WLC12-4: Reliable and Energy Efficient Transport Layer for 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
We present diversity coded directed diffusion (DCDD), a reliable and energy efficient transport protocol for sensor networks. In DCDD, the sink uses a number of receivers- called ldquoprongsrdquo-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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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