A Joint Fusion, Power Allocation and Delay Optimization Approach for Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we consider the cross-layer optimization of wireless sensor networks (WSNs) under the constraints of total energy consumption and transmission delay. A Gaussian WSN with time division multiple access (TDMA) media access control (MAC) layer protocol is used here. The sensed data are contaminated by sensor noise, before converting to digital bits by quantization. The digital bits are transmitted through a noisy wireless communication channel. In fusing the sensed information from individual sensors, a fusion rule is employed to recover the original data. Least square error (LSE) rule is considered here due to its low computation complexity. The mean distortion level is employed to measure the system performance. Since the battery life is limited and the data delay is crucial, we propose here to optimize the transmission power allocation strategy and the delay of each node in order to minimize the mean distortion of the measured information. Using computer simulations, the proposed adaptive approach is shown to provide an effective sensing in terms of performance and energy consumption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".