INTEGRATING IOT AND ROBOTICS FOR AUTONOMOUS SIGNAL PROCESSING IN SMART ENVIRONMENT

Abstract

The integrated framework demonstrated here represents the convergence of IoT and robotics for autonomous signal processing in smart environments. The proposed system makes use of IoT sensors (Zigbee/LoRaWAN) for the collection of data, processes it with Kalman filtering for noise reduction, and applies Fourier/Wavelet Transforms for feature extraction. The features extracted by a Convolutional Neural Network (CNN) are analyzed for pattern and anomaly detection, along with optimal robotic path planning carried out utilizing Dijkstra's algorithm. The proposed pipeline is deployed via cloud platforms (Docker/Kubernetes) making it scalable and the execution. Experimental results indicate remarkable accuracy (98.2%) in signal analysis, very high efficiency in path planning (95% success rate), and robust throughput (15,000 ops/sec in serverless deployments). Thus, this work fills the existing gaps in end-to-end autonomous signal processing and creates a solution scalable to dynamic environments.