Link Loss Inference in Wireless Sensor Networks with Randomized Network Coding
Bibliographic record
Abstract
Due to fading and interference, data transmission via wireless links may sometimes be prone to error. For some applications in wireless sensor networks, it is of interest to monitor the link status and infer the packet loss rate. It has been shown that randomized network coding can improve the reliability of wireless sensor networks with lossy links. With network coding, the loss rate of a chosen path in a wireless sensor network is the maximum link loss rate among all the links in that path. This behavior changes the link identification problem and imposes challenges on the link loss inference. In this paper, we study the passive loss tomography problem in coded packet wireless sensor networks. We show that by inspecting the content of the coded packets at the sink (i.e., destination), one can estimate the path loss rates not only from the source nodes but also from various intermediate nodes to the sink. By utilizing such information at the sink, we determine the set of links whose loss rates can be identified. We propose a passive loss inference with random linear network coding (PLI-RLC) algorithm to estimate the link loss rates. Results show that in coded packet wireless sensor networks, our proposed algorithm can identify the status of a higher number of links compared to a Bayesian inference algorithm.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".