Score based reliable routing in wireless sensor networks
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. In this paper, we focus on designing a general energy efficient, fault tolerant, and highly reliable routing protocol that prolongs the network lifetime; we call it SBRR (Score Based Reliable Routing). As the main objectives of this protocol are the reduction of packet loss and packet error, we select the best quality path of the network for highly reliable data transfer. The routing decision is based on a heuristic parameter named ‘Path Score’, which is a combination of four factors. These factors are relevant to hop count, energy level of sensors, error rate of links, and free buffer size of sensors for each path. Also our algorithm utilizes a disjoint backup path for every source; as a result, this reduces the risk of data loss and delivery delay. Simulation results reveal that the proposed algorithm yields a longer network lifetime, less packet latency, and higher delivery ratio than other existing schemes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".