Learning-Based IoT Data Aggregation for Disaster Scenarios
Bibliographic record
Abstract
Industrial Internet of Everything (IIoE), as the deep integration of industry 6.0, the Internet of Things (IoT) and 6G mobile communication technology, pave the way for intelligent industry, enabling industrial optimization and automation. To ensure the high quality of services (QoS) in IIoE, tremendous real-time information generated by the pervasive smart things needs to be aggregated and processed quickly and reliably. However, a large-scale disaster could damage the entire communication network and cut off data aggregation such that Qos is compromised. In this paper, an Intelligent NIB based Data Aggregation Strategy, named (IDAS), is proposed for after disaster scenarios in IIoE. Specifically, IDAS first applies both iterative cubature kalman filter and radial basis function neural network to predict the data collection rates of survived infrastructures. Then, an energy efficient task distribution mechanism is design. Next, a deep reinforcement learning method is developed for the car-carrying NIB route design to perform corresponding task. Eventually, all data are aggregated toward the rescue headquarter by NIB deployment based on Fermat tree constructions. The theoretical analysis and simulations indicate that IDAS is not only energy efficient for after disaster scenarios but requires the least NIB consumption while compared with contemporary strategies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".