#  Blockchains for Internet of Things: Fundamentals, Applications, and
Challenges

Yusen Wu, Ye Hu, Mingzhe Chen, Yelena Yesha, Mérouane Debbah
Y. Wu, M. Chen, Y. Hu, and Y, Yesha are with the University of Miami, FL, USA.
M. Debbah is with KU 6G Research Center, Khalifa University of Science and
Technology, P O Box 127788, Abu Dhabi, UAE and also with CentraleSupelec,
University Paris-Saclay, 91192 Gif-sur-Yvette, France. This work was supported
by the U.S. National Science Foundation under Grants CNS-2312139 and
CNS-2312138.

###### Abstract

Internet of Things (IoT) services necessitate the storage, transmission, and
analysis of diverse data for inference, autonomy, and control. Blockchains,
with their inherent properties of decentralization and security, offer
efficient database solutions for these devices through consensus-based data
sharing. However, it’s essential to recognize that not every blockchain system
is suitable for specific IoT applications, and some might be more beneficial
when excluded with privacy concerns. For example, public blockchains are not
suitable for storing sensitive data. This paper presents a detailed review of
three distinct blockchains tailored for enhancing IoT applications. We
initially delve into the foundational aspects of three blockchain systems,
highlighting their strengths, limitations, and implementation needs.
Additionally, we discuss the security issues in different blockchains.
Subsequently, we explore the blockchain’s application in three pivotal IoT
areas: edge AI, communications, and healthcare. We underscore potential
challenges and the future directions for integrating different blockchains in
IoT. Ultimately, this paper aims to offer a comprehensive perspective on the
synergies between blockchains and the IoT ecosystem, highlighting the
opportunities and complexities involved.

##  I Introduction

As one of the most notable technologies in recent years, the Internet of
Things (IoT) is expected to transform our daily lives with numerous
applications such as smart homes, autonomous driving, smart grids, and more.
However, the rise of IoT will also lead to a data explosion. Through a
consensus-based data-sharing mechanism, blockchain techniques can provide IoT
with a distributed, immutable, and efficient database solution, paving the way
for a trustworthy data analysis and sharing platform.

Some existing works [1, 2, 3, 4] have explored the use of blockchain in IoT
applications. One of the most significant applications is edge artificial
intelligence (edge AI) due to the increasing volume of data generated by edge
IoT devices such as sensors and wearable devices. Processing data at edge
devices can enhance training speed and data privacy, eliminating the need to
exchange sensitive information, such as medical data, with other devices and
central controllers, thus reducing the risk of data leakage. However, since
edge devices are susceptible to various malicious attacks, utilizing
blockchains to address data and model security concerns becomes essential. The
application of blockchains in healthcare has also garnered considerable
attention. A blockchain ledger’s immutable and trusted nature ensures that
transactions are secure, and its records are resistant to attacks.
Additionally, permissioned blockchains can bolster data privacy for sensitive
patient information. They can also be used to establish an electronic patient
clinical trial system across different untrusted organizations, reducing
traditional time-consuming administrative processes in hospitals. Lastly,
leveraging blockchain for IoT communication stands as a crucial application in
enhancing the security of data transmission across networks and IoT devices.

In this article, we begin by introducing three fundamental blockchain systems:
a) Public blockchain, b) Private blockchain, and c) Permissioned/consortium
blockchain. We summarize their respective advantages, disadvantages, and
implementation requirements. We also summarize the security issues associated
with different blockchains. Next, we explore the application of these
blockchain systems in three critical IoT domains: edge AI, healthcare, and IoT
communications. Finally, we describe the challenges associated with
implementing blockchain for IoT applications, concluding our discussion at the
end.

