Energy efficient data survivability for WSNs via Decentralized Erasure Codes
Bibliographic record
Abstract
Designing reliability solutions for WSNs poses intricate challenges due to limitations in processing power and available energy. Such networks are often deployed in harsh and inaccessible environments and are therefore required to be highly reliable. However, reliability normally translates to redundancy in hardware and other resources implying both complexity and higher costs. In this study, we consider data survivability in WSNs. We present a data-centric framework based on Decentralized Erasure Codes (DEC) to increase the likelihood of data survivability in case of sensor nodes failure. The proposed framework enables network engineers to estimate the redundancy in hardware and data to achieve a given data survivability level. We also show two approaches to reduce the energy requirements of the proposed coding scheme using Random Linear Network Coding (RLNC). In addition to being decentralized, the proposed schemes are low in complexity requiring only binary coding over F2. We evaluate the performance of the proposed schemes by simulations and compare them to schemes with no network coding.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".