TwinSnake: A ZTN-Orchestrated Architecture for Secure AIoT Model Training with Digital Twins and Bio- Inspired Snake Learning in Smart Cities
Bibliographic record
Abstract
Artificial Intelligence of Things (AIoT) systems are increasingly deployed in smart cities to enable automation, resource optimization, and real-time decision-making. How-ever, large-scale deployments face significant challenges, in-cluding device-level resource limitations, communication over-head, synchronization inefficiencies, and security threats such as data and model poisoning. To address these issues, a digi-tal twin-assisted collaborative learning framework is proposed. Resource-constrained devices are virtualized at home edge servers to offload computationally intensive training, while Multi- access Edge Computing (MEC) nodes equipped with Zero-Touch Networking (ZTN) autonomously orchestrate training policies. Snake learning is adopted to reduce synchronization delays and communication costs compared with federated and split learning, and Harris Hawks Optimization is applied to select participants based on trust, resources, and latency. Robustness against ad-versarial updates is ensured through a trust-weighted Adaptive Multi-Krum aggregation mechanism, while a permissioned blockchain provides tamper-proof auditability and accountability. Experimental results on a smart home intrusion detection dataset demonstrate a 50-65% reduction in communication, 30-45% reduction in computation and energy consumption, and Fl- scores above 95 % even under 40 % adversarial participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".