Adaptive Priority Scheduling of Internet of Things Data for Disaster Management in Smart Cities
Bibliographic record
Abstract
In the recent context of the emergence of smart cities, the massive amount of data generated by connected objects has led to unprecedented demands in terms of data transfer. The various constraints linked to their number, their characteristics, and their transmission are even greater and dim the effectiveness, in their regard, of traditional data planning schemes. As a result, the need to minimize the delivery time of urgent packets while reducing the average data delay, the difficulty in choosing and combining the appropriate criteria for classifying and prioritizing data, and the loss of packets are of continuing concern. In this paper, we propose an adaptive scheduling model based on multilevel priority packet classification, preemptive packet queuing with dynamic and adaptive reordering, contingency migration of packets in critical situations, and adaptive criticality-based selection of packet next-hop. We introduce two new parameters for scheduling decisions: the ratio of per-level deadlines reflecting the evolution of a packet in the network and the migration coefficient based on the experience of same-characteristic packets. Performance evaluation shows that the proposed model effectively prevents data loss and prioritizes the transfer of emergency data over a hierarchical wireless sensor network. Moreover, it guarantees the shortest delays for urgent data with an improvement of 31% and promotes fairness toward less urgent ones. The lowest delivery rate observed with the proposed method is 99.9%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".