Exploiting Concurrency for Efficient Dissemination in Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
Wireless sensor networks (WSNs) can be successfully applied in a wide range of applications. Efficient data dissemination is a fundamental service which enables many useful high-level functions such as parameter reconfiguration, network reprogramming, etc. Many current data dissemination protocols employ network coding techniques to deal with packet losses. The coding overhead, however, becomes a bottleneck in terms of dissemination delay. We exploit the concurrency potential of sensor nodes and propose MT-Deluge, a multithreaded design of a coding-based data dissemination protocol. By separating the coding and radio operations into two threads and carefully scheduling their executions, MT-Deluge shortens the dissemination delay effectively. An incremental decoding algorithm is employed to further improve MT-Deluge's performance. Experiments with 24 TelosB motes on four representative topologies show that MT-Deluge shortens the dissemination delay by 25.5-48.6 percent compared to a typical data dissemination protocol while keeping the merits of loss resilience.
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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 | 0.000 | 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