Formal reliability analysis of wireless sensor network data transport protocols using HOL
Bibliographic record
Abstract
In recent times, Wireless Sensor Networks (WSNs) have shown a great potential for monitoring physical or environmental conditions in a variety of safety and financial-critical applications, ranging from medicine to transportation and surveillance. Given the extreme conditions of most of the WSN environments, it is very important to make WSN communication resilient to network failures. Various data transport protocols have been proposed in the literature to serve this purpose. The reliability of these WSN data transport protocols is usually assessed by using Reliability Block Diagrams (RBDs). Traditionally, RBD-based reliability analyses of WSN data transport protocols is done using paper-and-pencil proofs or computer simulations, which cannot ascertain absolute correctness due to their inherent incompleteness. As a complementary approach, we propose to use the higher-order-logic theorem prover HOL to conduct the RBD-based reliability analysis of WSN data transport protocols. In particular, the paper provides a higher-order-logic formalization of series, parallel and parallel-series RBDs. These RBDs are then used to do the formal reliability analysis of the end-to-end (e2e) data transport mechanism, and the Event to Sink Reliable Transport (ESRT) and Reliable Multi-Segment Transport (RMST) data transport protocols.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".