Implementation of Cross Layer Design with Localization Techniques in Wireless Sensor Networks Using Deep Learning

This study addresses the integration of wireless sensor networks, machine learning models, and go-layer architecture to embellish industrial processes in small-scale manufacturing. A strong monitoring infrastructure is produced by the incorporation of various sensors, proximity, temperature, pressure, vibration, UWB, and RFID. Cross-layer arrangement, along with localization methodologies, gives spatial context to sensor information, enhancing safety, predictive maintenance, and asset monitoring. K-Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), Artificial Neural Networks (ANN), and Decision Trees (DT) act as predictive models, exhibiting outstanding overall performance across accuracy, precision, keep in mind, and F1 Score criteria. Practical deployment in a small-scale firm, displaying CNC machines and forging stations, illustrates the machine's versatility. Results suggest the models' success in forecasting replies relying completely on dynamic sensor data, allowing actual-time selection-making. The system's usefulness supports enhanced safety measures, simpler maintenance processes, and optimal asset management. In addition to offering practical insights to organisations navigating the challenges of Industry 4.0, this research adds to the theoretical framework of move-layer layout and machine learning integration. The suggested system emerges as a complete solution for intelligent operational improvement and performance in modern day industrial settings.