Energy-Efficient Path Construction for Data Gathering Using Mobile Data Collectors in Wireless Sensor Networks
Bibliographic record
Abstract
Energy is seen as a significant factor in wireless sensor networks (WSNs).It is a challenge to balance between battery lifetime of the different sensors and network lifetime.The main contribution of the proposed approach is to decrease the energy consumption of each sensor node, overcome unbalanced energy usage among sensor nodes, reduce the data gathering time and enhance the network lifetime.To achieve these goals, we combine the Hierarchical Agglomerative algorithm and an optimal path selection method.First, the suitable cluster heads (CHs) are elected based on the Euclidean distance and the residual energy of each sensor node.Then, the base station is situated at the center of the field, which will be partitioned into equal subareas, one for every mobile data collector (MDC).Second, the Kruskal algorithm is used to create an optimal data gathering path from each subset of elected cluster heads.Finally, each mobile data collector travels the optimal path to collect the data from the set of cluster heads of each subarea and returns periodically to the base station to upload gathered data.Computer simulation proves that the proposed approach outperforms existing ones in terms of data gathering time, residual energy and network lifetime.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".