Multiratecast in Wireless Fault Tolerant Sensor and Actuator Networks
Bibliographic record
Abstract
Abstract—We study the multicast problem in wireless sensor networks, where the source can send data to a fixed number of destinations (actuators) at a different rate (multiratecast). A typical motivation of such communication scheme is to enable fault tolerant monitoring applications where data is reported to more than one actuators using different rates that decrease with the sensors distance, so that if the closest actuator fails, others can take over from it. We propose two multiratecast routing protocols: Maximum Rate Multicast (MRM) and Optimal Rate Cost Multicast (ORCM), which are the first localized positionbased protocols specifically designed for this problem. The first, MRM, selects the next forwarding neighbor(s) in order to favor destinations requiring the highest rates, while the second, ORCM, evaluates several possible choices and select the best according to a cost over progress ratio criterion. The two protocols are compared by simulation, using a new metric that takes the rate into account when computing a multicast cost. Results show that ORCM provides a better routing performance in case of a small number of destinations, while MRM performs better for large numbers of destinations and has a lower computational cost. MRM also behaves better than ORCM when the variance among the rates becomes important. 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".