Cost‐effective design and evaluation of wireless sensor networks using topology‐planning methods in small‐world context
Bibliographic record
Abstract
Low‐power consumption and network resiliency are among the vital qualities for having a seamless, quality‐oriented wireless communication. Networks with small‐world property are known to possess both these favourable qualities. However, wireless networks are not inherently small‐world, neither is easy and cost‐effective to artificially create networks with this property by using the existing techniques. In other words, the traditional blind rewiring techniques that aimed at enhancing the network with such features, suffer from inefficiency and saturation behaviour. In this study, the authors propose topology‐planning methods that efficiently exploit the expensive long‐reach transmission facilities to add the small‐world property to the network. The authors show that these methods are practical, cost‐effective and efficient since they are appropriately tailored based upon the network realities, such as topology and channel fading. The proposed methods are tested for networks with diverse ranges of ‘clustering coefficient’ and ‘diameter’ in order to prove their aptitudes in dealing with real situations. The results illustrate that the incorporation of these techniques altogether decreases the network ‘diameter’ by almost 50% and the ‘average path length’ by 47%. This corresponds to 67% less facilities compared with blind rewiring techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".