Energy Prediction and Energy Management in Kinetic Energy-Harvesting Wireless Sensors Network for Industry 4.0
Bibliographic record
Abstract
Real-time control and monitoring are some of the main goals of Industry 4.0. To meet these requirements, sensors are needed at every step of the production process. Wireless sensors (WS) are better suited due to their flexibility but are limited in energy. In this work, kinetic energy harvesting using piezoelectric technologies are considered to ensure the energy autonomy of a Wireless Sensor Network (WSN). First, unlike most existing works, this paper focuses on WSN rather than a single WS since the control of the entirety of most industrial processes requires several WSs. The solution proposed here is based on deep learning of the harvestable power signals at each sensor deployed on the monitoring system. Specifically, vibration measurements were performed at 12 locations on an ore crushing mill in a mine. From there, a mechanical–electrical conversion model considering the system’s dynamics was set up to evaluate the power profile each of the WSs can harvest. Considering that the harvestable power has many peaks due to the different operating states of the engine, we first proposed a Predictor of the Harvestable Power from Vibrations (PHPV). Using a large database, compared to a state-of-the-art predictor, the Predictor of the Harvestable Energy from vibrations (PHEV) allows for significantly reducing the Root Mean Square Error (RMSE). More specifically, the lowest reduction achieved for RSME ranged from 9.4 μW (with PHEV) to 5.9 μW (with PHPV). A decrease in RMSE ranging from 18.45 to 4 μW was obtained for another measurement point. Since harvest rates differ from one location to another, a Hierarchical Energy-Balancing Protocol (HEBP) is proposed to maximize the number of WS capable of transmitting information about the system’s state, thus avoiding an interruption of the network coverage. In the HEBP, it is envisaged that some WSs, besides transmitting data, will supply other nodes with an energy deficit to allow them to communicate information about their location. For a minimum packet size of up to 1100 bits, the energy autonomy of all the WSs is ensured, unlike only 66% of the nodes with the previous protocols.
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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 | 0.000 | 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 | 0.000 | 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".