Data Lifetime Estimation in a Multicast-Based CoAP Proxy
Bibliographic record
Abstract
In this work we consider kernel-based record lifetime estimation in a proactive Internet of Things (IoT) proxy with multicast based cache management. Multicast refreshment requests were based on lifetime expiration for a predefined number of records. To reduce the traffic volume in the IoT domain, we assume that only nodes where the observed physical variable has changed its value will respond to the multicast request. For estimating the data lifetime at the proxy, we use Gaussian kernels, assuming that the intrinsic data lifetime probability distribution was taken from Erlang-k family of sub-exponential distributions. In this setup, we consider that the proxy connects to the IoT domain using an IEEE 802.15.4-compatible wireless network. Results indicate that narrow and symmetrical lifetime probability distributions require more frequent multicasting refreshments compared to wider and asymmetric ones. This increases traffic intensity and energy consumption in IoT domain. We quantify finding with numerical results.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".