Max-SNR Opportunistic Routing for Large-Scale Energy Harvesting Sensor Networks
Bibliographic record
Abstract
Providing sensors with adequate energy in largescale wireless sensor networks (WSNs) over long periods is a major bottleneck in their implementation. In this regard, energy harvesting (EH), i.e., capturing energy from ambient renewable energy sources, is a promising solution for low-power and low data-rate WSNs. The randomness in the energy available forces the redesign of WSN protocols. Our specific interest here is to enable the delivery of sensed data to a fusion center (FC) in a large-scale EH-WSN. We propose a novel, energy-aware, opportunistic routing protocol in a large-scale EH-WSN requiring multi-hop communication. In choosing the best forwarding partner, our scheme considers the energy available at sensor nodes, their distances from the FC and also the amount of data to be transmitted. Our protocol requires no prior knowledge of the network topology. We provide a mathematical analysis of our routing protocol to confirm the achieved numerical results. As our results show, the proposed protocol significantly increases data delivery as compared to the state-of-the-art technologies. We also introduce an EH-aware manner for distributing sensors in the environment such that all nodes have approximately equal transmission load independent of their locations, which significantly increases the data delivery ratio.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".