Balancing area coverage in partitioned wireless sensor networks
Bibliographic record
Abstract
This paper deals with a resource sharing problem in wireless sensor networks (WSNs). The problem calls for identifying k data collection trees that can be managed by independent users to run applications requiring area coverage. The formalized problem, called k-balanced area coverage slices (k-BACS), calls for identifying an ensemble of k trees that share the sink node only (and no other node) in a given WSN. To avoid nodal congestion, each tree is required to satisfy constraints on the maximum degree of its nodes. The objective is to maximize the minimum total area covered by any tree in the ensemble. Existing results in the literature show that the k-BACS problem is NP-complete even if k =2. Thus, effective heuristic algorithms are needed. In this paper, we present and compare the performance of two efficient algorithms for solving the problem. Our results show that the devised algorithms produce well-balanced trees. In addition, the combined use of the computed partitions and the PEAS energy conservation protocol can produce competitive lifetime for networks where a prescribed level of area coverage is required for successful operation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".