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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".