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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

Wenwen Xie, Geng Sun, Jiahui Li, Jiacheng Wang, Hongyang Du, Dusit Niyato, Octavia A. Dobre

TL;DR

The paper addresses the challenge of powering energy-constrained IoT devices by leveraging energy harvesting and GenAI to optimize network performance. It surveys fundamental GenAI models (GAN, diffusion, VAE, Transformer) and energy harvesting technologies (renewable natural sources and RF harvesting), presents the architecture of energy harvesting networks, and details how GenAI can improve channel estimation, relay design, antenna optimization, security, and renewable-generation forecasting. A UAV-enabled case study demonstrates a diffusion-model–augmented TD3 approach (DTD3) that improves AoI by optimizing UAV trajectory, scheduling, and charging time, illustrating the practical impact of GenAI for dynamic IoT deployments. The article also outlines open directions in distributed energy beamforming, inference management, and trade-off design, signaling significant opportunities for robust, autonomous, and energy-efficient IoT ecosystems.

Abstract

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAI) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAI in improving energy harvesting wireless networks for IoT in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting wireless networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting wireless networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data collection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some important open directions.

Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

TL;DR

The paper addresses the challenge of powering energy-constrained IoT devices by leveraging energy harvesting and GenAI to optimize network performance. It surveys fundamental GenAI models (GAN, diffusion, VAE, Transformer) and energy harvesting technologies (renewable natural sources and RF harvesting), presents the architecture of energy harvesting networks, and details how GenAI can improve channel estimation, relay design, antenna optimization, security, and renewable-generation forecasting. A UAV-enabled case study demonstrates a diffusion-model–augmented TD3 approach (DTD3) that improves AoI by optimizing UAV trajectory, scheduling, and charging time, illustrating the practical impact of GenAI for dynamic IoT deployments. The article also outlines open directions in distributed energy beamforming, inference management, and trade-off design, signaling significant opportunities for robust, autonomous, and energy-efficient IoT ecosystems.

Abstract

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAI) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAI in improving energy harvesting wireless networks for IoT in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting wireless networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting wireless networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data collection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some important open directions.
Paper Structure (34 sections, 3 figures)

This paper contains 34 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Overview of Energy Harvesting
  3. Renewable Natural Energy Harvesting
  4. Solar Energy
  5. Wind Energy
  6. Other Energy
  7. RF Energy Harvesting
  8. Energy Harvesting Architecture
  9. GenAI-enabled Energy Harvesting Network
  10. Overview of GenAI Models
  11. Generative Adversarial Network (GAN)
  12. Diffusion Model
  13. Variational Autoencoder (VAE)
  14. Transformer
  15. Reasons for GenAI on Energy Harvesting Optimization
  16. ...and 19 more sections

Figures (3)

  • Figure 1: Energy Harvesting Architecture. The information gateway is responsible for data transmission with the network devices. The energy transmitter is responsible for delivering energy to the network devices, where the energy sources include renewable natural energy and RF energy.
  • Figure 2: The framework of case study. The UAV is dispatched to charge a set of IoT devices and collect data from them to minimize AoI. Diffusion model is used to improve the actor network of TD3 algorithm to generate high quality decisions.
  • Figure 3: The AoI convergence of different algorithms.