Currently, several survey and magazine papers [5, 6, 7, 8, 9, 10, 11] have
introduced the use of blockchains for IoT systems. However, this article is
different from these existing papers in the following aspects. Compared to [5]
and [6], our paper details three distinct blockchains for different IoT
scenarios, especially for permissioned blockchains. Different from [7] that
ignored edge AI, we introduced permissioned blockchain for edge AI. Compared
to [8, 9, 10, 11] which discovered vast opportunities in the IoT domain, our
paper points to several differences: 1) We introduce several unique blockchain
applications for the Internet of Medical Things, for example, trusted executed
environment (TEE) based private smart contract for clinical trials. 2) We have
introduced choosing the appropriate blockchains for IoT applications, which is
not discussed in the current works. 3) We delved into real IoT applications
and summarized more important challenges for practical IoT settings.

TABLE I: Different types of Blockchain systems Property | Public blockchain | Permissioned / Consortium blockchain | Private blockchain
---|---|---|---
Consensus determination | All miners | Invited and authorized members | Administrator
[1pt/1pt] Permission | Public | Could be public or restricted | Could be public or restricted
[1pt/1pt] Scalability | Typically low to moderate | Moderate to high | High
[1pt/1pt] Security | High | Moderate to high | Low
[1pt/1pt] Immutability | Nearly impossible to tamper | Could be tampered with fewer nodes | Could be tampered with fewer nodes
[1pt/1pt] Efficiency (latency,cost,throughput) | Poor (high latency) | Moderate to Good | Good (low latency)
[1pt/1pt] Centralized | No | Partial | Yes (administrator)
[1pt/1pt] Consensus protocol | Proof of "X" (PoX) | Byzantine fault tolerance (BFT) | Byzantine fault tolerance
[1pt/1pt] Membership | Dynamic | Fixed; Know IDs of each other | Fixed; know IDs of each other
[1pt/1pt]  Applicability for IoT systems |  $\bullet$ High data security |  $\bullet$ Supply chain management |  $\bullet$ High sensitive data privacy
$\bullet$ Need transparency and trust |  $\bullet$ Healthcare and smart city |  $\bullet$ Require high transaction throughput
$\bullet$ Need global reach by all users |  $\bullet$ Permissioned network |  $\bullet$ Low-cost network fees
|  $\bullet$ Tokenization and micropayments |  $\bullet$ Require good transaction throughput |  $\bullet$ Private network participants

##  II Preliminaries and Overview

A blockchain system simply is a shared and decentralized ledger that is
publicly accessible by all the participants. A public immutable ledger is used
to record transactions across many nodes (clusters). And the underlying
consensus protocol ensures a new block added to the chains is in a final
agreement. There are at least four types of blockchain networks in the
industry: a) public blockchain, b) private blockchain, c)
permissioned/consortium blockchain, and d) hybrid blockchain. In this section,
we mainly focus on the first three blockchains and explain their advantages,
drawbacks, and implementation requirements as follows. Hybrid blockchain
usually just combines and merges them in different requirements. A detailed
comparison is shown in Table I.

####  II-1 Public Blockchains

In a public blockchain system, a participant (whether a miner or a user) can
join the blockchain network from anywhere and at any time. A core consensus
protocol is employed to ensure agreement, trust, availability, and safety. For
instance, Bitcoin relies on proof-of-work (PoW), whereas Ethereum utilizes
proof-of-stake (PoS). Users can employ a digital blockchain wallet to manage
cryptocurrencies like Bitcoin or Ether (ETH) for the purpose of exchanging
funds and assets. Public blockchains are well-suited for applications
requiring both high data security and a necessity for data transparency.

While the transparency and immutability of public blockchains make them
resilient to censorship and tampering, we continue to witness incidents where
attackers successfully steal cryptocurrencies as public blockchains are under
severe attacks, such as transaction verification vulnerabilities (e.g., double
spending, 51% attacks, privacy, etc.), private key vulnerabilities, phishing
attacks, wallet vulnerabilities, exchange vulnerabilities, physical theft,
etc. To enhance security, we can utilize reputable wallet software, store
private keys securely offline, enable two-factor authentication wherever
possible, avoid sharing private keys or sensitive information, and keep your
devices and software updated with the latest security patches. Moreover,
efficiency includes the speed of processing transactions (latency), the energy
and computational resources required (cost efficiency), and the system’s
overall ability to handle large volumes of transactions effectively
(throughput). However, due to the PoW consensus, efficiency of the public
blockchains are poor (high latency and low throughput).

