Bibliographic record
Abstract
Energy and time consumption are the two most common challenges regarding communication optimization in the Wireless sensor network (WSN) routing algorithms. There are many different approaches for solving these two major issues in WSNs. These approaches optimize over time or energy separately and yet rarely take into account multiple criteria when choosing the next hop on the route. In order to address this open problem in WSNs, it is advantageous to combine multiple criteria during the next hop selection step. In this paper, we propose a new concept called Routing Ensembles. Our approach takes into account the individual selection criterion for the next hop using various multiple routing algorithms and applies a voting mechanism to decide on the next hop in the route collectively. We tested our routing ensemble with five well-known base routing methods, i.e., Greedy, NFP, MPoPR, MFR, and Compass and then selected the most voted node as the next hop. This process repeats until either the destination node is reached or the routing process fails. This concept was tested and validated using multiple simulated networks of varying sizes. The experimental analysis concluded that the routing ensembles allowed us to: (1) increase the delivery rate regardless of the network field size, (2) minimize both the energy and time consumption and (3) enhance the network performance only using traditional and directly routing algorithms.
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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 | 0.007 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".