Optimizing the Average Hop-Count and Node Distance Using an Adjusted DV-Hop Algorithm with a Distance Error Rate
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are attracting great interest from a large research community.The main function of these types of networks is their ability to collect physical data from a given environment such as temperature, humidity and light, etc.They are mainly designed for low-power embedded communications.One of the most felt drawbacks of sensor nodes is their inability to recognize their own positions if they are not equipped with a global positioning module (GPS).In this paper, we first implemented the traditional "Distance Vector Hop" localization algorithm, known by the acronym "DV-Hop", on the Cooja/Contiki platform, which served as a control sample.Subsequently, we developed a new contribution in which the unknown node estimated its distance to all anchors in the network, based only on locally available information.Our goal was to significantly reduce the distance gap between the actual and the estimated distance.The idea of the contribution was implemented on the Cooja/Contiki emulator, and was based on two techniques: 1-Calculating the distance error rate (Euclidean distance/SSRI distance).2-Converting the type of node once located into an anchor.Our simulation results show that the proposed DVA-Hop algorithm had a better accuracy than the native "DV-Hop"method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".