Intent-Based Secure Fault Tolerance Model With Integrated AI for Edge-IoT Networks
Bibliographic record
Abstract
The development of real-time applications integrated with Intent-Based Networking (IBN) integrates an Internet of Things (IoT), providing interconnection between heterogeneous devices and physical objects for the formulation of smart cities. These systems provide seamless communication and maintain the adaptive network policies and infrastructure. Many existing schemes have proposed solutions for efficient routing with support from intelligent architectures; however, although most of them overlook the bounded and limited resources of IoT networks, they impose additional overhead while addressing unpredictable communications in IBN-IoT. Furthermore, security and trustworthiness are significant research challenges that must be addressed to prevent data breaches and allow only the use of authentic devices. This research presents a scalable model for an IBN-IoT environment that utilizes edge computing to enable trustworthy and fault-tolerant communication with energy efficiency. Firstly, Software-Defined Networking (SDN) is explored for load balancing and effective resource allocation in 6G Internet of Things (IoT) systems. Secondly, the proposed model explores artificial intelligence techniques to analyze the network environment and predict anomalies in the fault tolerance approach. Lastly, data is kept private and maintained in integrity using a private blockchain, providing a more reliable, distributed, autonomous system with minimal overhead. Using synthetic data, the proposed model is validated against QGA-ACO and MER-ODLADT solutions for energy consumption, anomaly detection, and time-to-failure metrics across dynamic scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".