####  II-2 Private Blockchains

A private blockchain is overseen by a network administrator, and participants
require consent or authorization from the administrator to join the network.
Due to the fewer nodes in a private blockchain compared to a public
blockchain, a private blockchain expends less time on achieving a final
consensus (high efficiency). Consequently, it conserves considerable resources
and provides significantly lower transaction fees with highly efficient
transaction execution times. It finds applications in secure data exchange and
the protection of highly sensitive medical data among small groups. However,
because a private blockchain is a centralized network with a limited number of
nodes, the data and transactions recorded in the ledger are comparatively less
secure when contrasted with a public blockchain.

Security in private blockchains relies heavily on access control and
encryption. Ensuring that only authorized participants can access the network
and that data is encrypted at rest and in transit is crucial. Additionally,
centralized control requires trust in the governing entity, however, it
introduces risks if the governing body is not trustworthy. Establishing clear
governance rules and mechanisms or implementing transparency and
accountability in decision-making processes can be a solution to mitigate
centralization risks. Moreover, even in private blockchains, smart contracts
can have vulnerabilities that could be exploited by malicious participants
(e.g., smart contracts are transparent to every participant). Therefore,
conducting thorough security audits of smart contracts before deployment or
implementing a monitoring tool to detect smart contract functions can be used
to address emerging vulnerabilities.

Figure 1: Blockchain for federated learning.

####  II-3 Permissioned / Consortium Blockchains

A permissioned blockchain, also referred to as a consortium blockchain,
operates as a distributed ledger that is not openly accessible to the public.
To become a participant in such a blockchain, one must be invited and gain
acceptance from the majority of existing participants. In permissioned
blockchains like Hyperledger Fabric or Iroha, each user’s identity is both
manageable and well-defined, and it is known to other participants. User
access control is granted through an identity management module.

A permissioned blockchain represents a partially centralized network. When new
members join the network, they also become owners of the blockchain and are
included as part of the blockchain’s committees. The underlying protocol of a
permissioned blockchain typically employs a Byzantine fault tolerance (BFT)
consensus protocol for achieving total order agreement so that the efficiency
is ensured. However, it’s important to note that BFT consensus has limitations
in terms of scalability, as latency tends to increase exponentially as more
nodes join the network, and it requires at least three rounds of communication
to reach a final agreement.

Security problems in permissioned blockchains involve assessing the robustness
of the consensus mechanism and the composition of the validator nodes. In
these networks, identity management and authentication are critical aspects of
security, as ensuring the legitimacy of participants is fundamental to the
system’s integrity. Therefore, the implementation of robust access control
mechanisms, including user authentication and role-based access control
(RBAC), is indispensable. Moreover, smart contract security, data privacy,
network security, and compliance with regulatory standards are important
considerations in permissioned blockchains, particularly in industries like
finance and supply chain management.

##  III Blockchains for Internet of Things

We introduce three main IoT applications of blockchain: 1) blockchain for edge
AI, 2) blockchain for IoT communications, and 3) blockchain for IoT
healthcare.

