Multi-objective Reliable multipath routing for wireless sensor networks
Bibliographic record
Abstract
Future WSNs are expected to carry different traffic such as voice and video as well as data to serve both real and non-real time applications. Therefore, the reliability and quality of the data delivered to support diverse applications is very important. Also, service differentiation is an essential component of QoS that should be supported by routing protocols. In this paper, we introduce a novel approach to reliability and multi-objective routing in WSNs. We present Multi-objective Reliable and Fault-Tolerant Multipath routing protocol (MRFTM) that estimates link quality before making a routing decision to provide a reliable transmission environment for data delivery. Simulation results show that MRFTM outperforms existing schemes with respect to the data delivery ratio where data can be delivered at high levels of reliability and assuring quality of services required by different applications. Using erasure coding to transmit data on the selected paths also enables very high reliability.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".