Enhancing MQTT Fault Tolerance and Resilience Against Broker Failures and DoS Attacks with Decentralized Blockchain Architecture
Bibliographic record
Abstract
The MQTT (Message Queuing Telemetry Transport) protocol has become the standard for IoT (Internet of Things) communication due to its lightweight nature and efficiency. However, its centralized architecture, with a single broker managing message flow, introduces vulnerabilities to failures and Denial of Service (DoS) attacks. This paper proposes a novel decentralized architecture for MQTT systems that leverages blockchain technology and smartcontracts to enhance resilience against broker failures and DoS attacks. Our approach utilizes a three-layer architecture comprising clients, cluster brokers, and a blockchain-based system to securely manage broker transitions and ensure continuous service. Key contributions of this work include a decentralized failover mechanism, secure recording and access control via blockchain, and a robust algorithm for improving system resilience. The use of blockchain and smartcontracts provides a tamper-proof and transparent method for managing broker transitions and access control, enhancing the security and trustworthiness of the system. These findings suggest that integrating blockchain technology with MQTT can mitigate some of the inherent vulnerabilities of traditional centralized architectures, paving the way for more resilient IoT communications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".