Ultrasonic Localization Detection using Manta Ray Foraging Optimization with Micro-Electro-Mechanical System

The recent growth of the Internet of Things (IoT) involves energy-efficient and accurate ultrasonic localization. However, the existing methodology has significant impact on the present models, and their defect detection speed is insufficient to fulfill the demands of an effective defect detection system in the industrial market today. Manta Ray Foraging Optimization with Micro—Electro--Mechanical System (MRFO-MEMS) provides as a solution of existing problems to increase the precision of ultrasonic localization detection. The MRFO is used to improve the effectiveness of algorithms for signal processing and the location of sensors. Then, MEMS is utilized to generate highly accurate sensors for precise ultrasonic evaluations. The model accuracy and efficiency in localizing ultrasonic signals are enhanced. The hybridization demonstrates how MRFO--MEMS was used to effectively increase location and detection accuracy, which is both practical and efficient. The findings showed that the proposed technique outperformed other models, including Bit Error Rate (BER), Massive MultipleInput Multiple--Output (MaMIMO), and Conditional Generative Adversarial Networks (CGAN) method, with an accuracy of 99.53%.