted applications or domains)
show that 27.9% of the included studies focused on healthcare domain, 18.6% focused on
proposing generic approaches, and 23.25% aimed to target smart environments applications
divided into 9.3% for smart home applications, 6.98% presented systems designed to target
smart cities, and 2.32% for each of the following: smart factory, smart traveling, and smart
agriculture. Furthermore, studies of the IoT device gateway, IoT information systems,
management systems, cloud environment, and fog computing have been carried out (4.65%
for each separate application). Finally, the rest (
≈
6.99%) aimed to address edge computing,
mobile IoT applications, and reputation systems. We also showed various characteristics
for each study, such as the main goal or objective, development level, a blockchain platform,
blockchain type, consensus algorithm, evaluation environment and metrics (if found),
notes for each study which contain prons and/or cons, and future works (open issues). All
articles are also discussed from an architectural and operational perspective. Finally, we
identified significant gaps and future considerations that can be taken into account when
integrating blockchain technology in the IoT domain.
Author Contributions:
Conceptualization, H.D.Z.; methodology, H.D.Z.; formal analysis, H.D.Z.;
investigation, H.D.Z. and P.V.; writing—original draft preparation, H.D.Z.; writing—review and
editing, P.V. and S.M.; visualization, H.D.Z. and P.V. and S.M.; supervision, P.V. and S.M.; funding,
P.V. and S.M. All authors have read and agreed to the published version of the manuscript.
Funding:
Project no. 135074 has been implemented with the support provided from the National
Research, Development and Innovation Fund of Hungary under the FK_20 funding scheme.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
IoT Internet of Things
IP Internet Protocol
TCP Transmission Control Protocol
UDP User Datagram Protocol
P2P Peer-to-Peer
SPI Serial Peripheral Interface
IGMP Internet Group Management Protocol
ICMP Internet Control Message Protocol
REST Representational State Transfer
Sensors 2023,23, 788 37 of 43
DDP Distributed Data Protocol
OSPF Open Shortest Path First
IGP Interior Gateway Protocol
AS Autonomous System
WSN Wireless Sensor Network
RFID Radio Frequency Identification
PoW Proof of Work
PoS Proof of Stake
DPoS Delegated Proof of Stake
TaPoS Transactions as Proof of Stake
BFT Byzantine Fault Tolerance
PBFT Practical Byzantine Fault Tolerance
DBFT Delegated BFT
PoA Proof of Authority
PoSV Proof-of-Stake-Velocity
PoP Proof-of-Personhood
PoB Proof of Bandwidth
PoET Proof of Elapsed Time
SCP Stellar Consensus Protocol
PKC Public Key Cryptography
PKC Public Key Cryptography
THS Threshold signature
TPS Transactions Per Second
PoV Proof of Vote
PoT Proof-of-Trust
LSB Lightweight Scalable Blockchain
ELIB Efficient Lightweight integrated Blockchain
SGD Stochastic Gradient Descent
LSB Least Significant Bit
IPFS InterPlanetary File System
DTC Distributed Time-based
ECC Elliptic Curve Cryptography
PDA Personal Digital Assistance
IHM IoT Health Manager
DAG Directed Acyclic Graph
CV Cardinal Value
DV Data Validity
CD Contact Degree
ACC Access Control Contract
JC Judge Contract
DTM Distributed Throughput Management
CH Cluster Head
OBMs Overlay Block Managers
DAC Discretionary Access Control Models
MAC Mandatory Access Control Model
MSP Membership Service Provider
JCC JointCloud Computing
JCCE JointCloud Collaboration Environment
DO Data Owner
KGC Key Generate Center
ACL Access Control List
ECIES Elliptic Curve Integrated Encryption Scheme
MLNCML Mixed Linear and Nonlinear Spatiotemporal Chaotic Systems
PEP Policy Enforcement Point
CCBCM Consortium Blockchain Manager
AEBCM Aggregated Edge Blockchain Manager
EBCMs Edge Blockchain Managers
PoEoI Proof-of-Epidemiology-of-Interest
Sensors 2023,23, 788 38 of 43
IHT Information Hiding Techniques
SSI Self-Sovereign Identity
VC Verifiable Credential
DID Decentralized ID
ABAC Attribute-based Access Control
RBAC Role-based Access Control
DSMAC Decentralized Self-Management of data Access Control
ICN Information-Centric Networking
IBE Identity-Based Encryption
PRE Proxy Re-encryption
ZK-SNARKsZero-Knowledge Succinct Non-Interactive Argument of Knowledge
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