Flow Sharing Reliability in Energy Harvesting Wireless Sensing Networks
Bibliographic record
Abstract
This paper introduces a new resource sharing problem in wireless sensor networks (WSNs) that employ energy harvesting for prolonged network uptime. The problem is on managing a given infrastructure of EH-WSNs by supporting concurrent applications. Each application is characterized by a set of traffic generating nodes, a sink node, and a minimum required traffic rate that should be periodically delivered to its sink node. The overall EH-WSN is modelled by a probabilistic graph where energy fluctuation over time in each node is described by a probability distribution and handled by adjusting the flow relaying capacity of a node. Performance of the obtained network management scheme is assessed by a reliability metric on the formulated probabilistic graph. We call the formulated problem the flow sharing reliability (FS-REL) problem in EH-WSNs. We present a heuristic algorithm to cope with the problem using ideas from minimum cost multi-commodity flows in networks and approximation of flow reliability using a factoring algorithm. We also present numerical results that give more insights into the problem and the proposed solution.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".