Open Access

ARTICLE

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

Mehrdad Shoeibi¹, Mohammad Mehdi Sharifi Nevisi², Sarvenaz Sadat Khatami³, Diego Martín^2,*, Sepehr Soltani⁴, Sina Aghakhani⁵

1 The WPI Business School, Worcester Polytechnic Institute, Worcester, MA 01609-2280, USA
2 Department of Computer Science, Escuela de Ingeniería Informática de Segovia, Universidad de Valladolid, Segovia, 40005, Spain
3 Department of Data Science Engineering, University of Houston, Houston, TX 77204, USA
4 Department of Industrial Engineering, College of Engineering, University of Houston, Houston, TX 77204, USA
5 Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA

* Corresponding Author: Diego Martín. Email:

Computers, Materials & Continua 2024, 81(2), 2819-2843. https://doi.org/10.32604/cmc.2024.056823

Received 31 July 2024; Accepted 29 September 2024; Issue published 18 November 2024

Abstract

In the evolving landscape of the smart grid (SG), the integration of non-organic multiple access (NOMA) technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management. However, the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages, especially when broadcasted from a neighborhood gateway (NG) to smart meters (SMs). This paper introduces a novel approach based on reinforcement learning (RL) to fortify the performance of secrecy. Motivated by the need for efficient and effective training of the fully connected layers in the RL network, we employ an improved chimp optimization algorithm (IChOA) to update the parameters of the RL. By integrating the IChOA into the training process, the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms. This can lead to improved performance in complex SG environments, where the agent must make decisions that enhance the security and efficiency of the network. We compared the performance of our proposed method (IChOA-RL) with several state-of-the-art machine learning (ML) algorithms, including recurrent neural network (RNN), long short-term memory (LSTM), K-nearest neighbors (KNN), support vector machine (SVM), improved crow search algorithm (I-CSA), and grey wolf optimizer (GWO). Extensive simulations demonstrate the efficacy of our approach compared to the related works, showcasing significant improvements in secrecy capacity rates under various network conditions. The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects, including the scalability of the NOMA communication system, accuracy, coefficient of determination (), root mean square error (RMSE), and convergence trend. For our dataset, the IChOA-RL architecture achieved coefficient of determination of 95.77% and accuracy of 97.41% in validation dataset. This was accompanied by the lowest RMSE (0.95), indicating very precise predictions with minimal error.

---

Keywords

---