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A survey on advancements in
blockchain-enabled spectrum
access security for 6G cognitive
radio IoT networks
Nassmah Y. Al-Matari 1,3, Ammar T. Zahary 1,3 & Asma A. Al-Shargabi 2,3
The emergence of 6G cognitive radio IoT networks introduces both opportunities and complexities
in spectrum access and security. Blockchain technology has emerged as a viable solution to address
these challenges, oering enhanced security, transparency, and eciency in spectrum management.
This survey paper oers a thorough analysis of recent advancements in blockchain-enabled security
mechanisms specically for spectrum access within 6G cognitive radio IoT networks. Covering
literature from 2019 to the present, the paper highlights signicant contributions and developments
in integrating blockchain technology with cognitive radio and IoT systems. It reviews spectrum access
security and shows how blockchain’s decentralized approach can solve related issues. Key areas
of focus include secure authentication systems, tamper-resistant spectrum sensing, decentralized
databases, and smart contracts for spectrum management. The paper also addresses ongoing
challenges like interoperability, scalability, and the need for comprehensive security frameworks.
Future research directions are proposed, emphasizing the development of advanced blockchain
protocols, integration with machine learning, and addressing regulatory and standardization concerns.
This paper provides valuable insights for researchers and practitioners aiming to leverage blockchain
technology, alongside ML/AI, to enhance security and eciency in next-generation cognitive radio IoT
networks.
Keywords Blockchain, Sixth-Generation (6G), Cognitive radio (CR), Internet of things (IoT), Spectrum
Access, Security
As we enter the intelligent information age, sixth-generation (6G) networks promise to transform wireless
communication, addressing challenges faced by current h-generation (5G) networks1. 6G will introduce
highly exible, scalable architectures, enabling applications in autonomous systems and smart cities2,3.
As globalization progresses, the amount of mobile data trac is increasing at a fast and exponential rate.
According to an ITU-R estimate, the monthly worldwide mobile data trac was recorded at 390 exabytes in
2024 and is expected to reach 5016 exabytes by 20304. e Internet of ings (IoT) plays a key role in this
transition, with 6G-enabled IoT ensuring reliable communication for billions of devices5.
e number of IoT devices is projected to grow from 15.4billion in 2023 to over 29.4billion by 20306. As
IoT develops, 6G is set to exceed the limitations of 5G, boasting ultra-fast data rates, minimal latency, extensive
coverage, precise localization, and supporting massive machine-type connections. Predicted to be approximately
1000 times faster than 4G and 100 times faster than 5G, 6G also promises enhanced network coverage and
reliability7. e advent of 6G-IoT presents challenges in ensuring ecient spectrum access (SA) for diverse
devices. With traditional narrow-band IoT (NB-IoT) unable to fully utilize spectrum resources, addressing
scarcity becomes crucial for optimal performance in 6G networks.
e rapid proliferation of IoT devices exacerbates concerns about spectrum availability5,7,8. Cognitive Radio
(CR) networks oer a promising solution to spectrum scarcity by sensing and utilizing unoccupied spectrum,
known as spectrum holes9. CR within mobile communication networks is viewed as an emerging technology for
implementing spectrum access and sharing mechanisms, to achieve optimal spectrum utilization10,11. e main
concept of Cognitive Radio-enabled Internet of ings (CR-IoT) devices is to exploit unused spectrum (spectrum
1Department of Information Technology, Faculty of Computer and Information Technology, Sana’a University,
Sana’a, Yemen. 2Department of Information Technology, College of Computer, Qassim University, Buraydah 51452,
Saudi Arabia. 3Nassmah Y. Al-Matari, Ammar T. Zahary and Asma A. Al-Shargabi contributed equally. email:
nsma.almtri@su.edu.ye
OPEN
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holes) and dynamically allocate it to secondary cognitive IoT devices, allowing them to opportunistically
transmit data, thus enhancing overall spectrum utilization by enabling unlicensed users to access spectrum not
in use by licensed users8,12,13.
Incorporating CR into IoT has paved the way for extensive connectivity within IoT networks, pledging to
accommodate an unparalleled multitude of sensors and devices14. Integrating Cognitive Radio into 6G networks
improves spectrum access eciency and network performance, essential for IoT networks, by enabling dynamic
spectrum allocation for diverse applications, but also introduces new security and privacy challenges15.
Blockchain technology (BCT) emerges as a signicant player in bolstering the security of spectrum
access within these networks, the integration of blockchain and 6G results in heightened security, expanded
bandwidth, and decreased operational and capital expenditures16. Integrating blockchain technology with IoT
systems enhances security by encrypting and digitally signing IoT data as blockchain transactions. Additionally,
it utilizes smart contracts to automatically update device rmware and address vulnerabilities17,18.
is study discusses advancements in blockchain-enabled security for spectrum access in 6G cognitive
radio IoT networks. It explores how Blockchain can address security challenges such as spectrum management,
interference, and authentication. e paper reviews recent research, presents Blockchain-enabled security
solutions, and highlights future research directions. e paper is organized as follows: Sect.2 outlines the survey
methodology. Section3 provides context on 6G networks, cognitive radio, IoT, and spectrum access security
challenges. Section4 oers an overview of blockchain technology and its relevance to spectrum access security.
Following this, Sect.5 discusses security challenges in 6G cognitive IoT networks. Section6 shis the focus
toward potential solutions through Blockchain-Enabled Security Solutions, including decentralized identity
management and smart contracts. Section7 covers future research directions. Finally, the paper concludes with
Sect.8, summarizing key ndings and recommendations.
Survey methodology
e literature review approach for this research entails a systematic and comprehensive examination of recent
advancements in blockchain-enabled security mechanisms for spectrum access in 6G cognitive radio IoT
networks. e review focuses on studies published from 2019 to the present to ensure the inclusion of the latest
developments and trends in the eld. A structured search strategy is employed, utilizing databases such as
Springer, IEEE Xplore, Google Scholar, Elsevier, ScienceDirect, MDPI, and IJRPR. Keywords such as “blockchain
technology,” “6G networks,” “cognitive radio,” “IoT,” “spectrum access attacks,” and “smart contracts” are used
to identify relevant literature. e initial screening involves reviewing titles and abstracts, followed by a full-text
assessment to evaluate the relevance and contributions of each paper. is approach guarantees a thorough and
up-to-date review of the literature, capturing key advancements and emerging trends.
Selection criteria for papers & sources
e selection criteria ensure that the review is comprehensive and relevant by including papers that focus on
integrating blockchain technology with cognitive radio and IoT systems for spectrum access security. Only
papers published from 2019 onward and sourced from peer-reviewed journals and reputable conferences are
considered. e process encompasses various types of research—such as theoretical, empirical, case studies, and
reviews—to provide a well-rounded perspective.
Scope and the contribution of the survey
is paper explores scholarly articles pertinent to the topic, emphasizing the substantial impact of blockchain
technology on the security landscape of 6G cognitive radio IoT networks. Table 1 provides a comparative
analysis of signicant research in this eld. Jahid et al.1 advocated blockchain integration in 6G and IoT to
address 5G data challenges, beneting IoT security and Industry 4.0. Pajooh et al.2 discussed the integration of
blockchain in 6G IoT for decentralized access control and security, laying the groundwork for Industry 5.0. Xu et
al.19 highlighted blockchain’s role in improving resource management and spectrum eciency in 6G networks.
Al-Dulaimi et al.20 examined cognitive radio’s role in alleviating spectrum scarcity in IoT, while Khasawneh et
al.21 reviewed CRN-IoT systems to meet wireless communication demands.
Zainuddin et al.22 explored blockchain’s security enhancements in IoT networks, focusing on supply chains,
healthcare, and smart cities, while addressing challenges like scalability and privacy. Bhaskar et al.23discussed
how cognitive radio optimizes spectrum use in IoT, and Mathew24 highlighted the integration of edge computing
and blockchain in 6G, emphasizing security challenges and emerging technologies like quantum computing.
However, while existing surveys focus on 6G IoT and blockchain integration, there is a gap in addressing
blockchain’s role in spectrum access security for 6G cognitive radio IoT networks. Our survey lls this gap by
analyzing recent developments, practical examples, and challenges in this area. It also presents a taxonomy of
threats to spectrum access security and counter-threat technologies, providing valuable insights for researchers
and practitioners in this specialized eld.
Sixth-generation (6G) networks and spectrum access security
is section introduces key topics in advanced communication technology, including 6G networks, cognitive
radio, IoT, and spectrum access security, establishing a framework for detailed analysis in subsequent sections.
Sixth-generation (6G) communication networks
e evolution of wireless technology has dramatically transformed communication, progressing from voice-
only 1G networks to the high-speed, low-latency 5G. With each generation emerging roughly every decade, 6G
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is expected around 203025,26. Wireless technology has continually improved spectrum use, eciency, reliability,
and data rates27.
As of 2022, there were nearly 1billion 5G subscriptions globally, with forecasts predicting growth to
4.7billion by 202828. 5G’s innovations, such as ultra-reliable low-latency communication (URLLC), massive
MIMO, and millimeter-wave (mmWave), enable applications like VR, UAVs, IoT, and autonomous vehicles,
oering multi-gigabit speeds and millisecond latency27,29,30. However, the growing demand for smart devices
and IoT is pushing 5G infrastructure to its limits31.
Research is now focused on 6G, which aims to enhance connectivity and security, supporting IoE applications
with data rates of up to 1Tb/s and latency under 1 ms. 6G will operate at frequencies that enable ultra-high-
speed transmission, reliable communication, and advanced integration, such as satellite networks and AI-driven
systems31–34.
is will further improve QoS, energy eciency, and bandwidth, supporting the exponential growth of IoT
devices and driving seamless interactions across interconnected networks35,36. To meet growing data demands,
6G expands into higher frequency bands like mmWave, terahertz (THz), and optical spectrums. While 5G uses
3GHz to 6GHz and mmWave (24GHz to 50GHz), 6G reaches THz and optical bands, boosting data rates by
100 to 1000 times. Regulators are now exploring frequencies like cmWave (3 to 30GHz) and mmWave (30 to
300GHz) to ease congestion37,38. e THz band (0.3 THz to 10 THz) bridges mmWave and infrared, with the
275GHz to 300GHz range overlapping mmWave37,39,40.
