A HIERARCHICAL ATTENTION MECHANISM FOR SENSOR DATA ANALYTICS IN INTERNET OF THINGS (IOT) APPLICATIONS

The rapid proliferation of Internet of Things (IoT) technology has resulted in an exponential increase in sensor data generated by diverse connected devices. Extracting valuable insights from this vast and complex data has become a critical challenge, necessitating advanced analytics techniques. In this project, to improve sensors analysis of data in applications for the Internet of Things, we suggest a unique technique that combines a method of attention with Long Short-Term Memory (LSTM). The attention mechanism selectively focuses on relevant sensors and their readings, dynamically weighting their importance based on the context, allowing intricate trends and connections between dates in the data to be captured by the algorithm. Concurrently, LSTM excels at modeling sequential information, enabling accurate predictions and efficient anomaly detection. Extensive experimentation and performance evaluations are conducted to assess the efficacy of our approach, contrasting it with current practices. The outcomes show that our suggested technique produces improved predictions. accuracy, efficiency, scalability, and robustness to missing data, outperforming other approaches. The synergistic integration of attention mechanism and LSTM empowers IoT applications with deeper insights and more informed decision-making capabilities. This research highlights the potential of advanced analytics techniques in optimizing IoT systems, fostering data-driven innovation, and promoting efficient resource utilization across various industries, including smart manufacturing.