Score based reliable routing in wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it