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Towards Secure and Efficient Data Aggregation in Blockchain‐Driven IoT Environments: A Comprehensive and Systematic Study

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Xujun Tong
Xujun Tong
Xujun Tong
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Marzieh Hamzei
Marzieh Hamzei
Marzieh Hamzei
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Nima Jafari
Nima Jafari
Nima Jafari
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The rapid evolution of the Internet of Things (IoT) has revolutionized various sectors, fostering seamless intercommunication and real‐time monitoring. Central to this transformation is integrating blockchain technology, which ensures data integrity and security in IoT networks. This paper provides a meticulous exploration of data aggregation techniques within the context of blockchain‐based IoT systems. The study categorizes data aggregation algorithms into Privacy‐Preserving, Machine Learning‐Based, Hierarchical, Real‐Time, and Custom Aggregation Algorithms, each tailored to specific IoT requirements. Privacy‐Preserving Aggregation Algorithms focus on safeguarding sensitive data through encryption and secure protocols. Machine Learning‐Based Aggregation adapts dynamically to data patterns, offering predictive insights and real‐time adaptability. Hierarchical Aggregation organizes devices into a structured hierarchy, optimizing data processing. Real‐Time Aggregation processes data instantly, ensuring low latency for time‐sensitive applications. Custom Aggregation Algorithms are bespoke solutions tailored to unique application demands, emphasizing efficiency and security. Through a comparative analysis of these techniques, this paper explores their advantages, disadvantages, and applicability, addressing the challenges and suggesting future research directions. The integration of blockchain‐based data aggregation techniques not only enhances IoT network efficiency but also ensures the longevity and security of modern technological infrastructures. This study builds upon prior research in the field of IoT and blockchain technology by extending the exploration of data aggregation techniques and their implications for network efficiency and security. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, and strategies. The results indicate most of the articles were published in 2021 and 2022. Moreover, some important parameters such as privacy and security, latency, data processing, energy consumption, complexity, and reliability were involved in these investigations.
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Transactions on Emerging Telecommunications Technologies
SURVEY ARTICLE
Towards Secure and Efficient Data Aggregation in
Blockchain-Driven IoT Environments: A Comprehensive
and Systematic Study
Xujun Tong1| Marzieh Hamzei2| Nima Jafari3
1Public Basic College, Anhui Medical College, HeFei, China | 2Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran |
3Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin, Taiwan
Correspondence: Xujun Tong (tongxujun@ahyz.edu.cn) | Nima Jafari (jnnima@yuntech.edu.tw)
Received: 30 November 2023 | Revised: 28 October 2024 | Accepted: 17 December 2024
Funding: This study was supported by Anhui Province Key Teaching and Research Project: Computer Application Basic Course Ideological and Political
Demonstration Course, No. 2020szsfkc0506; Anhui Province Natural Research Project: “Design and Practical Application Research of Artificial Intelligence
Based Post Internship Management System” (Project No. ZR2021B002); Anhui Province Quality Engineering Project: “Integrated Practice Research of Major +
Course in Nursing (Xinjiang) Medical Computer Culture Basic Course from the Perspective of Curriculum Ideology and Politics” (Project No. 2022jyxm790),
Information Technology Provincial Course Ideological and Political Demonstration Course (Project No. 2022kcsz150), Provincial Public Virtual Simulation
Training Base (Project No. 2022xnfzjd012).
Keywords: blockchain | custom aggregation algorithms | data aggregation | data aggregation algorithm | hierarchical aggregation | Internet of Things
(IoT) | machine learning-based | privacy-preserving algorithms | real-time aggregation
ABSTRACT
The rapid evolution of the Internet of Things (IoT) has revolutionized various sectors, fostering seamless intercommunication
and real-time monitoring. Central to this transformation is integrating blockchain technology, which ensures data integrity
and security in IoT networks. This paper provides a meticulous exploration of data aggregation techniques within the con-
text of blockchain-based IoT systems. The study categorizes data aggregation algorithms into Privacy-Preserving, Machine
Learning-Based, Hierarchical, Real-Time, and Custom Aggregation Algorithms, each tailored to specific IoT requirements.
Privacy-Preserving Aggregation Algorithms focus on safeguarding sensitive data through encryption and secure protocols.
Machine Learning-Based Aggregation adapts dynamically to data patterns, offering predictive insights and real-time adaptability.
Hierarchical Aggregation organizes devices into a structured hierarchy, optimizing data processing. Real-Time Aggregation pro-
cesses data instantly, ensuring low latency for time-sensitive applications. Custom Aggregation Algorithms are bespoke solutions
tailored to unique application demands, emphasizing efficiency and security. Through a comparative analysis of these techniques,
this paper explores their advantages, disadvantages, and applicability, addressing the challenges and suggesting future research
directions. The integration of blockchain-based data aggregation techniques not only enhances IoT network efficiency but also
ensures the longevity and security of modern technological infrastructures. This study builds upon prior research in the field of
IoT and blockchain technology by extending the exploration of data aggregation techniques and their implications for network
efficiency and security. SLR method has been used to investigate each one in terms of influential properties such as the main idea,
advantages, disadvantages, and strategies. The results indicate most of the articles were published in 2021 and 2022. Moreover,
some important parameters such as privacy and security, latency, data processing, energy consumption, complexity, and reliability
were involved in these investigations.
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© 2025 John Wiley & Sons Ltd.
Transactions on Emerging Telecommunications Technologies, 2025; 36:e70061 1of22
https://doi.org/10.1002/ett.70061
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