Energy-efficient tree-based message ferrying routing schemes for wireless sensor networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are prone to partitioning due to limited energy in sensor nodes and unreliable radio communications between them. Message ferrying (MF) has been proposed as an effective means to deliver data between separated parts of a partitioned WSN. In this paper, we propose a tree-based MF algorithm (TMFA), in which minimum-weight spanning trees of each partition of the WSN are evaluated with different alternate root nodes. Appropriate choice of the weights allows overall energy consumption or delay to be minimized. Two kinds of tree-constructing algorithms respectively named least energy tree (LET) and minimum hop tree (MHT) are presented and evaluated by deriving an energy model. For comparison, minimum spanning tree (MST) at a single root node and one level tree (OLT) that maximizes the use of the MF are considered. Simulation experiments show that LET and MHT outperform both MST and OLT when we take the message ferrypsilas moving cost into account.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".