Dynamic sensor activation for maximizing network lifetime under coverage constraint
Bibliographic record
Abstract
Wireless sensor networks consist of a large number of sensors equipped with limited energy and processing capabilities. They are deployed in a field to measure physical variables or detect events. In critical surveillance applications, sensors are used to monitor a geographical area and usually a full, or high, area coverage is required as a Quality-of-Service (QoS) parameter. In dense networks, sensors detection ranges usually overlap. Therefore, only a covering subset of sensors can be turned on while other sensors are put in a very low-power Sleep state. In this paper, we address the problem of maximizing the sensor network lifetime under area coverage constraint. For that, we propose a mechanism that dynamically activates an optimal covering subset of sensors, based on residual energies. We first model this problem as an Integer Linear Programming (ILP) problem that we resolve using CPLEX. Then, we propose a greedy heuristic to tackle the exponentially-increasing processing times of the exact solution. We show that the proposed heuristic provides for acceptable solutions while having a polynomial O(N2) complexity, suitable for large-scale 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".