####  III-1 Blockchain for Edge AI

Edge AI enables distributed edge devices to collaboratively train a machine
learning model without data sharing. However, since edge devices are
vulnerable to malicious attacks such as poisoning and backdoor attacks, the
number of security issues and concerns in edge AI increases significantly
compared to centralized AI. For example, within the edge AI training process,
malicious edge devices may be compromised and send faulty models or gradients
to a server or pretend they are correct among all the devices. Meanwhile, in
edge AI, attackers among a large number of edge devices cannot be easily
detected and traced since most of the attacks are anonymous. To improve
training security, privacy, traceability (explainable AI), and data integrity,
permissioned blockchain seems a promising solution to public blockchain. In
particular, permissioned blockchain can create a secure, decentralized, and
highly sensitive system for storing and processing AI-generated data, models,
or weights in the ledger so that all the data in the transactions become
traceable. Meanwhile, permissioned blockchain can use its analytics engine
(failure detection) to block malicious identities thus improving model and
data security. We introduce two specific applications of permissioned
blockchain for edge AI as follows:

  * •

TEE-based smart contracts for privacy-critical and data-sensitive edge AI. In
permissioned blockchains, new participants must obtain permission before
joining. This structure enhances data privacy in sensitive scenarios, such as
maintaining the confidentiality of patient data across different
organizations. Hospitals might employ a permissioned blockchain network for
secure data exchange or to exclusively share AI models trained from edge
devices, such as wearable health monitors that can incorporate ML to analyze
health metrics in real-time, minimizing data leakage risks. These models can
be cataloged in a ledger using a unique model ID, model hash, and accompanying
metadata. Through this ledger, hospitals can write, query, or aggregate
different AI models, thereby establishing a private model-sharing platform
fortified with smart contracts. Due to the immutable nature of the blockchain
ledger, every AI training epoch can be chronologically documented. This not
only ensures traceability in AI training but also guarantees that the
parameters in the ledgers remain intact. Such immutability provides clarity on
the derivation of the final AI model and flags potential malicious datasets.
However, in a permissioned blockchain, smart contracts, although secure, are
typically transparent to all members. This openness can pose a risk:
inquisitive participants (though not necessarily malicious) could potentially
access data via these contracts. To mitigate this, the use of private smart
contracts is recommended. Given that smart contracts can be a vulnerable
component in blockchain, safeguarding them with a trusted execution
environment (TEE) ensures that data inputs stay concealed from even blockchain
clients. In essence, a TEE-based smart contract enclave executes all its
functions within a secure TEE zone, shielding all data even from the operating
system and administrative users. Therefore, the synergy of edge AI and private
smart contracts can notably elevate the levels of data privacy and security.

  * •

Federated Learning meets permissioned blockchain in the edge. Federated
learning (FL) enables mobile devices to collaboratively learn a shared
prediction model while keeping all the training data private. However, the
devices are vulnerable to high-severity attacks, such as DDoS or backdoor
attacks, which severely destroy the availability, security, correctness, and
liveness of an FL framework. Federated learning can effectually take advantage
of blockchain to bring trust to edge model, and improve training and model
security, as shown in Fig 1. Each device needs to register an identity in a
blockchain network and thereby, each device owns a unique identity, and
blockchain only accepts data from authorized devices. Edge devices can collect
data, analyze and train data, and then submit the gradients or parameters to
the blockchain ledger as logs or receipts to make training traceable. In other
words, leveraging blockchain enables edge models accessible and trusted.
Interestingly, one of the most significant challenges for data owners is that
they always doubt how and when AI-based applications will use their data in
federated learning, which means that there is no trust between data owners and
systems. Leveraging blockchain technology, the data owners can fully control
their data by giving permission or consent (e.g., electronic smart contracts
including who and when can access the data) in AI-based applications. Data
owners can surface their data ownership by licensing their data to the
application and storing all the policies or rules in immutable blockchain
ledgers based on different conditions. Importantly, FL prioritizes data
privacy since clients are often reluctant to reveal their datasets. As a
result, public blockchains, which inherently prioritize transparency and
openness, are not the ideal choice for FL.

