Adaptive Cluster-Based Data Collection in Sensor Networks with Direct Sink Access
Bibliographic record
Abstract
Recently wireless sensor networks featuring direct sink access have been studied as an efficient architecture to gather and process data for numerous applications. We focus on the joint effect of clustering and data correlation on the performance of such networks. We propose a novel cluster-based data collection scheme for sensor networks with direct sink access (CDC-DSA), and provide an analytical framework to evaluate its performance in terms of energy consumption, latency, and robustness. In our scheme, CHs use a low-overhead and simple medium access control (MAC) conceptually similar to ALOHA to contend for the reachback channel to the data sink. Since in our model data is collected periodically, the packet arrival is not modeled by a continuous random process and, therefore, our framework is based on transient analysis rather than a steady state analysis. Using random geometry tools, we study how the optimal average cluster size and energy savings vary in a response to various data correlation levels under the proposed MAC. Extensive simulations for various protocol parameters show that our analysis is fairly accurate for a wide range of parameters. Our results suggest that despite the tradeoff between energy consumption and latency, both of which can be substantially reduced by proper clustering design.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".