IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i12p460-d1538033.html
   My bibliography  Save this article

A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks

Author

Listed:
  • Mehrdad Shoeibi

    (The WPI Business School, Worcester Polytechnic Institute, Worcester, MA 01605, USA)

  • Anita Ershadi Oskouei

    (School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ 07030, USA)

  • Masoud Kaveh

    (Department of Information and Communication Engineering, Aalto University, 02150 Espoo, Finland)

Abstract

The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing the dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including the limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network’s achievable rate while satisfying IoT devices’ energy harvesting constraints. Our DRL framework leverages a novel six-dimensional chimp optimization algorithm (6DChOA) to fine-tune the hyper-parameters, ensuring efficient and adaptive learning. The proposed 6DChOA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating interference from direct and RIS-cascaded links. The simulation results demonstrate that our optimized RIS design significantly improves energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts.

Suggested Citation

  • Mehrdad Shoeibi & Anita Ershadi Oskouei & Masoud Kaveh, 2024. "A Novel Six-Dimensional Chimp Optimization Algorithm—Deep Reinforcement Learning-Based Optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks," Future Internet, MDPI, vol. 16(12), pages 1-24, December.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:460-:d:1538033
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/12/460/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/12/460/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaoshao Mu & Maxwell Fordjour Antwi-Afari, 2024. "The applications of Internet of Things (IoT) in industrial management: a science mapping review," International Journal of Production Research, Taylor & Francis Journals, vol. 62(5), pages 1928-1952, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:460-:d:1538033. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.