Loss inference in wireless sensor networks based on data aggregation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we consider the problem of inferring per node loss rates from passive end-to-end measurements in wireless sensor networks. Specifically, we consider the case of inferring loss rates during the aggregation of data from a collection of sensor nodes to a sink node. Previous work has studied the general problem of network inference, which considers the cases of inferring link-based metrics in wireline networks. We show how to adapt previous work on network inference so that loss rates in wireless sensor networks may be inferred as well. This includes (1) considering the per-node, instead of per-link, loss rates; and (2) taking into account the unique characteristics of wireless sensor networks. We formulate the problem as a Maximum-Likelihood Estimation (MLE) problem and show how it can be efficiently solved using the Expectation-Maximization (EM) algorithm. The results of the inference procedure may then be utilized in various ways to effectively streamline the data collection process. Finally, we validate our analysis through simulations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it