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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".