Topology optimisation‐based distributed estimation in relay assisted wireless sensor networks
Bibliographic record
Abstract
This study studies a distributed estimation problem in relay assisted wireless sensor networks (WSNs). Different from most existing works, the network consists of two kinds of nodes, that is, sensor nodes (SNs) which is capable of sensing and computing and relay nodes (RNs), which is only capable of simple data aggregation. The problem of how to coordinate two kinds of nodes to facilitate distributed estimation is challenging because of their heterogeneous capability. The authors first develop a min‐weighted rigid graph‐based topology optimisation scheme to reduce the redundancy of communication links such that the energy consumption in the relay assisted WSN can be reduced. With the optimised topology, a consensus‐based estimation algorithm is proposed for SNs and RNs, respectively. The asymptotic unbiasedness and consistency of the estimation algorithm are analysed in the presence of measurement and communication noises. The proposed method is applied to estimate the distribution of slab temperature in the hot rolling process. It is demonstrated that the topology optimisation reduces communication energy consumption, while the deployment of RNs improves temperature estimation accuracy as compared to a homogeneous WSN with SNs only.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".