Multi-level clustering architecture and protocol designs for wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it