Reliable and Energy Efficient Transport Layer for Sensor Networks
Bibliographic record
Abstract
(DCDD), a reliable and energy efficient transport protocol for sensor networks. In DCDD, the sink uses a number of receivers— called “prongs”—that connect to it with reliable links. Sensors split observations into many fragments and generate parity fragments with an FEC algorithm. The fragments are then distributed over the paths and simultaneously sent to the sink. The sink can reconstruct the observations if it receives a portion of the fragments that is of the same size as their original observation. We use the ns-2 simulator to examine the ability of DCDD to increase end-to-end reliability, as well as the effect of DCDD on energy consumption in the network. Our simulations show that the network where DCDD is used outperforms the network in which the sensors use only MAC retransmissions to increase reliability. DCDD makes the energy use in the network more fair and at the same time it increases the end-to-end reliability in the network. DCDD also decreases the delay in the network. Index Terms — Sensor networks, directed diffusion, fault tolerance, energy efficient protocols I.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".