A Meander Delay System Design Using Caps-Triple GAN Optimized with the Remora Optimization Algorithm for IOT Application

Abstract

In this manuscript, a Meander Delay System design using Caps-Triple GAN optimized with the Remora Optimization Algorithm (ROA) is proposed for the Internet of Things (IoT) application. For designing the Meander Microstrip Delay Line (MMDL) Antenna, the Capsule Triple generative adversarial network (Caps-Triple GAN) is used. The Caps-Triple GAN error and delay parameters are optimized using the ROA. Hence, the designed MMDL Antenna is available in the IoT application. The proposed method is executed in the commercial Sonnet software package. The performance of the proposed method is examined under performance metrics, such as delay time with resistance characteristic, one-step delay time resistance characteristic, delay time, one-step delay time, error, and speed. The prediction results of the proposed MMDL-CAPS-Triple GAN-ROA method predict lower delay times of 14.77%, 15.89%, and 11.50%, higher speed of 32.57%, 16.25%, and 8.44% compared with the existing methods, such as Prediction of the meander delay system parameters for internet-of-things devices utilizing a Pareto-optimal artificial neural network and multiple linear regression (MDS-ANN-MLR), Predicting the frequency characteristics of hybrid meander systems utilizing the feed-forward back propagation network (MDS-FFBPN), Frequency Characteristic Analysis of Meander Structures with Different Connecting Electrodes (MDS-MoM), respectively.