Cognitive radio and IoT networks
As 6G networks promise signicant advancements in speed, connectivity, and latency, they face the challenge
of spectrum scarcity due to the increasing number of connected IoT devices41, i.e., when massive IoT terminals
access the spectrum for mobile communications, serving tens of thousands of users, available spectrum resources
become limited, jeopardizing communication requirements for each terminal7,8. Cognitive Radio Networks
(CRNs) provide a solution by enhancing spectrum allocation and utilization for IoT, addressing the limited
availability of frequency resources13.
Currently, spectrum bands are underutilized by licensed holders (primary users, PUs)23, Cognitive radio (CR)
technology, through opportunistic spectrum access, can alleviate bandwidth scarcity and improve spectrum
eciency42. CR monitors available spectrum in real-time, allowing secondary users (SUs) to access idle bands
without interfering with PUs43. is enables spectrum sharing, with PUs retaining priority while SUs access
unused frequencies without causing harmful interference44,45.
Spectrum sharing requires CRs to detect underutilized bands, enabling dynamic access46. Spectrum sensing
identies available channels, oen referred to as spectrum holes, for CR access. When a PU signal is detected,
Paper Ye ar
Domain
Key contribution Issues addressed6G CR IoT BCT SA
Security
Jahid et al1. 2021 ✔⨯✔ ✔ ⨯
Proposes blockchain integration into 6G networks
and IoT to tackle 5G data challenges, emphasizing
decentralization and security crucial for IoT and
Industry 4.0.
Identies challenges like infrastructure sharing and
latency in blockchain, 6G, and IoT convergence, exploring
mitigation techniques for improved integration.
Pajooh et
al2.2022 ✔⨯✔ ✔ ⨯
Surveying the potential of integrating 6G IoT with
blockchain for improved security and decentralized
access control.
Examining IoT security, centralization limitations, and
challenges in integrating 6G IoT with blockchain.
Xu et al19. 2020 ✔⨯ ⨯ ✔⨯
Exploring blockchain’s potential for ecient resource
management and sharing in 6G networks to meet
emerging service requirements.
Address spectrum scarcity and optimize resource use in
IoT, D2D communication, network slicing, and blockchain
ecosystems.
Al-Dulaimi
et al20.2023 ⨯✔ ✔ ⨯ ⨯
Oers insights into cognitive radio’s role in addressing
spectrum scarcity, focusing on IoT contexts and dynamic
spectrum access strategies.
e study proposes cognitive radio as a solution for
spectrum scarcity, and explores advanced spectrum
sensing, including machine learning, to boost spectrum
eciency in IoT.
Khasawneh
et al21.2023 ⨯✔ ✔ ⨯ ⨯ Emphasizes CRN-IoT integration benets and presents a
state-of-the-art systems overview.
e study focuses on addressing wireless communication
and IoT application demands through CRN-IoT
integration.
Zainuddin
et al22.2023 ✔⨯✔ ✔ ⨯
Highlight blockchain’s enhancement of IoT network
security and traceability, showcasing its applicability in
supply chains, healthcare, and smart cities.
e study addresses challenges including scalability, energy
consumption, interoperability, and privacy concerns
associated with blockchain integration into IoT networks.
Bhaskar et
al23.2022 ✔ ✔ ✔ ⨯ ⨯
Discusses cognitive radio’s potential for IoT
communication, addressing wireless access issues like
collision and contention.
e study focuses on optimizing spectrum use and
ecient management through cognitive radio technology
in wireless access networks.
Mathew24 2021 ✔⨯✔ ✔ ⨯
examines the convergence of edge computing and
blockchain in 6G, with a focus on addressing security
challenges and obstacles.
e study highlights security and privacy challenges
in integrating edge computing and blockchain into 6G
networks
Rachakonda
et al25.2024 ✔ ✔ ✔ ⨯ ⨯ e research explores spectrum-sharing solutions to
optimize IoT connectivity amidst limited resources. Highlighting security concerns, the study evaluates
spectrum-sharing technology for IoT.
is Survey 2024 ✔ ✔ ✔ ✔ ✔
e study discusses advancements in blockchain-enabled
security for spectrum access in 6G cognitive radio IoT
networks, highlighting its potential to enhance security
and reviewing recent research.
It addresses security challenges in Cognitive Radio 6G IoT
Networks, showcasing blockchain’s potential in managing
spectrum, interference, and authentication.
Tab le 1. Comparative study of the existing surveys with our study survey.
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CR systems must transition smoothly from the occupied channel42,47,48. CR can operate under interweave,
underlay, or overlay paradigms. In underlay, SUs transmit at low power to avoid interference, while in overlay,
they generate orthogonal signals to coexist with PUs. In interweave, SUs access the spectrum only when PUs are
inactive49,50.
Dynamic spectrum access (DSA) allows exible use of licensed bands, enabling smart radios (cognitive
radios) to opportunistically share spectrum51. e growing number of IoT devices demands dynamic spectrum
strategies, as static allocation results in low utilization. DSA has been proposed as a method to improve spectrum
usability by exploiting spectrum holes and enhancing real-time network resource management42,52–54.
Spectrum access security
e integration of CR technology into 6G networks promises to enhance spectrum access eciency and improve
overall network performance. In 6G Cognitive Radio-Driven IoT Networks, dynamic and ecient spectrum
allocation is crucial for supporting a diverse range of devices and applications. As the demand for high-speed data
transfer increases and IoT devices proliferate, advanced spectrum access techniques become essential. However,
the dynamic nature of spectrum access also introduces new challenges, including security and privacy threats.
Protecting privacy is vital in CR networks, where data can be lost, stolen, or compromised. ese networks are
particularly sensitive to security threats due to inadequate cooperation between primary and secondary users.
Additionally, the reliance on sensing information and machine learning in CR systems can lead to errors if the
environment is misinterpreted, allowing malicious attacks to exploit these vulnerabilities with potentially long-
lasting eects55,56. Attacks and threats to spectrum access in 6G cognitive radio-based IoT networks will be
discussed in the next Section.
Blockchain technology overview
Blockchain technology has gained signicant adoption in academia and industry due to its eciency, particularly
for distributed applications in 6G and industrial IoT. It transforms traditional centralized systems with key
features like decentralization, immutability, transparency, and peer-to-peer communication1,57. Introduced by
Satoshi Nakamoto in 2009 with Bitcoin, blockchain creates a secure, trustless network for decentralized peer-
to-peer interactions58.
Blockchain operates as a decentralized network where nodes (personal computers) maintain and update
transaction data. It includes key components like blocks (data units), nodes (computers), and miners (validators),
ensuring reliability by continuously sharing data across the network59. Han et al.60 describe blockchain as a
protocol for recording transactions rather than a standalone technology. Unlike the Internet, blockchain allows
ownership transfer between parties.
As a distributed ledger, blockchain guarantees persistent, immutable, and consistent transaction records
through public key cryptography, hashing, and distributed consensus. Timestamped blocks are cryptographically
linked, creating an unalterable chain. Each node holds a copy of the ledger, ensuring a consistent view for all
participants2,61,62. Blockchain enables seamless data transfer while maintaining data integrity63. As illustrated in
Fig.1, Blocks are organized using a Merkle tree, with hash pointers linking them securely64.
e four main pillars of blockchain technology are consensus, distributed ledger, cryptography, and smart
contracts2. Consensus mechanisms (CM) ensure a clear transaction order and integrity across distributed nodes,
inuencing factors like throughput, latency, scalability, and security. Common consensus algorithms include
Practical Byzantine Fault Tolerance (PBFT), Proof of Work (PoW), and Proof of Stake (PoS), each tailored to
blockchain’s specic needs and performance criteria19,26. Blockchain records all transaction data in an immutable
shared ledger. Transactions are grouped into blocks, which are sequentially linked and added to the ledger at
regular intervals2,65.
Smart contracts are specialized codes stored on the blockchain that execute predened instructions
automatically, enhancing transaction eciency and enabling exible agreements. In 6G networks, they have
the potential to improve network management and user-operator agreements1,26,66. Cryptography secures the
blockchain, ensuring transaction integrity, user authentication, and condentiality. Blockchains can be public/
private or permissionless/permissioned, with varying levels of control over participation64,67,68.
Security features of the blockchain
Blockchain uses a decentralized ledger to track changes and transfers, ensuring data integrity, t rust, and resistance
to attacks70. Key security features that benet 6G networks include:
Decentralization
Blockchain’s distributed architecture minimizes reliance on centralized authorities, enhancing fairness and
security. Consensus protocols (e.g., PoW, PoS, BFT) validate transactions, ensuring system integrity and
trust70,71. PBFT, in particular, is considered highly suitable for energy internet applications72.
Immutability
Once recorded, blockchain data cannot be altered without majority node consent. Cryptographic hashing and
block linking make historical data resistant to changes during the mining process67.
Transparency
Transactions are transparent to all participants. Updates require consensus, ensuring agreement among
legitimate parties before any changes occur73.
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Anonymity
Blockchain provides a degree of anonymity using recipient IDs, though complete privacy isn’t guaranteed.
Each transaction block includes a unique cryptographic hash, linking it to previous blocks and preventing
modications67,70.
Cryptographic security
Blockchain employs cryptographic functions like SHA-256 to secure data, linking each block to its predecessor.
Altering a block requires recalculating subsequent hashes, making tampering computationally impractical71.
Traceability
Blockchain’s timestamp feature ensures data reliability through encryption and signatures. Detailed transaction
traceability improves transparency and allows for better monitoring in supply chains or nancial systems1.
Relevance to spectrum access security
Current spectrum access techniques rely on centralized control, leading to issues like bias, distrust, data exposure
risks, increased communication overhead, and challenges in interference management. Blockchain provides a
decentralized solution by automating resource sharing, implementing fair incentives for operators, facilitating
equitable resource trading, and ensuring unbiased spectrum access52. Integrating blockchain with cognitive
radio technologies enhances transparency and trust in spectrum allocation through decentralized security,
while CR optimizes spectrum utilization. For future 6G networks, ensuring the scalability and interoperability of
blockchain is crucial, requiring eective integration techniques with CR platforms and spectrum environments74.
Blockchain improves spectrum access security through cryptographic hashing and smart contracts,
safeguarding data and automating permission management. Decentralization and immutability prevent
tampering and eliminate single points of failure. Consensus mechanisms ensure data visibility and veriability,
creating persistent, auditable records for traceability. is paper examines the security challenges related
Fig. 1. Simplied blockchain structure69.
