Optimized Event-Based PID Control for Energy-Efficient Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) face the critical challenge of reducing node energy consumption to extend network lifetimes in the presence of uncertainties and external disturbances.This paper proposes an event-based PID controller to minimize power consumption while maintaining required data rates in WSNs.Initially, the system is stabilized using a simple controller.Two structures are considered for this purpose: one based on state feedback control and the other utilizing a PID controller.The event-based PID controller is then developed to improve the WSN system's performance and reduce power consumption.The performance of the event-based PID-controlled system is evaluated for both structures.Results with the first structure show moderate improvements in settling time, overshoot, and error, with values of 9.78 sec, 0, and 9.715, respectively.Subsequently, significant enhancements are observed with the second structure of the event-based PID controller, incorporating a stabilizing PID controller.The results demonstrate a settling time of 0.0924 sec, overshoot of 3.58, and error of 0.01486.To further enhance the controlled system's performance, the Gray Wolf Optimization (GWO) method is applied to fine-tune the parameters (P, I, and D) of the implemented controllers.The application of the GWO algorithm maintains desirable characteristics, with settling time, overshoot, and error values of 7.7527 sec, 0.0358, and 6.4217, respectively.This research offers valuable insights for reducing power consumption and improving the performance of WSNs using an event-based PID controller and GWO optimization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".