####  III-2 Blockchain for Wireless Communications

Motivated by the use of cryptocurrency and the establishment of secure
decentralized networks among various untrusted entities, blockchain technology
has been integrated into wireless networks [12]. This integration permits
uncertain individuals to engage with each other across different network
areas. Specifically, blockchain can instill immutable trust among these
distinct networks. For instance, public blockchains can ensure secure data
access and transaction traceability. Conversely, permissioned or private
blockchains can enhance data privacy and support private resource sharing in
wireless networks. We have listed several research areas related to the
application of blockchains in wireless networks as follows:

Figure 2: Healthcare platform for secure image exchange with permissioned
blockchain.

  * •

Application management in wireless networks with blockchain. Wireless devices
include GPS, wireless computer devices (e.g., headphones or keyboards),
satellites, cordless phones, radio transceivers, etc. The applications of
wireless networks include different Wi-Fi applications or network services
from network providers installed on those devices. A public blockchain enables
different providers to manage their services and assets efficiently, for
example, by selling network services online through cryptocurrencies in an
Ethereum blockchain, and the network will be active only after the provider
gets a valid payment. Customers and providers can fully trust each other
through the immutable and transparent transactions in the ledgers. Likewise, a
detailed and well-designed smart contract needs to be developed automatically
to execute, control, or document legally relevant payments and events.

  * •

Adversarial modeling in wireless communications. Adversarial modeling is the
technique of identifying malicious attackers based on some suspicious
behaviors (e.g., time, entities, frequency, and protocols to thwart attacks),
rather than only searching for specific indicators of an attack. Traditional
solutions such as authentication and signature fail in this case since the
attackers are often no anomalies and pretending they are honest, thereby
adversarial modeling becomes the most common solution in wireless settings to
filter spam and detect malware. Combined with public blockchain technology,
the platform can act as an immutable notebook to record all the suspicious
behaviors of attackers and become an adversarial detection toolbox for all
users. A smart contract is designed and runs independently as a middleware to
interact with blockchain ledgers. For example, invoking commit suspicious
behaviors function to store a behavior record including malicious behavior ID,
timestamp, and descriptions, or query a transaction by a transaction ID, or
delete a transaction.

Take blockchain in wireless vulnerability assessment as an example. Wireless
security assessment helps identify vulnerabilities and security issues in the
wireless settings, e.g., Wi-Fi vulnerability assessment can help detect and
fix the risks before being attacked, we can discover nearby wireless devices,
investigate rogue devices, test WLAN infrastructure, and apply the test
results in a blockchain. A smart contract enables the monitoring of specific
parts of the radio frequency (RF) spectrum to identify unauthorized wireless
transmissions and activities. A dedicated smart contract also can be
implemented to commit all the reports and results to immutable ledgers.
Experts or researchers can join and contribute research results to the
assessment. An intrusion detection report can also be part of the assessment.

  * •

Secure routing protocols. Secure routing protocols are designed to counteract
routing attacks that might disrupt route discovery. A route fails when the
source node uses up its network retries without receiving an ACK, and the
routing poisoning attack generally happens by editing the routing tables. With
public blockchain, an ad hoc smart contract for managing and orchestrating
iptables can prevent them from malicious attacks, thus improving network
routing security.

  * •

Digital forensics with blockchain in wireless communications. Indisputably,
public blockchain technology can offer digital forensics with substantial
benefits for the whole procedure, including identification, preservation,
analysis, documentation, and presentation. Wireless forensics similarly
includes data collection, data analysis, security analysis, and network
investigations to determine whether the network has been used for illegal
purposes. The features of immutability, transparency, and trust of public
blockchain bring reliable digital forensics to wire communications.

