Bibliographic record
Abstract
Data dissemination and node localization are key components of a Wireless Sensor Network (WSN), since the position of sensor nodes should be known so that applications can map events detected by those sensors. In this paper, we propose a joint localization algorithm that utilizes the position of a mobile sink, e.g., a vehicle, and of the neighbor nodes to estimate the position of nodes with no GPS modules. A data dissemination algorithm is proposed based on the well-known Greedy Geographic Forwarding (GGF) algorithm by combining the position of the neighbors and their remaining power when deciding where to send a packet to. The concept of bridges is also introduced, in which the sink compares its current position with previous positions and calculates whether there is a shortest path in order to create a bridge that will reduce the number of hops a packet has to travel through. According to our performance evaluation experiments, LOGR shows a slight improvement on network lifetime compared to GGF, while keeping similar delivery ratio performance. Including the power level of nodes in the forwarding decision equation tends to increase the path length to the sink. However, results show that bridges can minimize this increase by shortening the path.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.230 | 0.225 |
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".