An Adaptive Delay-Minimized Route Design for Wireless Sensor-Actuator Networks
Bibliographic record
Abstract
Wireless sensor-actuator networks (WSANs) have recently been suggested as an enhancement to the conventional sensor networks. The powerful and mobile actuators can patrol along different routes and communicate with the static sensor nodes. This work is motivated by applications in which the objective is to minimize the data collection time in a stochastic and dynamically changing sensing environment. This is a departure from the previous static and deterministic mobile element scheduling problems. In this paper, we propose PROUD, a probabilistic route design algorithm for wireless sensor-actuator networks. PROUD offers delay-minimized routes for actuators and adapts well to network dynamics and sensors with non-uniform weights. This is achieved through a probabilistic visiting scheme along pre-calculated routes. We present a distributed implementation for route calculation in PROUD and extend it to accommodate actuators with variable speeds. We also propose the Multi-Route Improvement and the Task Exchange algorithms for balancing load among actuators. Simulation results demonstrate that our algorithms can effectively reduce the overall data collection time in wireless sensor-actuator networks. It well adapts to network dynamics and evenly distributes the energy consumption of the actuators.
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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 | 0.002 | 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 | 0.001 |
| 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".