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

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.