Trustworthy IoT Services With Blockchain and Information-Centric Networking: A Survey
Bibliographic record
Abstract
The exponential growth of Internet of Things (IoT)-generated content has given rise to efficient, secure, and scalable caching mechanisms to manage real-time data exchanges, driving the evolution of information-centric IoT architecture. However, centralized content distribution models introduce single points of failure, trust vulnerabilities, and scalability limitations, compromising the reliability and security of IoT services. Blockchain technology, with its decentralized, immutable, and tamper-resistant properties, offers a trustworthy caching framework, ensuring data authenticity, provenance, and secure access control, thereby ensuring trust in information-centric IoT. This paper presents a comprehensive survey of the literature on Blockchain-based information-centric IoT services, systematically analyzing trust frameworks, security models, and identity management solutions. We examine Blockchain and information-centric networking synergies in establishing trust, preserving privacy, enhancing attack resilience, and ensuring fairness in content delivery in IoT services. Additionally, we provide a detailed taxonomy of the literature on trustworthy information-centric IoT, considering key parameters such as data provenance, lightweight consensus mechanisms, identity management, decentralized trust models, and smart contract-based access control. The survey also investigates real-world implementations of trustworthy information-centric IoT solutions, highlighting deployment-level performance metrics and design considerations to evaluate their practical feasibility. Finally, we outline future research directions, emphasizing the need for scalable lightweight consensus protocols, adaptive caching strategies, and privacy-aware access control mechanisms to advance trustworthy IoT caching architectures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".