@article{4711,
  author = {Ketmaneechairat, H. Oothongsap, P. Mingkhwan, A},
  title = {A Multi-Layer Analytical Framework for Reliability Assessment of IoT-Blockchain Systems Using Sensor-Driven Transaction Modeling},
  journal = {Journal of Networking Technology},
  year = {2025},
  volume = {17},
  number = {2},
  doi = {https://doi.org/10.6025/jnt/2026/17/2/63-82},
  url = {https://www.dline.info/jnt/fulltext/v17n2/jntv17n2_2.pdf},
  abstract = {The integration of blockchain technology with Internet of Things (IoT) systems presents significant potential
for enhancing security, decentralization, and trust. However, this convergence introduces critical challenges
including high computational overhead, scalability limitations, latency constraints in 5G-enabled
environments, and the absence of unified frameworks that simultaneously address security, trust
management, and energy efficiency. This study proposes a multi layer analytical framework for assessing
reliability in IoT-blockchain systems using sensor-driven transaction modeling. The methodology integrates
statistical correlation analysis via Point-Biserial coefficients, probabilistic modeling through logistic
regression, and comprehensive reliability metrics including Transaction Success Rate (TSR) and Mean
Transactions to Failure (MTTF). Analysis of an enhanced IoT dataset augmented with blockchain transaction
attributes reveals that, while global linear relationships between sensor values and transaction outcomes
remain weak (|r| < 0.07), specific sensor types particularly temperature sensors demonstrate statistically
significant elevated failure rates (42.9%, OR = 1.54, p = 0.0087). Logistic regression identifies a critical
threshold beyond which failure probability increases substantially, while network and transaction graph
analyses highlight the importance of understanding interdependencies among system components. The
findings emphasize that transaction reliability is driven by localized, context specific factors rather than
broad linear relationships, necessitating targeted interventions including sensor specific calibration, oracle
optimization, and smart contract edge case handling. This framework provides actionable insights for
enhancing robustness in next-generation 5G-enabled IoT blockchain ecosystems.},
}