  * •

User privacy with permissioned blockchain in wireless communications.
Blockchain transactions can be encrypted by data owners using private and
public keys, enabling them to store the ciphertext privately in a ledger.
Third-party participants, even authorized members within the same permissioned
blockchain network, are prohibited from misusing or obtaining the data. When
personal data is stored on the blockchain ledger, data owners have complete
control over when and how a third party accesses it. This undoubtedly enhances
user privacy and data confidentiality. Specifically, the advantage of
employing cryptographic primitives to encrypt data during transmission from
devices to servers via wireless networks is that it bolsters data privacy,
especially when the raw data (which lacks privacy measures) is susceptible to
eavesdropping attacks. Permissioned blockchain participants can log all
transactions in the ledger, efficiently manage or share their data, and
utilize permissioned blockchain technology to develop innovative data-sharing
and access control applications.

####  III-3  Permissioned Blockchain with the Internet of Medical Things

Blockchain’s adoption in healthcare is expanding rapidly with platforms like
BurstIQ helping healthcare entities securely manage vast patient data.
BurstIQ’s platform offers comprehensive patient records, potentially aiding in
curbing prescription drug misuse. The Internet of Medical Things (IoMT) 111The
internet of medical things (IoMT) is the network of Internet-connected medical
devices, hardware infrastructure, and software applications used to connect
healthcare information technology allows hospitals real-time patient
connectivity, enabling timely data acquisition by healthcare professionals.
Given the sensitivity of patient data, permissioned blockchains are preferred
for ensuring data privacy due to the ability to ensure security, regulatory
compliance, and efficient handling of sensitive patient data within an access
controlled and customizable network.

  * •

A zero-trust data sharing platform with IoMT. Medical devices produce vast
amounts of sensitive patient data. Therefore, a big data analytics platform is
crucial for managing these valuable patient details, encompassing modules for
data sharing, analysis, and storage. A demonstration of data sharing in an
IoT-based healthcare system among various hospitals is depicted in Fig. 2.
Smart medical devices, such as robotic surgeons or wearable sensors, transmit
diagnostic and therapeutic procedure data as transactions to the smart
contract. This contract then records the transactions in the blockchain
ledger. Various hospitals can participate as authorized members, enhancing
specific medical research domains within the blockchain. Researchers or
experimenters can access, download, and analyze the data once they obtain
permission (a demonstration of how a third party obtains permission is
discussed in section • ‣ III-3). Notably, since blockchains cannot store large
CT scan images, such image data is uploaded to a secure or decentralized
storage system [13] using API functions.

  * •

Secure lightweight solutions for IoMT systems with Blockchain. Both public and
permissioned blockchains require powerful servers for storage, consensus, and
calculations. Lightweight solutions, such as a lightweight consensus protocol
running on smart medical devices, aim to make all devices smarter by
facilitating consensus overhead between them. This is a research direction to
reduce resources and costs because there might not be a need to set up many
decentralized servers. In other words, a novel blockchain system designed
specifically for IoT devices could be developed. Sambhav et al. [4] proposed a
new blockchain that runs a consensus module and data storage module separately
(e.g., running consensus on IoT devices and storing data on regular servers),
allowing smart devices to become a node in the blockchain system. While some
lightweight cryptographic primitives can be used to simplify installation
steps and ensure IoT data privacy, given that many devices have a limited
configuration unsuitable for installing a full version of the cryptographic
library, new consensus protocols still need to be developed for IoMT. This
would account for limitations from medical devices (e.g., limited memory and
storage) when thousands or millions of IoMT devices connect in the future.

  * •

Novel access control algorithms for blockchain-enabled IoMT. The downsides of
contemporary access control in IoT still suffer from complicated use and
severe attacks. A blockchain and smart contract-based access control reduce
cumbersome processes (e.g., how to choose a reasonable access control
algorithm and deploy it). Permissioned blockchain, in particular, Hyperledger
fabric blockchain, has its own attributed-based access rule for user identity
management, and its channels can efficiently set the access control policy for
all the participants.

  * •

Permissioned blockchains with Electronic Health Record (EHR). Centralized
clinical trial systems, though commonly used, are often insecure and
inefficient, especially when managing and sharing data across various
disparate organizations. Ensuring patient and data privacy in such a
centralized system presents challenges. Constructing a permissioned
blockchain-based EHR application can effectively address concerns about data
confidentiality, privacy, traceability, and integrity. We present a detailed
patient clinical trial use case and its workflow to provide a clearer
understanding of the innovative clinical trial architecture, as depicted in
Fig 3.

