Balancing Message Criticality and Timeliness in IoT Networks
Bibliographic record
Abstract
We study the problem of balancing timeliness and criticality when gathering data from multiple sources using a two-level hierarchical approach. The devices that generate the data transmit them to a local hub. A central decision maker then has to decide which local hubs to allocate bandwidth to and the local hubs have to prioritize the messages they transmit when given the opportunity to do so. Whereas an optimal policy does exist for this problem such a policy would require global knowledge of messages at each local hub, rendering such a scheme impractical. We propose a distributed reinforcement-learning-based approach that accounts for both the timeliness requirements and criticality of messages. We evaluate our solution using a criticality-weighted deadline miss ratio as the performance metric. The performance analysis is done by simulating the behavior of the proposed policy as well as that of several natural policies under a wide range of system conditions. The results show that the proposed policy outperforms all the other policies - except for the optimal but impractical policy - under the range of system conditions studied and that in many cases it performs close (3% to 12% lower performance depending on the condition) to the optimal policy.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".