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to spectrum access in 6G networks employing cognitive radio for the Internet of ings, explores current
blockchain-based security solutions, and identies existing research gaps.
Challenges in spectrum access security in 6G cognitive IoT networks
In 6G CR-IoT networks, security against unauthorized interference and ensuring data integrity are critical
due to dynamic spectrum management and the growing number of connected devices. is section highlights
key challenges, including data privacy, interference management, and the need for scalable, resilient solutions.
Addressing these is vital for maintaining reliability and safety in 6G networks.
Spectrum sensing security
IoT applications rely on reliable, high-throughput connections, and spectrum sensing (SS) is essential for
opportunistic access in spectrum-scarce environments44. CR networks use SS to identify available spectrum
holes before transmission, helping manage underutilization75. Ensuring the integrity of spectrum sensing data is
crucial, but it remains vulnerable to attacks.
Nasser et al.76 explore how learning techniques improve spectrum sensing and address security challenges.
Furthermore, Aslam et al.55 discuss the vulnerabilities of CRs to data compromise, including potential destruction,
eavesdropping, or unauthorized alteration. ey also explore how attackers can disrupt transmissions from PUs
or manipulate spectrum sensing data to favor SUs in channel access. Attacks such as SSDF exemplify these
security challenges.
1) Spectrum Sensing Data Falsication (SSDF)refers to situations in which attackers inject false sensing data
into the network. is can result in inaccurate information regarding spectrum availability, prompting legit-
imate users to access occupied channels, which in turn causes interference and degrades service quality. Fur-
thermore, malicious IoT devices can deliberately mislead the fusion center (FC) with false sensing results,
leading to incorrect global decisions about the status of PUs77.
Authentication and access control
e application layer is crucial for securing IoT data through authentication, authorization, and restricting
spectrum access to authorized devices25. e diverse nature of IoT devices complicates authentication, and
unauthorized devices can cause interference and security breaches. Research focuses on improving these
mechanisms to secure IoT systems.
According to Al-Sudani et al.44, a collision-free media access control mechanism is proposed that distributes
channel sensing among users to enhance the performance of CR networks. Kokila et al.78 conducted a survey
analyzing the latest authentication and access control mechanisms in the IoT, emphasizing the critical need for
high levels of security, privacy, and resilience against attacks in this rapidly expanding domain. eir article
oers a comprehensive review of the security challenges associated with IoT implementations. Below are the
attacks most commonly linked to these security challenges.
1) Unauthorized AccessAttackers exploit weaknesses in access control to gain unauthorized entry to spectrum
resources, risking data and user security25.
2) Primary user emulation (PUE) attacksMalicious users mimic legitimate signals to mislead secondary users,
causing interference and unauthorized access to spectrum79,80. ese attacks degrade service quality, waste
bandwidth, and may lead to Denial of Service (DoS)81.
3) Replay Attackinvolves the retransmission of previously captured data16. ese attacks compromise authen-
tication by resending captured data to gain unauthorized access or manipulate controls. While they can
indirectly aect spectrum allocation and resilience against jamming, their primary impact is on the integrity
and security of authentication systems.
Privacy-preserving spectrum access
is involves strategies to protect IoT data by minimizing personal information collection while enabling
secure data sharing82. Privacy preservation involves safeguarding sensitive data transactions from unauthorized
malicious users (MUs) through various methods, including encryption, authentication, dierential privacy,
perturbation-based techniques, and blockchain technology80. During spectrum access involving sensitive
information, it is crucial to implement encryption and privacy-preserving techniques to secure communication
channels and prevent unauthorized data access.
Zainuddin et al.22 discuss privacy-preserving techniques using blockchain in 6G IoT networks. Vo et al.83
highlight AI-driven privacy threats in spectrum sharing for 6G. Below are the attacks most frequently associated
with these security challenges.
1) EavesdroppingEavesdropping involves covertly monitoring communications to extract sensitive data, a
heightened risk in IoT devices using shared spectrum. Attackers can intercept communications, compromising
privacy, especially in critical applications. Machine learning can help detect eavesdropping patterns, improving
IoT communication condentiality and mitigating threats in 6G networks25,34
2) Location privacy attacks involve attackers utilizing spectrum access data to ascertain the physical location
of users or devices, thereby raising signicant privacy concerns. For instance, geolocation tracking entails
analyzing spectrum usage patterns to estimate the locations of IoT devices, which can subsequently be exploited
for targeted attacks or invasions of privacy. In cognitive IoT networks, where spectrum resources are shared,
such attacks can result in considerable privacy and security challenges. Ahmed et al.84 provide an overview of
location privacy attacks and propose solutions to address the issues these attacks present in IoT environments.
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Jamming and interference resilience
Spectrum access in cognitive IoT networks is vulnerable to jamming and interference, where attackers
intentionally disrupt communication. Jamming involves emitting disruptive signals, while interference includes
any unwanted signals aecting communication.
1) Spectrum jamming attack: Involves transmitting noise into radio signals, disrupting communication
and degrading the signal-to-noise ratio (SNR)85. According to86, jammers that share the wireless spectrum can
be categorized into four main types: constant, deceptive, proactive/random, and reactive. Constant jammers
continuously disrupt all network packets by emitting random signals, which are easy to detect and trace.
Deceptive jammers transmit signals that mimic legitimate communications, making them traceable due to
their consistent patterns. Both of these types require signicant power. In contrast, proactive jammers alternate
between active and idle phases without synchronizing with CR transmissions, while reactive jammers are more
energy-ecient, activating only when they detect CR transmissions.
2) Coordinated co-channel interference attack: In this type of attack, malicious actors inundate the same
frequency channel used by legitimate communications, thereby disrupting network operations. Managing
interference in shared spectrum environments is challenging and necessitates strategies to minimize disruption
while ensuring secure access for authorized users. According to87, a coordinated attack may involve multiple
attackers conducting distributed scans to evade detection systems. is type of attack consists of two phases:
cyber and physical. e authors examine both phases and propose a method that eliminates the need for complex
manual rules or extensive prior knowledge to achieve eective results.
Dynamic spectrum allocation
Cognitive radio optimizes spectrum use by identifying and assigning unused frequency bands (“spectrum
holes”). is involves algorithms to maximize usage, minimize conicts, and maintain eciency76,88.
Dynamic spectrum allocation in cognitive radio networks involves the frequent reallocation of spectrum
to optimize usage, which can introduce vulnerabilities. Eective management requires regulating spectrum
use, controlling interference, and implementing robust security measures. However, the frequent changes in
spectrum availability can complicate the maintenance of secure access and pose challenges such as single points
of failure, malicious activities, and other security issues12,89. Here are some key attacks that may impact DSA:
1) Spectrum spoong attackAttackers create false signals to mislead cognitive radios, making them believe
certain spectrum bands are occupied or available, gaining unauthorized access or launching attacks90.
2) DoS attackIn this type of attack, the perpetrator seeks to prevent users from accessing network resources
and services84.DoS in CRNs occurs when all idle spectrum bands are compromised in a worst-case scenario
involving PUE attacks. is prevents SUs from locating available spectrum, rendering the entire CRN inca-
pable of serving any SUs81. In DSA, attacks primarily focus on DoS incidents. CR-based dynamic spectrum
allocation methods in balancing minimal interference with optimal spectrum utilization, particularly due to
malicious adversaries25.
3) Collusion attacksMultiple malicious SUs collaborate to enhance the reputation of nearby nodes for their own
benet91. Collusion attacks occur when multiple attackers or malicious IoT devices collaborate to deceive the
spectrum management system by providing false spectrum usage data. is undermines dynamic spectrum
allocation and compromises spectrum access, particularly if security mechanisms are insucient.
Each of these attacks can impair Dynamic Spectrum Allocation, leading to inecient spectrum utilization,
reduced network performance, and increased vulnerability to further attacks.
Secure spectrum database management
e CR is responsible for collecting the radio spectrum status from 6G networks and storing this information in
databases74. is process enhances spectrum management by safeguarding incumbent users in white spaces92.
Securing a database that contains spectrum information against unauthorized access and tampering is essential.
A breach of this database could lead to incorrect spectrum allocation and unauthorized access by IoT devices.
According to26, blockchain can function as a secure and decentralized database for spectrum management,
eectively recording all related activities. Spectrum database attacks target databases that manage spectrum
information, which is crucial for allocating and controlling radio frequencies. ese attacks can potentially
compromise the security, integrity, and functionality of spectrum management systems.
1) Injection attacksrefer to attempts to alter or insert harmful code or data into existing systems93. ese attacks
involve malicious actors injecting harmful code or commands into the spectrum database or its interfaces.
is category includes SQL Injection, which occurs when an attacker inserts malicious code into SQL state-
ments to gain unauthorized access to the database and manipulate or extract data94, Another type is Com-
mand Injection, in which malicious commands are injected into the spectrum management system to alter
its behavior. is can lead to unauthorized access, data modication, and disruptions in operations.
Trust and reputation management
Trust and Reputation Management (TRM) enhances security in networks, particularly in access networks.
Trust refers to one entity’s belief in another, while reputation represents the collective opinion within a network
community. TRM systems reward trustworthy behavior and penalize malicious actions, aiding in the detection
and mitigation of harmful nodes to improve data authenticity and foster network collaboration95.
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In decentralized environments, establishing trust among devices is challenging. Trust and reputation
management are essential for secure spectrum access. Trust management facilitates reliable interactions among
distributed nodes, while reputation management evaluates node credibility based on their behavior. ese
mechanisms optimize spectrum utilization, enhance network reliability, and mitigate security risks in dynamic
settings. Sybil attacks are one of the signicant challenges in this context.
Sybil attacksis type of attack is characterized by its ability to create fake nodes, referred to as Sybil nodes,
which disrupt the network and lead to various issues96. Sybil attacks involve attackers creating multiple fraud-
ulent identities or nodes to manipulate spectrum allocation and deceive the management system. is can
result in unauthorized spectrum access and potential interference with legitimate users.
Intrusion detection and prevention
In 6G Cognitive IoT networks, Intrusion Detection and Prevention Systems (IDPS) are crucial for identifying and
mitigating unauthorized access and malicious activities. ese intelligent and robust systems utilize advanced
techniques such as machine learning and articial intelligence to analyze vast amounts of data from IoT devices
and network trac. By employing behavioral analysis, IDPS can detect subtle indicators of malicious activity
that may evade traditional signature-based methods. ey also enable real-time threat detection and response,
streamline incident management, and coordinate actions across various security tools and teams.