Figure 3: Secure patient clinical trials and consent system workflow overview
with private smart contracts.

Workflow Overview. In this example, Institutional Review Boards (IRBs) approve
clinical trials; investigators oversee them, while researchers monitor
subjects and evaluate changes. HFB refers to the Hyperledger Fabric
Permissioned Blockchain, and all its members (owner of blockchain nodes) are
authorized participants. Patients first register on the blockchain network
(Step 9). They grant permissions using web interfaces (Step 8). Their
sensitive data is stored in an AWS cloud database or AWS S3 bucket, with only
authorized researchers able to access it (Step 10). Specifically, third-party
users need to send requests to ask for permission from patients. After
obtaining patient consent, the smart contract provides a data endpoint link to
the researchers. A distributed threshold signature [14] facilitates clinical
approval. When an IRB member creates a trial (Step 2), its details are
broadcasted to all investigators (Step 1). After verification, investigators
sign and move to a combination function (Step 3). If validated, the trial gets
committed to the blockchain (Step 4). Researchers require permission to access
patient trials (Steps 5, 6). Once they have the necessary approvals, they can
download patient data for study. The complete implementation and evaluations
are detailed in [15].

##  IV Challenges

While a public blockchain operates as an open ledger accessible to all, the
advent of permissioned blockchains has addressed some significant challenges,
particularly in terms of privacy and efficiency. However, permissioned
blockchains still confront several issues. We delve into the primary
challenges for both permissioned and public blockchains below.

###  IV-A Energy Efficiency

When integrating public blockchains with IoT applications, a primary concern
is energy efficiency. This concern spans beyond the energy demands of the IoT
applications and data transmissions themselves and extends to the energy used
in node competition stemming from consensus protocols. In many IoT systems,
edge devices operate on limited energy resources, which can preclude them from
serving as full consensus nodes or from handling intensive computational
tasks. Additionally, certain public blockchain consensus protocols, like Proof
of Work (PoW), require all participating miners (or devices) to constantly
compete in solving complex mathematical puzzles, a process that markedly
elevates energy consumption. Given these considerations, permissioned
blockchains could play a pivotal role in enhancing data communication
efficiency in IoT settings.

###  IV-B Scalability in Blockchains

Blockchains must be scalable to handle rising transactions and more network
nodes. Scalability in blockchain technologies is significantly influenced by
factors such as network size, consensus mechanism, block size and frequency,
degree of decentralization, and the level of trust among participants. Public
blockchains face scalability challenges due to the extensive validation
processes required by a large number of nodes, using computationally intensive
consensus mechanisms like PoW. In contrast, permissioned and private
blockchains can achieve higher scalability through more efficient consensus
protocols and smaller, more trusted networks that allow for quicker validation
processes. Adjustments in block size and intervals can enhance throughput, but
they must balance against potential security risks and the impact on
decentralization. Thus, each blockchain type makes specific trade-offs to
optimize scalability while considering security and operational needs.

###  IV-C Pruning in Permissioned Blockchains

The size of the blockchain continuously grows as new blocks are added. Over
time, this can lead to significant storage requirements, which can be
especially challenging for nodes that need to store the entire blockchain. The
larger the blockchain, the longer it may take to validate, synchronize, and
back up the data. This can affect the performance and speed of the entire
network. In order to solve the problem, implementing garbage collection
mechanisms to identify and remove data that is no longer required. This is
especially applicable in permissioned blockchains where certain data might
have a defined "active period" or "active duration".

