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 extension to conventional sensor networks. The powerful and mobile actuators can patrol along different routes and communicate with the static sensor nodes. Obviously, it is crucial to optimize the routes for the actuators to collect the sensor data in a timely fashion. Given the nonuniform and time-varying distribution of sensors and events in large networks, the route design has to be dynamic and scalable as well as balance the loads of the actuators. In this paper, we propose probabilistic route design (PROUD), which is an effective and adaptive algorithm for weight-differentiated route calculation. PROUD constructs ana prioriroute that covers the sensor locations, following which, the actuators probabilistically and cyclically visit the sensor locations according to their weights. We show that this probabilistic approach adapts well to network dynamics without frequent recalculation of the whole route. It works for both small-scale sensor-actuator networks and large-scale sensor-actuator networks with partitioning. We further develop a distributed implementation of PROUD and extend it to accommodate actuators with variable speeds. Finally, we devise a multiroute improvement and a task-exchange algorithm that enable load balancing. Our performance evaluation shows that PROUD effectively reduces the overall data-collection time and evenly distributes the energy consumption across the actuators, as compared with other state-of-the-art solutions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".