A Novel Node Selection Algorithm for Collaborative Beamforming in Wireless Sensor Networks
Bibliographic record
Abstract
Collaborative beamforming (CB) provides an effective way of increasing the transmission range and improving the energy efficiency of collaborative sensor nodes in wireless sensor networks (WSNs). Although the mainlobe of the CB beampattern can maintain a stable amplitude, the sidelobes at unintended base stations (BSs) are significantly affected by the randomness of sensor node locations. To reduce the sidelobe power in the direction of unintended BSs, sidelobe control based on node selection needs to be performed. Yet, the computational complexity of optimal node selection is high due to the combinatory nature of the problem. Considering the limited energy supply and computational capabilities of sensor nodes in WSNs, the development low-complexity node selection algorithms is of paramount importance. In this paper, we propose a novel node selection algorithm to control sidelobe at a lower computational complexity. The algorithm consists of two phases which are developed for node selection and node exchange, respectively. The implementation of the proposed algorithm is also presented by detailing its message exchange processes. The performance of the proposed algorithm is evaluated by extensive simulations. Simulation results indicate that the proposed algorithm offers better performance and lower computational complexity in comparison with the existing node selection schemes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".