Bibliographic record
Abstract
The main purpose of a sensor network is information gathering and delivery. Therefore, the quantity and quality of the data delivered to the end-user is very important. The immense potential of wireless sensor networks (WSNs) has created a growing awareness of the need for reliability in such networks. A major concern in the design of WSN protocols, including those concerned with reliability, is energy efficiency. In this paper, we present a novel approach to reliability in WSNs. We introduce REAR (reliable energy aware routing), which is a distributed, on-demand, reactive routing protocol that is intended to provide a reliable transmission environment for data packet delivery. REAR introduces local node selection, path reservation and path request broadcasting delay to provide a reliable transmission environment to reduce retransmissions caused by unstable paths. The scheme efficiently utilizes the limited energy and available memory resources of sensor nodes. REAR attempts to take precaution against errors, instead of finding a solution after encountering the errors. Simulation experiments show that, by deploying an energy reservation scheme, REAR outperforms traditional schemes by establishing an energy-sufficient path from the sink to the source with special path request flooding, and also by distributing the traffic load more evenly in the network
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".