Multi-Level Clustering Architecture and Protocol Designs for Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor network (WSN) consists of a number of sensors, which measure and gather data in a variety of environments. In a WSN, sensed data are collected at a centralized location, called sink, for processing and analysis. With limited transmission ranges, sensed data may require multiple relays to reach the sink. In this thesis, a novel system design for multi-level clustering (MLC) WSNs and its associated protocol operations are proposed. Cluster-heads in the proposed design form a tree with a goal to reach all sensor nodes in the network. Subsequently, all sensed data in the tree are delivered to the sink. Energy savings is improved by exploiting sensor node redundancy in the WSN. To validate the proposed design, thorough simulations have been carried out. Upon comparing to the LEACH protocol, it offers consistent wider coverage area and longer life span of a WSN with proper settings of system parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".