In the dynamic and heterogeneous environments of 6G, IDPS must continuously adapt to emerging threats
and vulnerabilities, thereby enhancing overall network security by detecting anomalies, preventing attacks, and
ensuring data integrity and condentiality97,98. Below is an example of an attack that is related to this challenge.
1) Man-in-the-middle (MitM) attackis a type of cyberattack that enables an assailant to intercept and modify
network trac. is can result in the the of sensitive data or the initiation of attacks on other connected
devices90. By intercepting and manipulating communications between devices, an attacker can cause unau-
thorized data modications or disrupt services.
Quantum-safe security
As 6G networks integrate with cognitive IoT systems, they must prepare for the implications of quantum
computing, which could undermine traditional cryptographic methods such as RSA and Elliptic-Curve
Cryptography (ECC). Implementing quantum-safe cryptography, including new algorithms and adaptations
like enhanced AES key sizes and Quantum Key Distribution (QKD), is essential to safeguard against quantum
threats and ensure robust privacy and data integrity in the face of future quantum threats99.
1) Quantum-enabled cryptographic attacksese attacks could jeopardize 6G networks by undermining en-
cryption and other security measures that are currently considered secure, utilizing quantum computers90.
If quantum computers advance suciently, they could utilize Shor’s algorithm to compromise widely used
public-key cryptographic systems such as RSA or ECC. is implies that an attacker equipped with a quan-
tum computer could decrypt sensitive data encrypted with RSA or ECC, potentially exposing condential
communications and data within IoT devices and network infrastructure.
Figure2 summarizes the challenges and potential attack examples discussed above. To address the security
challenges in 6G cognitive radio–IoT networks, robust mechanisms such as secure spectrum access,
authentication, and encryption are essential. Ongoing research and development will be crucial for identifying
and mitigating emerging threats. Blockchain technology, along with other emerging technologies like articial
intelligence and secure hardware platforms, can signicantly enhance the security of spectrum access. e
following Section oers a comprehensive analysis of various blockchain-based solutions proposed in the
literature, detailing how blockchain can improve the security of spectrum access in these advanced networks.
Recent advancements in blockchain-based research for spectrum access security
As 6G Cognitive Radio IoT networks continue to evolve, addressing security challenges is essential for ensuring
reliable communication. ese challenges, each revealing unique vulnerabilities to various attack vectors,
highlight the necessity for robust security frameworks and protocols. Mitigating spectrum access attacks
requires the implementation of secure protocols, cryptographic techniques, intrusion detection systems, and
collaborative security measures. e integration of blockchain and Cognitive Radio technologies shows promise
for enhancing transparency and trust in spectrum allocation while facilitating intelligent spectrum utilization.
However, ensuring that blockchain systems are interoperable and scalable enough to meet the demands
of 6G mobile communication—particularly when integrated with Cognitive Radio for ecient spectrum
management—presents signicant challenges. Blockchain’s decentralized and tamper-resistant model oers
potential solutions, promising a more secure, transparent, and resilient framework for spectrum access. e
integration of IoT and blockchain aims to optimize distribution within IoT, thereby enhancing the eectiveness
of blockchain-based schemes in conjunction with 6G network architecture1,12,74.
is section oers a comprehensive analysis of nine blockchain-based solutions aimed at addressing security
challenges and attacks associated with spectrum access. It emphasizes recent advancements in blockchain
technology for secure spectrum sensing and access, while also identifying current research limitations.
Approaches for secure authentication and identity management
A recent study by Mughal et al.100 addresses the increasing interest in CRNs for spectrum sharing in IoT. It
proposes a tree-centric approach using a Centralized Base Station (CBS) to dynamically allocate channels to
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Secondary Users, optimizing utilization and reducing interference. Simulations show the Channel Dynamic
Control (CDC) mechanism provides channel access in one to two requests with an average delay of about 72
milliseconds.
e research conducted by Deepanramkumar et al.101 introduces BlockCRN-IoCV, which integrates
authentication, density-aware clustering, dual-agent spectrum access, and secure beamforming to enhance
security in the Internet of Connected Vehicles (IoCV). e authentication of primary and secondary users is
achieved through blockchain technology, utilizing the Hybrid Advanced Encryption Standard and Hyper-elliptic
Curve Cryptography (AES-HCC) algorithm. is process securely registers credentials such as ID, Physically
Unclonable Function (PUF), and location. e secret key, generated by the hybrid AES-HCC algorithm, ensures
the execution of authentication, eectively mitigating security threats posed by malicious secondary users.
Duraisamy et al.102 propose enhancing security in CRNs through the use of certicate linkable ring signature-
based blockchains (CLRSB), which utilize cryptographic keys to identify trustworthy users. ey implement a
blockchain structure that incorporates smart contracts and public ledger principles, integrating PUs and SUs to
Fig. 2. Taxonomy of spectrum access challenges and examples of potential attacks.
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improve spectrum sensing eciency. Additionally, they apply the mayy optimization algorithm (MFOA) and
an improved convolutional neural network (ICNN) in spectrum sensing to reduce interference between PUs
and SUs.
A paper by Sajid et al.103 proposes a blockchain-based method for detecting malicious users (MUs) in
networks. is method dierentiates MUs from legitimate users by utilizing cryptographic keys. If a user’s digital
signature is veried, they are classied as Authenticated Users (AUs); otherwise, they are categorized as MUs.
e process involves rst verifying the public key and then the private key, both of which contribute to consensus
on user validation. e eciency of this mechanism is evaluated through MATLAB simulations, indicating its
potential for authenticating participants in spectrum sensing processes within CRNs for IoT applications.
Venkatraman et al.104 propose a blockchain-based identity management system for computing assets within
an IoT ecosystem, which integrates devices, soware, users, and data operations. ey develop a proof-of-
concept prototype utilizing a federated and distributed blockchain platform with smart contracts, ensuring
secure authentication and reliable data storage for IoT resources. is implementation aims to authenticate
and authorize employee access to applications, systems, or networks by linking user rights and restrictions to
established identities, thereby addressing the growing number of IoT devices within the organization’s network.
e Paper by V et al.34 proposes the Authentication and Acknowledgment (AA) approach to evaluate
the eectiveness of a new 6G wireless security architecture. is architecture, which is based on secret key
authentication and exible position-based identication, establishes a foundation for assessing identity
management and authentication. e authors demonstrate the advantages of this architecture by analyzing
the Bit Error Rate (BER) in relation to the Signal-to-Noise Ratio (SINR) and measuring throughput across
various SINR values. Additionally, the paper discusses the limitations of the proposed architecture and oers
recommendations for enhancing security in 6G cellular networks.
Ghourab et al.105 propose a blockchain-based method for secure relay selection and spectrum access
in cognitive radio systems. eir approach utilizes virtual wallets to manage spectrum access, assesses relay
trustworthiness through a mathematical framework, and stores relay information on a blockchain. e system
classies relays based on cumulative intercept probability and digital signatures, thereby demonstrating enhanced
security, credibility, and integrity.
In summary, the reviewed works emphasize the use of blockchain and cryptographic techniques to enhance
authentication and identity management in CRNs and IoT systems. ese approaches improve spectrum
management, mitigate malicious threats, and ensure secure, scalable solutions for 6G networks. Together, they
highlight the critical role of secure authentication in next-generation wireless communications.
Figure3 representing the authentication and identity management general framework for the related work.
is diagram highlights the ow of processes starting from user registration and hybrid AES-HCC authentication,
leading to blockchain ledger management, malicious user detection, certicate linkable ring signatures, relay
trust assessment, and nally secure spectrum access. is framework shows how these components interact and
contribute to the overall system for enhanced security and spectrum access management.
Fig. 3. General framework for secure authentication and identity management101,102,104,105.
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Tamper-resistant spectrum sensing data
Research conducted by106 investigates the use of CR to enhance spectrum eciency in wireless multimedia
communications, with a particular focus on spectrum sensing that enables SUs to identify vacant frequency
bands. e study addresses the challenge posed by MUs who transmit false data, which degrades the performance
of Cooperative Spectrum Sensing (CSS) and contributes to congestion in licensed bands. e paper proposes a
blockchain-based CSS method to manage the spectrum and detect MUs, utilizing performance metrics such as
sensitivity, node selection, throughput, and energy eciency. is approach oers a more eective CSS solution
with MU suppression, demonstrating a 15% improvement in MU detection when 40% of users are malicious, as
indicated by simulation results.
Balakumar et al.107 focus on enhancing spectrum utilization in CR through blockchain-based spectrum
detection methods. ey evaluate the eectiveness of an energy sensor in detecting frequencies between 470MHz
and 790MHz using additive white Gaussian noise (AWGN). eir study nds that the probability of detection
(Pd) improves with an increased number of samples. Additionally, they introduce the M-ary Quadrature
Amplitude Modulation (QAM) technique to enhance performance, reducing false alarms and missed detections
by 5% during similar delay periods. e proposed method increases the likelihood of detecting a 3 dBm SNR for
64-QAM modulated signals by at least 15% compared to existing models, eectively addressing challenges such
as shadowing and fading.
Marriwala et al.108 tackle the issue of spectrum scarcity by proposing CR as a solution to improve spectrum
utilization. CR enables unlicensed secondary users to share the spectrum with licensed primary users without
causing interference. is paper presents two key approaches: the Node Evaluation and Selection (NES)
algorithm and a secure spectrum sensing mechanism. ese approaches leverage blockchain technology to
record user interactions and connection distances, thereby enhancing node selection and network security by
mitigating attacks and improving spectrum eciency.
erefore, Hu et al.17 propose a blockchain-based dynamic spectrum access (DSA) framework in which
secondary users conduct spectrum sensing and participate as miners and veriers within the blockchain network,
thereby eliminating the need for a central fusion point. Secondary users earn tokens for their contributions,
which they can use to bid for spectrum access. e system’s eciency is contingent upon its sensing, access,
and mining policies. Simulations indicate that while higher probabilities of sensing and mining enhance
transmission rates, they also increase energy consumption, highlighting the necessity for an optimal balance to
achieve maximum eciency.
e study conducted by Pari et al.9 integrates CRNs with the IoCV to address issues of spectrum scarcity
and communication reliability. ey propose a 6G CRN–IoCV approach along with a temporal-based logistic
regression algorithm (STLR) to minimize collisions at intersections. Spectrum utilization is enhanced through
spectrum sensing performed by SUs utilizing lightweight convolutional neural networks (Lite-CNNs), with
encrypted reports transmitted to a fusion center. Optimal routing is achieved using the Dingo Optimization
Algorithm (DOA) to increase throughput and packet delivery rates. Additionally, hybrid beamforming and a
multi-agent-based categorical Deep-Q Network (categorical DQN) are employed to improve communication
reliability and spectral eciency.
