Routing-Aware Clustering Algorithms for Two-Tiered Sensor Networks
Bibliographic record
Abstract
In hierarchical two-tiered sensor networks, higher-powered relay nodes can be used as cluster heads for designing scalable sensor networks. It has been shown that, in such networks, the assignment of sensor nodes to clusters plays an important role in determining the lifetime of the network. In this paper, we have proposed two routing-aware, distributed algorithms for assigning sensor nodes to clusters in two-tiered networks. The first heuristic assumes that all relay nodes, acting as cluster heads, send their data directly to the base station. The second heuristic relaxes this assumption and is to be used with any network where each relay node uses a multihop route to send its data to the base station. Unlike conventional clustering algorithms, our approaches take into consideration the routing scheme used by the relay nodes, and attempt to balance the energy dissipation of the nodes. We have compared the results of our distributed approaches with the optimal solutions obtained using an integer linear program (ILP) formulation, as well as existing techniques, based on heuristics. The results indicate that our approaches, on average, can produce results that are close to the optimal solutions and consistently outperform existing heuristics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".