Topology Control for Guaranteed Connectivity Provisioning in Heterogeneous Sensor Networks
Bibliographic record
Abstract
Topology control is important for heterogeneous sensor networks in order to minimize the total network power consumption under the constraint that all sensor nodes' connectivity requirements are satisfied. To address this issue, an optimization problem is first formulated, which is formally proved to be NP-hard. For practical applications, an effective solution, named topology adaptation algorithm (TAA), is proposed. TAA adopts both graph theory and maximum flow theory to find prespecified node disjoint paths with low time complexity and high network power efficiency. In order to further save the network power consumption, a judgment theory is proposed to remove any unnecessary long edges at the beginning without affecting network connectivity. Both theoretical and numeric results show that the proposed topology control algorithm can outperform counterparts in terms of the total network power consumption, the percentage of supernodes achieving k-connectivity, the average degree of nodes, and the average length of paths.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".