In brief, these studies explore blockchain and CR-based methods to enhance tamper-resistant spectrum
sensing. ey focus on detecting MUs, improving spectrum eciency, and ensuring secure spectrum access.
Techniques such as Cooperative Spectrum Sensing, energy detection, node selection, and machine learning
algorithms are employed to reduce false data and interference. Blockchain integration plays a key role in
enhancing security and performance, demonstrating signicant improvements in malicious user detection,
throughput, and communication reliability across various CRN and IoT applications.
Decentralized spectrum database
Rather than relying on a centralized spectrum database that may be vulnerable to attacks, a blockchain-based
decentralized ledger can be employed to manage spectrum information. is method renders the database
tamper-resistant and trustworthy, signicantly reducing the risk of data breaches. paper by62 examines the
application of blockchain technology in radio spectrum management, with a particular focus on dynamic
spectrum sharing applications. While blockchain technology has the potential to improve broader spectrum
management, it functions as a decentralized database that allows data owners to retain control, distinguishing it
from traditional cooperative approaches. Consequently, it is essential to explore how the database capabilities of
blockchain could enhance the eectiveness of various spectrum-sharing methods.
Kotobi et al. n.d109. propose a blockchain verication protocol designed to enhance spectrum sharing in
mobile cognitive radio networks and vehicular ad hoc networks (VANETs). By utilizing a virtual currency
called Specoins and a decentralized auction mechanism, the protocol facilitates transactions and maintains a
distributed database. Miner nodes earn Specoins for updating the blockchain, while non-miner CRs can lease
spectrum through various methods. is system improves both eciency and security, even in the presence of
severe fading conditions.
Chen et al.110 highlight that traditional centralized platforms for electromagnetic spectrum monitoring suer
from signicant data redundancy. ey propose utilizing propagation loss and signal direction-nding data to
enhance signal source estimation and introduce the Minimum Average Distance (MAD) method for improved
collaborative detection in CRNs. eir solution incorporates a blockchain-based Distributed Electromagnetic
Spectrum Database (BC-DSDB) and the Proof of High Condence (POHC) consensus mechanism to manage
and store data. Additionally, they introduce Spectrum Resource Currency (SRC) to prioritize secondary users
and manage spectrum allocation during collisions. is approach reduces data redundancy and facilitates more
eective spectrum access policies in distributed CRNs.
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ese approaches focus on using blockchain-based decentralized databases to enhance the security
and eciency of spectrum management. By replacing vulnerable centralized databases, blockchain creates
tamper-resistant and trustworthy systems for managing spectrum information. Techniques such as Specoins,
decentralized auctions, and the POHC consensus mechanism improve spectrum sharing and allocation in CRNs.
is decentralized approach also reduces data redundancy and strengthens the integrity of spectrum databases.
Smart contracts for spectrum allocation
Smart contracts, which are self-executing programs on the blockchain, can automate and enforce spectrum
allocation policies. is reduces the risk of unauthorized access and ensures compliance with established access
rules. Research by61 proposes a blockchain-based platform that utilizes a digital token to manage spectrum
access, track frequency usage, and prevent interference. Implemented on the Ethereum blockchain, this platform
supports spectrum sharing through smart contracts, enabling automatic transactions and license transfers,
which enhances both eciency and trust.
e Fig.4 provides a detailed representation of the blockchain-based spectrum management framework
proposed by Fan et al.53 to address spectrum utilization conicts in Cyber-Physical-Social Systems (CPSSs).
e framework consists of three planes: the Service Plane, Access Plane, and Transport Plane. e Service Plane
includes a blockchain unit for ensuring data privacy and authenticity, a smart contract unit for automating third-
party-free transactions, and a payment unit for managing users’ virtual wallets. is structure enables secure and
ecient spectrum transactions, optimizing spectrum management for edge computing.
Patel et al.111 propose Block6Tel, a blockchain-based scheme for secure and equitable 6G spectrum allocation.
is system employs a 6G protocol stack and a blockchain auction algorithm utilizing smart contracts to facilitate
transactions. Simulations demonstrate that Block6Tel outperforms traditional methods in terms of resource
utilization, request overhead, and fairness.
In summary, these studies demonstrate the signicant potential of smart contracts in automating and
securing spectrum allocation. e approaches outlined not only improve eciency and prevent interference
but also ensure fairness and compliance with established policies. From managing license-free spectrum in
CPSSs to optimizing 6G spectrum allocation, smart contracts are a promising solution for enhancing spectrum
management in next-generation networks.
Secure communication with smart contracts
Communication between IoT devices and the spectrum management system, including smart contracts, can be
secured through the use of blockchain technology. e integration of cryptographic techniques with blockchain
ensures the condentiality and integrity of communication channels, eectively preventing eavesdropping
and unauthorized access. Recent research by Muhammad Asad et al.70 propose the integration of blockchain
technology with private mobile networks to enhance next-generation train networks. By utilizing blockchain
Fig. 4. Blockchain-based spectrum management framework53.
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and smart contracts, their framework aims to improve security, privacy, and exibility. is approach facilitates
resource sharing and dynamic access, beneting Beyond 5G (B5G) and 6G networks while promoting a
decentralized future.
Rathod et al.89 explore the role of blockchain in enhancing security and reliability in 6G networks for
applications such as autonomous vehicles and smart cities. eir study integrates AI, blockchain technology, and
6G to prevent data integrity attacks. ey assess scalability and performance through the use of smart contracts
and the Interplanetary File System (IPFS).
Study by Femenias et al.112 propose a dynamic spectrum sharing (DSS) scheme for cell-free massive MIMO
networks, which enhances spectrum utilization. e scheme employs blockchain technology and smart contracts
to ensure transparent and secure spectrum transactions. Participants utilize blockchain addresses for trading,
while smart contracts manage the DSS protocol.
Raphaelle Akhras et al.113 propose utilizing an Ethereum-based blockchain and smart contracts to enhance
the security of smart grid communication, ensuring both authentication and secure data reporting for smart
meters. Simulations validate the security of this approach, but they also reveal challenges related to scalability
and cost.
To summarize, integrating blockchain technology with smart contracts oers a robust solution for securing
communication in IoT networks and beyond. ese approaches eectively ensure data integrity, condentiality,
and secure resource sharing in dynamic and decentralized systems. From enhancing security in B5G and 6G
networks to improving smart grid communications, the use of blockchain and smart contracts plays a crucial
role in safeguarding communication channels and facilitating secure, transparent interactions.
Consensus mechanisms for decision-making
Blockchain consensus mechanisms like PoW or PoS ensure collective agreement on spectrum access decisions,
thereby preventing manipulation by malicious actors. Recent paper by74 introduces Blockchain-based DSM for 6G
Networks (BSM-6G), a novel spectrum management model designed to address scalability and interoperability
challenges in 6G networks. Figure5 illustrates the BSM-6G framework, which integrates blockchain with CR
Fig. 5. BSM-6G spectrum management model for 6G networks74.
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systems using an Oracle Interoperability Design to enable seamless communication between the blockchain and
6G cognitive sensing data. e model incorporates the Proof-of-History (PoH) consensus protocol for improved
scalability and faster transaction validation. Additionally, the system features a Decentralized Application
(dApp), which directly interacts with both the blockchain and CR modules, allowing SUs and PUs to securely
access and share the available radio spectrum.
Hanwen Zhang et al.114 propose a novel distributed CBRS-Blockchain model to address the challenges
associated with the Citizens Broadband Radio Service (CBRS) in 6G networks. Traditional CBRS encounters
issues such as high administrative costs and privacy risks. e paper introduces a specialized consensus method
called proof-of-strategy, which integrates with spectrum allocation to provide a robust consensus mechanism
and mitigate the risk of single-point failures.
A recent study by Ameri et al.115 examines how AI can enhance blockchain technology, specically focusing
on consensus algorithms that ensure reliability in networks with untrusted nodes. e paper introduces a
“cognitive blockchain” that utilizes the Cellular Game of Life (CGL) model to develop a new consensus protocol.
is intelligent approach partitions nodes and veries blocks, thereby improving fault tolerance, scalability, and
performance while simultaneously reducing costs. Experimental results show the protocol eectively manages
faulty nodes and improves scalability and throughput.
Blockchain-based consensus mechanisms such as PoW, PoS, and specialized protocols like Proof-of-History
and Proof-of-Strategy play a critical role in securing spectrum access in 6G networks. ese approaches ensure
decentralized, transparent decision-making, eectively mitigating the risks of manipulation and enhancing
network scalability. Models like BSM-6G, CBRS-Blockchain, and cognitive blockchain optimize spectrum
allocation and management, addressing key challenges such as privacy concerns, interoperability, and fault
tolerance, thus improving the overall eciency and trustworthiness of spectrum management in next-generation
networks.
Immutable record of spectrum transactions
All spectrum transactions, including handos, can be recorded on the blockchain as immutable records. is
guarantees a transparent and auditable history of spectrum access, making it more dicult for attackers to alter
or tamper with previous transactions. Liang et al.116 investigate the integration of blockchain with Dynamic
Spectrum Access DSA to improve spectrum management by leveraging the decentralization and tamper-
resistance of blockchain technology. ey propose a reference architecture that includes an interference-based
consensus mechanism and a targeted validation system. Simulations demonstrate that these mechanisms enhance
system fairness and Signal-to-Interference-plus-Noise Ratio (SINR), suggesting that blockchain provides a more
secure and ecient solution for spectrum management.
Paper by Jain et al.117 propose a scheme to ensure the authenticity of Secondary Users by requiring them to
deposit a monetary stake, with penalties for non-compliance. is approach secures transactions and maintains
privacy in cognitive radio networks by utilizing blockchain-based smart contracts and a reputation system to
evaluate user trustworthiness.
