Clustering-Based Expanding-Ring Routing Protocol Applied in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) consists of hundreds of thousands of energy-limited sensor nodes that are densely deployed in a large geographical region. Once deployed, the replacement of the energy source of these sensor nodes is usually not feasible. Hence, energy efficiency is always a key design issue that needs to be enhanced in the WSNs to improve the life span of the entire network. Although each layer of the open system interconnect (OSI) reference model should be considered when exploiting the energy constrains in the WSN, in this paper we propose a routing protocol which focuses on the network layer, called clustering-based expanding-ring routing protocol (CBERRP). CBERRP is centralized controlled by the base station (BS) and utilizes the structure of not only clusters but also chains in the WSN to route and forward the sensed data to the BS. The performance of CBERRP is compared to analogous clustering-based schemes such as low-energy adaptive clustering hierarchy (LEACH) and LEACH-centralized (LEACH-C). Simulation results show that CBERRP presents a significant improvement over these two algorithms on the performance of overall energy consumption and the network lifetime by around 50% and 100%, respectively
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".