Generating Random Graphs for Wireless Actuator Networks
Bibliographic record
Abstract
In this paper, we consider graphs created by actuators (people, robots, vehicles etc.) in sensor-actuator networks. Most simulation studies for wireless ad hoc and sensor networks use connected random unit disk graphs generated by placing nodes randomly and independently from each other. However, in real life networks are created by actuators in a cooperative manner. Usually certain restrictions are imposed during the placement of a new node in order to improve network connectivity and functionality. This article is an initial study on how connected actuator graphs (CAG) can be generated by fast algorithms and what kind of desirable characteristics can be achieved compared to completely random graphs, especially for sparse node densities. We describe several CAG generation schemes where the next node (actuator) position is selected based on the distribution of the nodes already placed. In our Minimum Degree Proximity algorithm (MIN-DPA), a new node is placed to be a neighbor of an existing node with the lowest degree (number of neighbors). In our Maximum Degree Proximity algorithm (MAX-DPA), a new node cannot be placed to increase the degree of any existing node over a pre-specified parameter limit. We show that these new algorithms are significantly faster than the well-known random unit graph generation scheme for sparse graphs. The graphs generated by these new schemes are not necessarily drawn from the same distribution as those generated by the independent node placement. Thus, we explore their properties by studying their average node degree and partition patterns.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".