In summary, the use of blockchain for recording spectrum transactions ensures the creation of immutable
and transparent records, which prevents tampering and enhances security. Research by Liang et al. and Jain et al.
demonstrates how blockchain can improve spectrum management, from enhancing system fairness and SINR
through decentralized consensus mechanisms to securing Secondary User transactions with smart contracts and
reputation systems. ese approaches oer reliable, tamper-resistant solutions for Dynamic Spectrum Access,
ensuring both eciency and trust in the spectrum sharing process.
Resilience against DoS attacks
Blockchain’s decentralized nature enhances resilience against denial-of-service attacks. Even if some nodes are
compromised, the network’s distributed structure ensures continued operation, thereby strengthening the overall
resilience of the spectrum access infrastructure. Xue et al.118 address the ineciencies of Nakamoto consensus in
spectrum management through their Spectrum Trading Blockchain (STBC) protocol, which enhances spectrum
utilization and minimizes transaction delays. e STBC protocol incorporates a novel consensus mechanism,
sharding for improved scalability, and privacy protection against DDoS attacks. It surpasses existing schemes by
increasing spectrum utilization by 30% and reducing transaction delays by a factor of 125.
Recent research by Dansana et al.119 proposes a blockchain-based security model for Cognitive Radio
Ad-hoc Networks (CRAHNs) aimed at enhancing attack resilience and QoS. e model employs a Mayy
Optimizer (MO) to eciently select and deploy both active and redundant miners for secure data storage,
thereby improving resource utilization and reducing costs. It eectively decreases communication delays by
18.5%, increases throughput by 19.5%, and enhances the Packet Delivery Ratio (PDR) by 19.4%, while also
conserving 12.5% in energy. Additionally, the model incorporates protections against DDoS attacks and ensures
high communication speed and eciency.
Khorseed et al.120 propose utilizing Hyperledger Fabric to improve DDoS attack detection in Soware-
Dened Networks (SDNs) by compiling victim IP addresses into a blacklist and employing blockchain
transactions to create a secure ledger. is approach enhances adaptability and exibility, reduces mitigation
times, and strengthens overall network security.
In conclusion, blockchain’s decentralized structure signicantly enhances resilience against DoS attacks by
ensuring continuous operation even when some nodes are compromised. Research by Xue et al., Dansana et al.,
and Khorseed et al. highlights various blockchain-based approaches that improve spectrum management, attack
resilience, and QoS. ese solutions, which include novel consensus mechanisms, sharding, and optimization
techniques, successfully reduce transaction delays, improve spectrum utilization, and protect against DDoS
attacks, strengthening the security and eciency of the spectrum access infrastructure.
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Privacy-preserving solutions
Blockchain can implement privacy-preserving features to keep sensitive spectrum access information
condential. Techniques such as zero-knowledge proofs and private transactions can be employed to protect
the privacy of IoT devices. Research by Vuppula et al.80 proposed a novel Blockchain-oriented Location Privacy-
preserving (BoLPP) framework for Cognitive Sensor Systems (CSS) in 6G networks. Figure6 illustrates the
BoLPP framework, which integrates blockchain with CRNs and employs energy detection techniques to
enhance privacy and resilience against malicious attacks targeting Secondary Users. is approach addresses
the challenge of maintaining privacy and security, outperforming existing methods like the Friend or Foe (FoF)
and Tidal Trust Algorithm (TTA) across several metrics, including response time, consistency, false alarm
probability, frame loss, network throughput, energy eciency, and security.
e Paper by Manogaran et al.121 discusses the emerging 6G communication environment, which aims to
deliver high throughput and low latency. Given the integration of diverse resources and standards, security
has become a signicant concern. e authors propose a blockchain-based integrated security measure (BISM)
to enhance access control and user privacy. is measure utilizes virtualized resource states and Q-learning
for dynamic access control, while privacy is preserved by optimizing service response longevity. Performance
is evaluated using metrics such as true positives, access denial ratio, access time, memory usage, and time
complexity. e BISM eectively enhances security by managing resource access and user privacy with minimal
time and memory costs, achieving a high success rate and reducing false positives.
Nguyen et al.122 address the security and privacy challenges in the expanding IoT sector by proposing a
privacy-preserving framework that combines Secure Ant Colony Optimization with Multi-Kernel Support
Vector Machine (ACOMKSVM) and the Elliptic-Curve Cryptography (ECC). is approach utilizes blockchain
technology to ensure data integrity and privacy during IoT data sharing. e system encrypts and records data
Fig. 6. BoLPP framework for location privacy in 6G CSS networks80.
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Security challenge
addressed Ref. Year Main ndings Methods used Limitations and/or future
directions
Secure authentication
and identity
management
100 2024
e proposed tree-centric approach in CRNs
improves spectrum allocation eciency, managing
authenticated SUs, and reducing channel access
requests to 1–2 with an average delay of about 72 ms.
e study employs a tree-centric approach,
where a CBS manages a tree of channels and
authenticates SUs for ecient allocation,
validated by extensive simulations.
e main challenge is throughput
loss due to PU movement. Future
work will improve sensing to better
track PU activity and optimize
channel allocation.
101 2022
integrating blockchain-based authentication with a
hybrid AES-HCC algorithm signicantly enhances
security in the BlockCRN-IoCV method. is
approach improves the accuracy of authenticating
both Primary and Secondary Users.
e study uses blockchain with AES-
HCC for user authentication, DADRC for
mobility, DA-TD3 for spectrum access, and
BiGRU-CapsNet for secure beamforming.
Performance is assessed through
OMNET + + and SUMO simulations.
Limitation: Poor
Spectral eciency.
Future directions include
implementing hybrid beamforming
to improve hardware, spectral, and
computational eciency.
102 2022
Integrating CLRSB with MFOA-ICNN boosts
security and eciency in CRNs by using
cryptographic keys to distinguish trustworthy users,
and reduces response time and frame loss by 18.13%
and 17.81%, respectively.
e study employs CLRSB for security
and MFOA-ICNN for improved spectrum
sensing.
e study points out security gaps,
scalability issues, and the need for
better optimization.
Future work should address these
problems and validate results in real-
world settings.
103 2020
e study nds that a blockchain-based method
using cryptographic keys eectively detects
malicious users in CRN for IoTs, improving
spectrum sensing accuracy and overall cognitive
radio performance.
blockchain-based approach to digital
signatures to authenticate users,
leveraging cryptographic keys for secure
communication. Simulations, assessed
through MATLAB simulations.
Did not explore the potential
computational overhead associated
with cryptographic operations.
104 2022
e study nds that a blockchain-based ID
management system improves security and privacy
for IoT ecosystems by using self-sovereign identity
and cryptography.
develops a proof-of-concept prototype using
a federated blockchain platform with smart
contracts to manage identities and secure
data in IoT environments.
Future work aims to improve
scalability and evaluate real-world
performance compared to emerging
solutions.
34 2024
e study shows that the new 6G security
architecture, improving position-based and exible
authentication, enhances security and performance
with a 94% better BER and 96% higher throughput.
e architecture is evaluated using Riverbed
Modeler 17.5 simulations, focusing on secret
key authentication and exible position-
based identication.
e study acknowledges the need to
address limitations in the proposed
security framework and suggests
further exploration to enhance 6G
network security.
105 2023
e study shows that combining blockchain with
a cross-layer method for relay selection enhances
security and trustworthiness in cognitive radio
systems by eectively distinguishing reputable from
non-reputable relays.
e approach uses blockchain for managing
relay trustworthiness, virtual wallets
for spectrum access, and algorithms for
classifying relays.
Future work should enhance relay
classication and blockchain
eciency, especially in dynamic
environments with eavesdroppers.
Tamper-resistant
spectrum sensing
data
106 2023 e blockchain-based cooperative spectrum sensing
(CSS) method enhances MU detection by 15% and
improves spectrum management and security.
e method uses blockchain for spectrum
sensing and MU identication, evaluated
with metrics like sensitivity and throughput
through MATLAB simulations.
Future work should address delays
caused by large numbers of cognitive
radios and improve real-time
security management.
107 2023
e study shows that energy detection with
collaborative spectrum sensing boosts spectrum
utilization, improving detection by 15% for 64-QAM
signals at 3 dBm SNR.
e eectiveness of energy detection
is evaluated using receiver operating
characteristic (ROC) curves under various
conditions, with simulations conducted in
MATLAB R2021b.
Future work should address noise
uncertainty, concealed nodes, and
the eects of fading and shadowing
on SNR.
108 2023
Cognitive radio networks can improve spectrum
utilization and security by allowing SUs to coexist
with PUs. e NES algorithm and secure spectrum
sensing enhance network eciency.
e study uses the NES algorithm and secure
spectrum sensing with blockchain to manage
user interactions and node performance.
e study needs to address
vulnerabilities related to wireless
media exposure and potential
attacks.
17 2021
e proposed blockchain-based DSA framework
improves spectrum management by decentralizing
the sensing and access process, reducing reliance
on a single point of failure, and rewarding SUs with
tokens.
e framework employs a time-slotted
protocol where secondary users act as both
sensing and mining nodes in a blockchain,
using a heuristic policy for participation and
bidding.
e system faces a trade-o
between energy consumption
and performance. Future research
should optimize sensing and mining
policies to improve energy eciency.
92022
e 6GCRN–IoCV approach improves spectrum
utilization, reduces collisions, and enhances
communication reliability in cognitive radio
networks integrated with IoCV.
Spectrum sensing with Lite-CNNs and
encrypted reporting. e study validates
its performance using SUMO and
OMNeT + + simulation tools.
Limitations: High sensing
delay = 20ms with 100 SUs, and high
acquisition delay
Decentralized
spectrum database
62 2019
Blockchain has the potential to improve dynamic
spectrum sharing in radio spectrum management
through its decentralized database, which allows data
owners to retain control.
e study compares blockchain to existing
spectrum management methods and
evaluates its benets for dynamic spectrum
sharing.
e paper does not provide detailed
case studies or address challenges
like scalability and integration issues
with blockchain.
109 2018 e article introduces a blockchain-based distributed
database for securing spectrum sharing in cognitive
radio networks, using Specoins for access.
e methods involve using blockchain for
Specoin transactions, verifying with private
keys, and comparing performance to the
Aloha protocol under dierent fading
conditions.
