A gradient‐based multiple‐path routing protocol for low duty‐cycled wireless sensor networks
Bibliographic record
Abstract
ABSTRACT Routing in a low duty‐cycled wireless sensor network (WSN) has attracted much attention recently because of the challenge that low duty‐cycled sleep scheduling brings to the design of efficient distributed routing protocols for such networks. In a low duty‐cycled WSN, a big problem is how to design an efficient distributed routing protocol, which uses only local network state information while achieving low end‐to‐end (E2E) packet delivery delay and also high packet delivery efficiency. In this paper, we study low duty‐cycled WSNs wherein sensor nodes adopt pseudorandom sleep scheduling for energy saving. The objective of this paper is to design an efficient distributed routing protocol with low overhead. For this purpose, we design a simple but efficient hop‐by‐hop routing protocol, which integrates the ideas of multipath routing and gradient‐based routing for improved routing performance. We conduct extensive simulations, and the results demonstrate the high performance of the proposed protocol in terms of E2E packet delivery latency and packet delivery efficiency as compared with existing protocols. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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".