Leveraging Deep Learning Algorithms for Alarm Detection Using IoT Sensor Networks

Abstract

In the realm of Internet of Things (IoT), alarm detection systems play a crucial role in identifying and responding to critical events. This paper explores the application of deep learning algorithms for enhancing alarm detection using IoT sensors. By leveraging advanced deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the research proposes a robust framework for analyzing sensor data and detecting alarms with high accuracy and minimal latency. The framework integrates multiple IoT sensors, processes their data using deep learning models, and generates real-time alerts for various applications, including security, industrial monitoring, and environmental sensing. The paper evaluates the performance of different deep learning architectures in terms of detection accuracy, response time, and scalability. Results demonstrate the effectiveness of deep learning in improving alarm detection reliability and efficiency, offering a significant advancement over traditional methods. The research highlights the potential for deep learning algorithms to revolutionize alarm detection systems in IoT environments, paving the way for more intelligent and adaptive solutions.