Future works will focus on the
eects of additional wireless channel
parameters and managing power
consumption with limited resources.
110 2022
e Blockchain-based Distributed Electromagnetic
Spectrum Database (BC-DSDB) reduces data
redundancy and enhances spectrum management
in CRNs.
Minimum Average Distance (MAD) method
and the BC-DSDB with Proof of High
Condence (POHC).
Scalability, security, and real-world
implementation challenges of the
BC-DSDB are not addressed.
Continued
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
Security challenge
addressed Ref. Year Main ndings Methods used Limitations and/or future
directions
Smart contracts for
spectrum allocation
61 2019
e research proposes a Blockchain-based
platform with a spectral token to manage dynamic
spectrum access, reduce interference, and ensure
compensation for primary users.
e platform uses Ethereum Blockchain
and smart contracts to manage spectrum
sharing and leasing, with a proof of concept
demonstrating its performance.
Future work will explore complex
leasing strategies and faster dynamic
spectrum access.
53 2020
A blockchain-based framework is proposed for
managing spectrum resources in CPSSs, addressing
spectrum scarcity and competition using edge
computing for non-real-time data.
e framework employs a multi-ring private
blockchain for spectrum mining and leasing,
with smart contracts to secure transactions
and rewards in virtual currency and
spectrum licenses.
e framework lacks real-time data
handling and consensus algorithm
considerations, with future work
focusing on spectrum auctions and
alternative algorithms.
111 2021
e research proposes Block6Tel, a blockchain-based
system for 6G spectrum allocation, to improve
fairness, reduce auction delays, and prevent collusive
bidding.
Block6Tel combines a 6G protocol stack
and blockchain-based auction with smart
contracts for resource allocation.
Scalability challenges are not
addressed.
Secure
communication with
smart contracts
70 2024 e article presents a blockchain-based framework
for secure spectrum sharing in NGTNs, enhancing
privacy and resource management.
e framework employs blockchain and
smart contracts, with a tokenization model
for privacy.
Future research should explore
scalability, spectrum pricing,
consensus algorithms, and
enhancing smart contracts.
89 2023
e research presents a case study on using
blockchain, AI, and 6G technology to enhance
security and data integrity in public safety
applications, addressing issues like data attacks and
privacy.
e study employs blockchain for security,
smart contracts for automation, and IPFS
for storage, and uses Google Colaboratory
and MATLAB for machine learning and
communication.
e study did not explore CR
technology or consider throughput.
Future work will aim to resolve
issues and enhance the performance
of blockchain-based 6G systems.
112 2024
e paper introduces a dynamic spectrum-sharing
scheme for cell-free massive MIMO networks,
utilizing blockchain and smart contracts to improve
spectrum allocation and transparency.
A Stackelberg game formulation is used
for bandwidth allocation and pricing, with
blockchain ensuring transparent and secure
transactions.
Future research will evaluate various
blockchain models to enhance
scalability, security, and eciency in
spectrum trading.
113 2020
e paper presents a blockchain-based
solution using Ethereum to secure smart grid
communications, ensuring privacy and data integrity
between smart meters and utilities.
e framework employs Ethereum and
smart contracts to validate smart meter
authenticity and secure data reporting.
Future research will address these
challenges, integrate renewable
energy sources, and test the solution
with real smart grid data.
Consensus
mechanisms for
decision-making
74 2024
e BSM-6G model integrates blockchain with
Cognitive Radio to improve dynamic spectrum
management in 6G networks, addressing issues of
transparency, interoperability, and scalability.
e model uses an interoperable blockchain
Oracle and the Proof-of-History (PoH)
consensus protocol for ecient spectrum
management.
Future work will enhance Oracle
automation, improve band
subdivision, and explore custom
blockchain networks for better
spectrum management.
114 2020
e CBRS-Blockchain model cuts cost, enhances
privacy with ring signatures, and improves reliability
with proof-of-strategy, avoiding single points of
failure.
e model and its performance are evaluated
through MATLAB simulations.
Future work should explore user
interactions, predict incumbent
behavior, and optimize spectrum use
for PAL users.
Immutable record of
spectrum transactions
116 2021
Blockchain-based spectrum management improves
security and eciency with its decentralized and
tamper-resistant design, optimizing transaction
eciency and reducing validation overhead.
e study compares blockchain with
traditional spectrum management, proposes
a new architecture, and tests an interference-
based consensus and validation mechanism
through simulations.
Further research should focus on
real-world implementation and
integrating blockchain with new
technologies.
117 2023
e scheme ensures transaction authenticity and
user participation in cognitive radio networks by
using money-locking and a reputation parameter to
penalize unreliable users while protecting reliable
ones.
Blockchain smart contracts secure
transactions, a money-locking scheme
penalizes failures, and a reputation
parameter manages user reliability.
Future work may use Federated
Learning to optimize penalty
thresholds and reputation
calculations, enhancing transaction
accuracy and adaptability.
Resilience against
DoS attacks
118 2022
e STBC protocol increases spectrum utilization
by 30%, speeds up transaction conrmation by
125x, and reduces energy consumption, while also
protecting against DDoS attacks and ensuring high
security.
e protocol features a new consensus
mechanism for faster transactions, uses
sharding for better eciency, and includes
temporarily anonymous transactions for
privacy and DDoS protection.
e STBC protocol handles only
spectrum transaction consensus,
lacks a full management-auction
system, and depends on strong
security assumptions that require
further improvement.
119 2024
e blockchain-based security model for CRAHNs
reduces delays by 18.5%, boosts throughput by
19.5%, improves PDR by 19.4%, saves 12.5% in
energy, and mitigates DDoS attacks.
e model uses blockchain and a Mayy
Optimizer for ecient miner selection and
secure verication, enhancing performance
and DDoS resistance in CRAHNs.
Future research should test the
model in larger networks and
integrate bioinspired consensus and
Q-learning to improve performance
and DDoS detection.
120 2024
e Hyperledger Fabric blockchain approach
improves SDN DDoS mitigation by reducing
response time and avoiding port blocking, enhancing
security and exibility.
e method uses Hyperledger Fabric to
detect DDoS attacks with entropy analysis
and maintain a victim IP blacklist on the
blockchain, tested across various topologies
and attack scenarios.
e main limitation is the high
computational cost. Future work
should optimize this, explore more
attacks for IDS, test other blockchain
platforms, and increase nodes for
better performance.
Continued
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on a distributed ledger, thereby protecting both data condentiality and the parameters of the ACOMKSVM
model. Performance tests conducted with datasets such as the Breast Cancer Wisconsin dataset and the heart
disease dataset demonstrate superior results compared to existing methods.
To summarize, blockchain technology provides eective privacy-preserving solutions for spectrum access,
ensuring the condentiality of sensitive IoT data through methods like zero-knowledge proofs and private
transactions. Research by Vuppula et al., Manogaran et al., and Nguyen et al. highlights blockchain-based
frameworks that strengthen privacy, security, and access control in 6G and IoT systems. ese approaches tackle
privacy challenges eectively, oering improvements in metrics like energy eciency, response time, and false
alarm rates, while securing data integrity and condentiality. e privacy-enhancing solutions presented show
how blockchain can address security issues and improve the reliability of spectrum management.
Table 2 summarizes recent blockchain-based solutions developed to tackle the security challenges and
attacks on spectrum access discussed above. It compares the various approaches based on the specic security
challenges they address and the methods they employ, while also highlighting their limitations and potential
future directions.
Future directions and research opportunities
By integrating blockchain technology into the security architecture of 6G Cognitive Radio IoT networks, it is
possible to create a more secure, transparent, and resilient framework for spectrum access, eectively addressing
many of the challenges and threats previously mentioned. However, it is important to recognize that blockchain
alone is not a cure-all; its implementation should be viewed as a component of a comprehensive security strategy.
Several potential challenges and future research directions still require attention, which are outlined as follows:
Advanced blockchain protocols for 6G networks
6G will surpass 5G by oering higher performance standards, including ultra-low latency, extremely high speeds,
and support for a wide range of applications32. As 6G networks evolve to accommodate unprecedented numbers
of devices and high-speed data transmission, particularly in cognitive IoT environments, blockchain protocols
must also advance. To eectively manage the complexities of spectrum access in these dynamic, high-density
6G networks, blockchain solutions need to be highly scalable and capable of handling surges in transaction
volumes and device interactions without compromising performance. is necessitates the optimization of
consensus algorithms to reduce latency and enhance throughput. Hybrid consensus algorithms can improve
both security and eciency by integrating elements from various mechanisms. For instance, combining PoW
with Delegated Proof of Stake (DPoS) enhances computational performance and security, with PoW responsible
for block creation and DPoS overseeing block validation. Similarly, integrating PoS with PoW increases both
security and decentralization. Additionally, combining DPoS with PBFT provides enhanced security, scalability,
and eciency123.
Additionally, Layer 2 scaling solutions, such as state channels and rollups, are essential for enhancing
transaction speeds and reducing latency. State channels enable o-chain transactions that are recorded on-chain
only, when necessary, while rollups consolidate multiple transactions into single batches, thereby increasing
Combination Purpose
Symmetric cryptography + QKD Secure key exchange for ecient data encryption.
Asymmetric cryptography + QKD Secure key management for digital signatures and authentication.
Symmetric cryptography + post-quantum cryptography Secure key management and data encryption against quantum threats.
Asymmetric cryptography + post-quantum cryptography Future-proof public key infrastructure and digital signatures.
Tab le 3. Cryptographic techniques and their security functions.
Security challenge
addressed Ref. Year Main ndings Methods used Limitations and/or future
directions
Privacy-preserving
solutions
80 2023
e BoLPP framework signicantly improves
security and privacy for secondary users in
Cooperative Spectrum Sensing for 6G networks,
showing better performance than existing methods.
e framework combines blockchain
with Cognitive Radio Networks and uses
energy detection, simulated in Python and
MATLAB.
Drawbacks of using POW, low
throughput.
Future directions include integration
with additional technologies and
analysis of new security threats.
121 2020
e blockchain-based integrated security measure
(BISM) enhances both access control and privacy
for 6G communication, achieving improved security
and service performance.
BISM uses blockchain for secure access and
privacy, with Q-learning for decisions, and
assesses performance with metrics like true
positives and access success.
