Localized Topology Control Algorithm with No Geometric Information for Ad Hoc Sensor Networks
Bibliographic record
Abstract
We present a localized algorithm (LTCA) for topology control in wireless sensor networks which has certain desirable properties. First, the algorithm is very simple, strictly local (requires 1-hop information of neighbors) and fast, requiring each node to communicate with its neighbors exactly twice. Second, LTCA does not assume the underlying graph to be a unit disk graph, and in fact works on general graphs. Third, the most important feature of LTCA is that it is completely independent of any location information of nodes in the network graph; it relies only on the connectivity information and the identities (ids) of the neighboring nodes. Assuming sensor nodes are deployed in the plane, it is shown that the resulting subgraph obtained by our algorithm is connected, symmetric and contains few edge crossings. We provide simulation results and show that on random graphs the resulting topology is spanner and the average degree of the subgraph is low. Due to the simplicity of the algorithm, LTCA can easily be implemented in practical ad hoc sensor networks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".