Open Access

ARTICLE

Secure Rate Maximization for UAV-RIS-Aided IoT Network in Smart Grid

Jian Wu^*, Xiaowei Hao, Chao Han

Information and Communication Branch, State Grid Shanxi Electric Power Company Limited, Taiyuan, 030021, China

* Corresponding Author: Jian Wu. Email:

(This article belongs to the Special Issue: Innovations and Challenges in Smart Grid Technologies)

Energy Engineering 2026, 123(3), 11 https://doi.org/10.32604/ee.2025.071023

Received 29 July 2025; Accepted 23 September 2025; Issue published 27 February 2026

Abstract

Owing to the development of communication technologies and control systems, the integration of numerous Internet of Things (IoT) nodes into the power grid has become increasingly prevalent. These nodes are deployed to gather operational data from various distributed energy sources and monitor real-time energy consumption, thereby transforming the traditional power grid into a smart grid (SG). However, the openness of wireless communication channels introduces vulnerabilities, as it allows potential eavesdroppers to intercept sensitive information. This poses threats to the secure and efficient operation of the IoT-driven smart grid. To address these challenges, we propose a novel scenario that incorporates an Unmanned Aerial Vehicle (UAV) as a relay gateway for multiple authorized smart meters. This scenario is further enhanced by the integration of Reconfigurable Intelligent Surface (RIS) technology, which dynamically adjusts the direction of information transmission. Our objective is to maximize the secure rate within this UAV-RIS-aided system with multiple authorized smart meters and an eavesdropper based on physical layer security (PLS) techniques. We formulate the problem of secure rate maximization by jointly optimizing the active beamforming of the UAV, the passive beamforming of the RIS, and the UAV’s trajectory. To solve this complex optimization problem, we introduce the Twin Soft Actor-Critic (TSAC) algorithm. This algorithm employs a dual-agent framework, where Agent 1 focuses on optimizing the beamforming for both the UAV and the RIS, while Agent 2 concurrently searches for the optimal trajectory of the UAV. Simulation results demonstrate the TSAC algorithm significantly enhances the secure rate of the system, achieving faster convergence and higher rewards under the worst communication conditions. The TSAC algorithm consistently outperforms the Twin Deep Deterministic Policy Gradient (TDDPG) and Twin Delayed Deep Deterministic Policy Gradient (TTD3) algorithms. Furthermore, the TSAC algorithm exhibits robust performance when the distribution of smart meters follows a Gaussian distribution, further validating its practical applicability and effectiveness in real-world scenarios.

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Keywords

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