A Survey and Ontology of Blockchain Consensus Algorithms for Resource-Constrained IoT Systems

Area of Science:

• Computer Science
• Information Technology
• Cybersecurity

Background:

• Blockchain technology offers decentralization, security, and immutability, beneficial for Internet of Things (IoT) applications.
• Traditional consensus algorithms (e.g., Proof-of-Work, Proof-of-Stake, Byzantine Fault Tolerance) are computationally intensive and unsuitable for resource-restricted IoT environments.
• Existing surveys lack a systematic approach to classify consensus algorithms, hindering understanding of their IoT adaptability.

Purpose of the Study:

• To systematically classify and analyze blockchain consensus algorithms tailored for resource-constrained IoT systems.
• To develop an ontology for structuring knowledge on blockchain consensus mechanisms concerning IoT adaptability.
• To provide a comprehensive overview for researchers and practitioners in the field.

Main Methods:

• Developed a novel ontology for classifying consensus algorithms, divided into generic (CONB) and IoT-specific (CONIoT) categories.
• Conducted a systematic review and analysis of various consensus algorithms based on the developed ontology.
• Evaluated IoT compliance of major consensus algorithms based on design and implementation targets.

Main Results:

• The ontology provides a structured framework for understanding the relationships between different consensus algorithms and their suitability for IoT.
• Analysis revealed key challenges and trade-offs in adapting legacy consensus algorithms and designing new ones for IoT.
• Identified specific consensus algorithms and techniques that demonstrate higher IoT adaptability.

Conclusions:

• The developed ontology offers a robust tool for categorizing and analyzing blockchain consensus algorithms for IoT.
• The study highlights the need for specialized consensus mechanisms to overcome the limitations of traditional algorithms in IoT settings.
• Future research directions include exploring novel consensus approaches and optimizing existing ones for enhanced IoT performance and security.