Cost Based Optimal Data Sampling Rate in Wireless Sensor Network
Bibliographic record
Abstract
Data sampling rate is an important performance metric in sensor-based environmental monitoring and real-time applications. Optimal and reliable monitoring in sensor-based applications requires frequent data sampling. However, when data is transferred through wireless channels, as is the case in Wireless Sensor Networks (WSNs), increasing this rate may limit battery lifespan. This is because Radio Data Transmissions (RDTs) are the most important source of energy consumption in WSN nodes. In this paper, we compare the performance of several RDT reduction algorithms such as Redundant Data, K-Means, Autoregressive data reduction (AR), Autoregressive Integrated Data Gathering (ARIMA) and Adaptive Distributed Data Gathering (ADiDaG), varying the data sampling rate, and determine the best sampling rate that minimizes a cost function, another performance metric. We perform the simulations using the readings from 20,000 real smart meters from the City of Moncton's Water and Utility Department. Simulation results demonstrate that the ARIMA approach achieves the best sampling rate that minimizes the cost function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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 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".