Future research should focus
on testing BISM in practical 6G
scenarios, optimizing its eciency.
122 2020 e ACOMKSVM framework with ECC boosts IoT
data privacy and security by using blockchain for
secure data exchange and precise privacy protection.
e approach combines blockchain,
ACOMKSVM, and ECC to secure and
optimize IoT data sharing, tested on Breast
Cancer Wisconsin and Heart Disease
datasets.
Future work could extend the model
to support various machine learning
algorithms and improve privacy
across multiple encrypted datasets.
Tab le 2. Existing approaches in Blockchain-based Spectrum Access Security.
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throughput and minimizing delays124. ese advancements are essential for establishing blockchain as a viable
solution for managing real-time spectrum access in the complex and high-demand environment of 6G networks.
Integration with machine learning for enhanced security
Integrating machine learning (ML) classiers with blockchain technology presents a powerful strategy for
enhancing security and eciency in advanced networks such as 6G. is combination ensures the transparent
and immutable recording of threats, providing a decentralized platform for sharing threat intelligence
and executing automated responses, thereby strengthening the overall security infrastructure. By merging
blockchain’s transparency with ML’s predictive capabilities, researchers can improve real-time threat detection
and adaptive security measures.
Future research should prioritize the development of adaptive ML models that can adjust to evolving network
conditions, as well as federated learning (FL) approaches that safeguard data privacy while incorporating insights
into the blockchain for consensus and decision-making. Furthermore, ML can optimize blockchain consensus
mechanisms and smart contract execution, enhancing spectrum management by predicting and mitigating
threats and optimizing spectrum allocation. Employing ML and deep learning (DL) techniques—such as
Decision Trees, Random Forests, Support Vector Machines (SVM), Neural Networks, and K-Nearest Neighbors
(KNN)—in conjunction with blockchain can signicantly improve spectrum management and overall security
in cognitive radio networks. is integration also holds great promise for the IoT and 6G technology, oering
enhanced security, privacy, and eciency76,89,125.
Federated learning enables the training of models across decentralized devices while maintaining data
privacy. Meanwhile, blockchain technology ensures that these insights are securely integrated for consensus and
decision-making, making it especially eective in the vast landscape of the IoT126. Recent research demonstrates
the eectiveness of secure federated deep learning in detecting false data injection attacks by combining
Transformer-based detection, federated learning for collaborative training, and the Paillier cryptosystem to
preserve data privacy127
Advanced cryptographic techniques
Advanced cryptographic techniques are essential for securing blockchain-based spectrum management in 6G
cognitive radio IoT networks, where data integrity and privacy are paramount. As quantum computing poses a
threat to traditional cryptography, research into post-quantum cryptography is vital for developing quantum-
resistant algorithms.
Hybrid techniques that combine traditional cryptography with Quantum Key Distribution (QKD) or Post-
Quantum Cryptography (PQC) provide enhanced security. For instance, symmetric encryption methods such as
AES can be paired with QKD for secure key exchanges, while asymmetric methods like RSA or ECC combined
with QKD improve key management and digital signatures. Zero-knowledge proofs further safeguard privacy
by verifying transactions without revealing sensitive information. As 6G networks are expected to incorporate
quantum communication, new strategies will be necessary to address quantum-specic properties, including
QKD and quantum teleportation. Although QKD ensures secure key exchange, symmetric systems like AES are
more resistant to quantum attacks compared to asymmetric methods like RSA and ECC, which are vulnerable
to such threats128–130.
us, developing robust encryption methods and incorporating advanced security features such as
quantum-resistant cryptography and network slicing, will be crucial for ensuring comprehensive protection for
blockchain-based spectrum access in 6G cognitive IoT networks. As quantum computers become capable of
breaking conventional cryptographic algorithms like RSA, (EC)DSA, and (EC)DH within a short time frame131.
Integrating traditional cryptographic techniques with QKD or Post-Quantum Cryptography can signicantly
enhance the security of blockchain-based spectrum access systems. Each combination addresses specic aspects
of cryptographic security, providing a multi-layered defense against both current and future threats, as illustrated
in the Table3.
Addressing regulatory and standardization challenges
Integrating blockchain technology for spectrum access in 6G networks presents regulatory and standardization
challenges. Future research should align blockchain solutions with existing regulations and establish new
guidelines for these technologies. Collaboration with standardization organizations such as IEEE and ITU-T is
essential for creating interoperable protocols and security frameworks. e implementation of smart contracts
can automate compliance with spectrum access regulations. A signicant challenge is the lack of standardization,
which results in interoperability issues among various blockchain systems. erefore, developing comprehensive
blockchain standards is vital to address this issue132.
Investigating the impact of regulatory requirements on blockchain deployments and developing strategies for
compliance will facilitate broader adoption. is involves creating comprehensive regulatory frameworks that
balance innovation with security and privacy concerns, establishing industry standards and best practices, and
ensuring that blockchain solutions comply with both existing and emerging regulations12,25,129.
Standardization is essential for the advancement of 6G technology, with the European Telecommunications
Standards Institute (ETSI) striving to keep pace with rapid developments. As discussions surrounding 6G
progress even before the global adoption of 5G, updated standards are vital for ensuring seamless connectivity
between networks and devices. Standardization bodies are dedicated to establishing independent standards for
IoT and CRN, highlighting the necessity for cohesive and up-to-date regulatory and standardization eorts to
address these challenges32,50.
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Enhancing sustainability and energy eciency
As blockchain technology becomes integrated into 6G networks, addressing its environmental impact is a
signicant concern. To minimize the carbon footprint of blockchain operations, future research should focus on
developing energy-ecient algorithms and practices. For instance, transitioning from the energy-intensive PoW
consensus mechanism to the less energy-consuming PoS method can lead to substantial energy savings. Energy
eciency is crucial for sustainable mobile communication, particularly in 6G and IoT networks where billions
of devices are expected to connect and consume considerable amounts of energy133.
Exploring green consensus mechanisms, such as Proof-of-Authority (PoA) or PoS, which consume
signicantly less energy compared to PoW, is essential for reducing energy consumption in blockchain systems.
In 6G networks, adopting PoS or Delegated Proof-of-Stake (DPoS) can signicantly lower the overall energy
footprint, which is particularly important for IoT devices with limited power resources. Furthermore, integrating
blockchain with energy-ecient technologies and practices, such as lightweight blockchain protocols like IOTA’s
Tangle or Hedera Hashgraph, can optimize resource usage, meet the demands of 6G networks, and minimize
computational and storage requirements. ese advancements will enhance the sustainability of spectrum access
operations, aligning with the environmental goals of 6G networks134,135. Enhancing sustainability and energy
eciency in power systems can be further supported by incorporating advanced methods, such as spatio-
temporal graph wavelet convolutional neural networks. ese networks eectively detect and localize dummy
data injection attacks (DDIAs) even in the presence of incomplete topological information, thereby maintaining
grid stability and security136.
Implementation of cross-layer security approaches
To achieve comprehensive security in 6G cognitive IoT networks, it is essential to integrate blockchain technology
across multiple network layers. Future research should investigate how blockchain can enhance security not
only at the application layer but also at the physical, link, and network layers. is cross-layer integration could
facilitate end-to-end security solutions that address various attack vectors and strengthen overall spectrum
access security. For example, blockchain can safeguard physical layer signal processing from tampering and
eavesdropping while ensuring transparency and trust at higher network layers. Furthermore, eective cross-
layer security designs are critical for 6G, as they mitigate threats such as eavesdropping, DDoS attacks, and
man-in-the-middle attacks. By leveraging cross-layer information, these designs reduce overhead, increase fault
tolerance, and enhance power eciency, making them particularly well-suited for resource-constrained and
dynamic IoT networks137,138.
Real-world implementation and testing
Many blockchain-based solutions for 6G networks remain conceptual or experimental, posing challenges for
practical deployment. Future research should prioritize pilot projects and testbeds to assess these solutions in
real-world environments, documenting case studies and gathering data on performance, scalability, and security.
Implementing blockchain in 6G networks necessitates addressing high costs associated with hardware, soware,
and training, as well as ensuring compatibility with existing spectrum management and security systems.
Eective integration requires meticulous planning to guarantee compatibility and minimize disruption. Pilot
projects can help identify potential issues and demonstrate feasibility prior to full-scale deployment. Successful
integration will rely on collaboration among technology developers, network operators, and regulators to
leverage insights from pilot projects for broader implementations.
Conclusion
is paper provides a comprehensive exploration of how blockchain technology can signicantly enhance
security for spectrum access within the context of advanced 6G cognitive radio IoT networks. By synthesizing
research from recent years, it demonstrates how blockchain can address critical security challenges, such as
ecient spectrum management, interference mitigation, and robust authentication. e discussion highlights
blockchain’s potential through various solutions, including tamper-resistant spectrum sensing, decentralized
databases, and smart contracts for dynamic spectrum allocation.
However, the paper also acknowledges the substantial challenges involved in integrating blockchain with
6G cognitive radio IoT networks, particularly regarding interoperability, scalability, and the development of
comprehensive security protocols. It emphasizes that blockchain should be viewed as part of a broader security
strategy rather than a standalone solution. Future research directions are identied, including the development
of advanced blockchain protocols for 6G networks, integration with machine learning for enhanced security,
exploration of advanced cryptographic techniques, and addressing regulatory and standardization issues.
Overall, the paper underscores blockchain’s signicant potential to revolutionize spectrum access security while
recognizing the need for ongoing innovation and research to overcome existing challenges.
e datasets used and/or analyzed during the current study available from the corresponding author on
reasonable request.
Data availability
We do not analyse or generate any datasets, because our work proceeds within a review approach. Example from:
https://doi.org/10.1007/s11235-023-01079-1.
Received: 20 September 2024; Accepted: 2 December 2024
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Content courtesy of Springer Nature, terms of use apply. Rights reserved
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Author contributions
e rst author (Nassmah Al-Matari) is a Master student and this paper is a part of the Master thesis. e role
of Nassmah is mainly that she wrote the manuscript.e second author (Dr. Ammar Zahary) is the main su-
pervisor of the Master thesis. His role was guring out the ideas and methodology of the paper. He is the main
advisor of the thesis and consequently the manuscript.e third author (Dr. Asma Al-Shargabi) has performed
a valuable revision and proofreading of the paper.
Declarations
Competing interests
e authors declare no competing interests.
Additional information
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