###  IV-D Integration with Existing IoT Systems

Integrating public or permissioned blockchains with current IoT infrastructure
presents challenges. For instance, different blockchains have different
protocols, consensus mechanisms, and data structures. Integrating multiple
blockchains or connecting a blockchain with a legacy system might require
complex bridging solutions. However, there are emerging solutions to address
these challenges, including novel middleware for blockchain integration,
Blockchain as a Service (BaaS) offerings, and standardized APIs for
interacting with legacy systems. Crucially, ensuring that the entire system is
reliable and free from defects requires extensive debugging and testing. Even
when deploying on cloud platforms like AWS blockchain, there’s a learning
curve involved due to the platform’s complexity.

###  IV-E Interoperability for Private Blockchains

How do private blockchains communicate and interact with other private
blockchains or even public blockchains? The challenge of ensuring seamless
communication between disparate blockchain systems is often referred to as
"interoperability". Several solutions have been proposed, such as cross-chain
communication protocols, atomic swaps between different blockchain systems,
and standardized APIs. However, trust issues, security concerns, or data and
protocol mismatches still are the most significant barriers.

###  IV-F Incentivization Mechanisms in Permissioned Blockchains for Data
Owners

In public blockchains, network participants (like miners or validators) are
incentivized through monetary rewards (like mining or staking rewards) to
validate and add transactions to the blockchain. However, permissioned
blockchains generally lack this direct financial incentive, as they are
typically used by consortiums or groups of known entities for specific
purposes and do not rely on native tokens or cryptocurrencies for operation.
There are still ways to motivate participation, such as adding reputation. The
key is to identify what’s valuable for the participants and structure the
incentives around that.

##  V Conclusion

In this paper, we presented a thorough review of various blockchain
applications for IoT. Initially, we provided an overview of three distinct
blockchain systems, outlining their strengths, weaknesses, and implementation
needs. We also summarize the security issues associated with different
blockchains. Subsequently, we delved into three primary blockchain
applications: edge AI, IoT communications, and IoT healthcare, illustrating
their function across different blockchain platforms. In conclusion, we
discussed the challenges and future research directions for blockchain’s
integration within IoT contexts.

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Yusen Wu<EMAIL_ADDRESS>is an Assistant Scientist at the Institute for
Data Science and Computing (IDSC), University of Miami, USA. His research
interests include distributed systems, blockchain, and AI for healthcare.

Ye Hu<EMAIL_ADDRESS>is currently an Assistant Professor at the University
of Miami. Her research interests include wireless networks, machine learning,
game theory, unmanned aerial vehicles, and cyber-physical systems.

Mingzhe Chen<EMAIL_ADDRESS>is currently an Assistant Professor at
the Electrical and Computer Engineering Department, University of Miami, USA.
His research interests include machine learning, virtual reality, unmanned
aerial vehicles, and wireless networks.

Yelena Yesha<EMAIL_ADDRESS>is the Knight Foundation Endowed Chair of Data
Science and AI at the Institute for Data Science and Computing (IDSC),
University of Miami, USA. Dr. Yesha is also the Innovation Officer and Head of
International Relations. She is endowed professor both in Department Computer
Science and Department of Radiology at UM. Her research interests include
blockchain, big data analytics, and AI for healthcare.

Mérouane Debbah<EMAIL_ADDRESS>is a full professor at KU 6G
Research Center, Khalifa University of Science and Technology and
CentraleSupelec, University Paris-Saclay, 91192 Gif-sur-Yvette, France. He has
founded several public and industrial research centers, start-ups and is now
Chief Researcher at the Technology Innovation Institute in Abu Dhabi. He is a
frequent keynote speaker at international events in telecommunication and AI.
His research has been lying at the interface of fundamental mathematics,
algorithms, statistics, information and communication sciences, with a special
focus on the applications of random matrix theory and learning algorithms to
communication sciences. In the wireless Communication field, he has been at
the heart of the development of small cells (4G), Massive MIMO (5G) and Large
Intelligent Surfaces (6G) technologies, for which he received multiple
distinctions.