International Journal of Research in Electrical, Electronics and Communication Engineering ISSN: 3107-8907 (Online)

Author: Kavita Mehta, Dr. Anil Desai.

Abstract: Wireless Sensor Networks (WSNs) are essential for various applications, including environmental monitoring, industrial automation, and smart cities. This paper examines the application of deep learning techniques to improve signal processing in WSNs. Deep learning algorithms, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are leveraged to enhance the accuracy and efficiency of signal processing tasks such as data aggregation, noise reduction, and anomaly detection. The research involves the development and training of deep learning models on large datasets collected from WSN deployments. The performance of these models is evaluated through extensive simulations and field tests, demonstrating significant improvements in signal processing accuracy and network performance.

Keywords: Wireless Sensor Networks, Deep Learning, Signal Processing, Convolutional Neural Networks, Anomaly Detection.