The advent of Wireless Power Transfer (WPT) technologies enhances the convenience and reliability of charging. Numerous studies have concentrated on addressing the energy utility maximization issue, often disregarding the different users’ sensitivity to charging delays. In response to this, we focus on the minimal charging delay (MAD) problem in this section to reduce charging delays. It is observed that the sensor’s received energy depends not only on the distance but also on the angle between the sensor and the charger’s orientation in a directional WPT. Consequently, we develop a practical energy transfer model, substantiated by experimental validation. To find the optimal solution, we cast the problem as a linear programming task. Additionally, we propose a technique for discretizing the charge power, which considerably limits the search space. A merging approach is also presented for more realistic application scenarios. Lastly, we show through simulations and experiments that our methods exceed the performance of the Set Cover baseline method by an average of 34.2%.

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Advanced Charging Models in WRSNs

  • Chi Lin,
  • Yu Sun,
  • Wei Yang

摘要

The advent of Wireless Power Transfer (WPT) technologies enhances the convenience and reliability of charging. Numerous studies have concentrated on addressing the energy utility maximization issue, often disregarding the different users’ sensitivity to charging delays. In response to this, we focus on the minimal charging delay (MAD) problem in this section to reduce charging delays. It is observed that the sensor’s received energy depends not only on the distance but also on the angle between the sensor and the charger’s orientation in a directional WPT. Consequently, we develop a practical energy transfer model, substantiated by experimental validation. To find the optimal solution, we cast the problem as a linear programming task. Additionally, we propose a technique for discretizing the charge power, which considerably limits the search space. A merging approach is also presented for more realistic application scenarios. Lastly, we show through simulations and experiments that our methods exceed the performance of the Set Cover baseline method by an average of 34.2%.