Multi-level clustering architecture and protocol designs for wireless sensor networks
Bibliographic record
Abstract
Wireless sensor network (WSN) consists of sensors for measuring and gathering data in a variety of environments. These sensors, with the size and battery constraints, usually have limited transmission ranges due to the low-power wireless radio transceivers. In a sensor network, sensed data should be collected at a centralized location, called sink, for processing and analysis. With limited transmssion distances, sensed data may require multiple relays to reach the sink. In this paper, a novel multi-level clustering (MLC) wireless sensor network design and its associated operating protocol will be presented. Energy optimization is always a critical factor in the designs and deployments of wireless sensor networks. The goal is to create an energy-efficient and effective routing protocol for the networks. Cluster creation in this paper is different from the well-known Low-Energy Adaptive Clustering Hierarchy (LEACH) design. Cluster-heads in our proposed design form a tree with a goal to reach all sensor nodes in a network. Subsequently, all sensed data in the tree can be delivered to the sink while LEACH can not offer this guarantee. Energy savings may be improved with different numbers of levels in the hierarchical clustering architecture. To validate the proposed design, thorough simulations have been carried out. Upon comparing to a multi-hop LEACH protocol, the proposed design offers consistent wider coverage area and longer life span of a wireless sensor network.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".