Energy efficient cellular automaton based algorithms for mobile wireless sensor networks
Bibliographic record
Abstract
We design new cellular automaton based algorithms to improve coverage in a network with mobile sensors. The algorithms can be useful in applications where sensors are initially deployed in one place and need to disperse to the environment autonomously, or in situations where in certain areas sensors may be destroyed (e.g. due to a natural disaster), and the sensors need to use their mobility in order to restore coverage. We propose a cellular automaton model that divides the neighborhood of a cell into four (North West, North East, South West and South East) quadrants and the sensors try to find out the directions where they can move to increase the coverage. We compared our model with a previous model for different initial configurations and have found that our model reaches a comparable coverage more quickly. Our algorithms use two parameter values to guide the movements of the sensors. Especially with the best choices of the parameter values, our algorithms require the sensors to make considerably fewer atomic movements than the earlier algorithm. For mobile sensor networks, energy consumption is largely determined by the amount of movement, and minimizing movement will increase the life time of the network.
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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".