Improving the Reliability of RPL Using Hybrid Deep Learning and Objective Function-Based DODAG Structure for AMI
Bibliographic record
Abstract
Advanced Metering Infrastructure (AMI) is the prime smart grid application that connects smart meters and electric power stations. Routing Protocol for Low-Power and Lossy Networks (RPL) is the most familiar lightweight routing protocol for AMI networks. The reliability of RPL routing is a potential problem for the efficient deployment of AMI networks. This paper ensures attack and network reliability for AMI-RPL and proposes Hybrid Deep Learning based Intrusion Detection System (HDL-IDS) for attack reliability and a Multi-Objective Function-based Reliable RPL (MOR-RPL) for network reliability. The HDL-IDS assures AMI-RPL reliability against attacks by analyzing and eliminating the attack traffic successfully. The MOR-RPL method improves the RPL construction reliability by procuring the multi-objective function-based reliability metrics in the DODAG building. The simulation results show that the attack and network reliability are significantly enhanced with balanced energy consumption.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".