Energy Analysis in Single Cluster WSNs with Power Control and In-network Data Compression
Bibliographic record
Abstract
Analytical expressions for the energy associated with data collection in Wireless Sensor Networks (WSNs) is an important tool when operating integrated sensing and transmitting devices constrained by limited battery life-time. This paper develops a new method of finding the energy dissipated by sensors due to (i) variable times associated with the transmission of in-network compressed data and (ii) transmit power adjusted to overcome deterministic attenuation of signals with distance in various radio propagation environments. Specifically, semi-analytic expressions are derived for energy associated with data aggregation and communication when sensor nodes send data to a single cluster head (sink) located at the origin and assuming that sensors are distributed within the cluster according to a two dimensional (2-D) Poisson point process. The compressed representations of the sensed phenomenon at various nodes in the system exploit spatial correlation among sensor measurements and, similarly as transmit powers, are dependent on the distances between nodes and the sink. The starting point for the analysis is the observation that node distances to the sink form a gamma distribution. By using this model, the dissipated energy is derived by finding the fractional moments of the gamma distribution. With sensors distributed randomly in a plane, close agreement is found between the calculated and simulated results for dissipated energies in different propagation and sensing conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".