An Energy-Efficient and Load-Balancing Cluster-Based Routing Algorithm for CSMA-Based Wireless Sensor Networks
Bibliographic record
Abstract
This paper presents an Energy-efficient and Load- balancing Cluster-based (ELC) routing algorithm for CSMA-based wireless sensor networks. In particular, both distance and residual energy are taken into consideration in developing the cluster-head selection procedure. Furthermore, in addition to distance, cluster size is also used in formulating the cost function for cluster forming in order to balance load and energy consumption among the nodes, and hence, to enhance the network lifetime. Besides, ELC employs multi-hop inter-cluster routing based on a lowest-cost path approach that considers both energy efficiency and load balancing. Illustrative simulation results show that, for the same amount of delivered data, ELC consumes less energy and offers longer network lifetime as compared to other cluster-based routing algorithms such as LEACH-C and CBCDACP.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".