Energy-Efficient State Scheduling for Maximizing Sensor Network Lifetime under Coverage Constraint
Bibliographic record
Abstract
Wireless sensor networks consist of a large number of low-power, limited-processing capability sensors that are deployed in afield to measure physical variables or detect events like intrusion or movement. In surveillance applications, ensuring a high area coverage is an important issue requiring a certain number of sensors to be turned on to report events. In this paper, we address the problem of maximizing the sensor network lifetime under area coverage constraint. For that, we propose a scheduling mechanism that calculates, for every time slot of the network operating period, an optimal covering subset of sensors that will be activated while all other sensors will go on sleep. We model this problem as an integer linear programming (ILP) problem, that we resolve using ILOG CPLEX [9] and we show that the obtained solutions provide for a more balanced energy consumption when we increase a balancing exponent lambda, which increases network lifetime.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".