MS-ExTdO: mobile sink path planning based on extended Tasmanian devil optimization for wireless sensor networks
Bibliographic record
Abstract
Path planning in mobile sink is a critical aspect of Wireless Sensor Networks (WSN) to optimize data collection efficiency, energy consumption, and lifetime of network. This work introduces an energy-efficient protocol for Path planning in mobile sink based on extended Tasmanian devil optimization (MS-ExTdO). The proposed approach employs optimized node clustering and Voronoi-based node deployment to resolve issues related to coverage and node failures. The system model integrates the ExTdO algorithm for optimal clustering and mobile sink path planning. The algorithm considers fitness factors such as energy consumption, and distance during clustering and selects Cluster Heads based on remaining energy and degree of centrality. The proposed method acquired the throughput measured by MS-ExTdO is 99.974%, which is 6.16%, 4.73%, and 1.60% superior compared to MPSORP, MMSCM, and MACR methods. In addition, the proposed method demonstrates the superiority of MS-ExTdO in terms of minimal delay, highest residual energy, packet delivery ratio, and throughput.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".