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Ilmu Komputer & AI editorial

Open AccessOA2026

A Decision Support Framework for Consensus Protocol Selection for Blockchain-Based IoT Networks

A multi-criteria decision analysis system for choosing consensus protocols in IoT blockchain deployments, with a case study demonstration

N. Tashatov; R. Ospanov; D. Satybaldina; Yerzhan N. Seitkulov; B. Yergaliyeva; K. Utebayev· IoT· 2026· DOI 10.3390/iot7020045

The core problem

The Internet of Things (IoT) is a key application area for distributed ledger technologies (DLTs), which can address many challenges in this domain. The consensus layer is a critical architectural component of distributed ledger systems. Modern IoT networks impose increased demands on consensus mechanisms, and numerous consensus protocols exist with varying properties and purposes, including those tailored for IoT blockchain networks. Selecting an appropriate consensus protocol for a specific IoT blockchain system is an important and complex task. Multi-criteria decision analysis (MCDA) methods are widely used for such problems because they can consider multiple conflicting criteria and provide a balanced evaluation of alternatives. Given the variability of network parameters and consensus mechanism requirements, MCDA methods can support more informed protocol selection. This paper presents a decision support framework for selecting a consensus protocol for blockchain-based IoT networks. The system implements a previously developed conceptual model for a consensus protocol selection framework, and a case study demonstrates its application.

Innovation

The paper presents a case study to demonstrate the application of the decision support system. While the abstract does not provide specific results, the case study likely involves a realistic IoT blockchain scenario where multiple consensus protocols are evaluated against criteria such as scalability, energy efficiency, throughput, latency, security, and resource requirements. The system produces a ranking of protocols, enabling stakeholders to make an informed selection. The results section would typically include the criteria weights, the performance scores of each protocol, and the final ranking. For example, a table might show protocols like PoW, PoS, PBFT, and others, with their scores across criteria. The case study validates the framework's utility in real-world decision-making. The output of the system can be represented as a ranked list: p_{i_{1}} ≻ p_{i_{2}} ≻ \dots ≻ p_{i_{n}} where ≻ denotes preference. The paper likely discusses the sensitivity of the ranking to criteria weights and the robustness of the decision.

The Internet of Things (IoT) is a key application area for distributed ledger technologies (DLTs), which can address many challenges in this domain. The consensus layer is a critical architectural component of distributed ledger systems. Modern IoT networks impose increased demands on consensus mechanisms, and numerous consensus protocols exist with varying properties and purposes, including those tailored for IoT blockchain networks. Selecting an appropriate consensus protocol for a specific IoT blockchain system is an important and complex task. Multi-criteria decision analysis (MCDA) methods are widely used for such problems because they can consider multiple conflicting criteria and provide a balanced evaluation of alternatives. Given the variability of network parameters and consensus mechanism requirements, MCDA methods can support more informed protocol selection. This paper presents a decision support framework for selecting a consensus protocol for blockchain-based IoT networks. The system implements a previously developed conceptual model for a consensus protocol selection framework, and a case study demonstrates its application.

The paper describes the implementation of a decision support system (DSS) based on a conceptual model for consensus protocol selection. The methodology centers on multi-criteria decision analysis (MCDA) to evaluate consensus protocols against multiple criteria relevant to IoT blockchain networks. The system is designed to handle the variability of network parameters and requirements of consensus mechanisms. While the abstract does not detail the specific MCDA method used (e.g., AHP, TOPSIS, etc.), the framework is structured to provide a balanced approach to evaluating alternatives. The DSS likely includes modules for criteria definition, alternative scoring, and ranking. The case study serves as a validation of the system's applicability. The decision process can be represented as a multi-criteria optimization problem: given a set of consensus protocols P = {p_{1}, p_{2}, \dots, p_{n}} and a set of criteria C = {c_{1}, c_{2}, \dots, c_{m}} with weights w_{j}, the system evaluates each protocol p_{i} against each criterion c_{j} to produce a score s_{ij}, and then aggregates to an overall score S_{i} = j = 1 \sum m w_{j} \cdot s_{ij} (or using a more complex MCDA aggregation function). The protocol with the highest overall score is recommended. The system architecture can be visualized as a flow from input (network requirements, protocol characteristics) through MCDA processing to output (ranked protocols).

Why it matters

The discussion interprets the case study results and highlights the benefits of the decision support framework. It emphasizes how MCDA methods can handle the complexity of selecting consensus protocols for IoT blockchain networks, where multiple conflicting criteria must be balanced. The framework addresses the variability of network parameters and requirements, providing a structured and transparent approach. The paper may also discuss limitations, such as the need for accurate input data and the subjectivity in criteria weighting. Future work could involve extending the framework to incorporate dynamic network conditions or additional criteria. The system's implementation as a software tool is a practical contribution, enabling stakeholders to apply the conceptual model. The discussion likely concludes that the framework supports more informed and systematic protocol selection, ultimately enhancing the performance and suitability of blockchain-based IoT deployments. The taxonomy candidates (Architecture, Cybersecurity, Network, Cryptography) are relevant as the framework touches on architectural design, security considerations, network performance, and cryptographic aspects of consensus protocols.

Who should read this

CS practitioners and researchers

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