• Swetha Ramachandran
• Bachala Lekhasree
• Amrutha Obili
• Mr. M. Sadhasivam

The rapid development in the field of System of Chip (SoC) technology Internet of Things (IoT) cloud computing and artificial intelligence has brought more possibilities of improving and solving the current problems. With data analytics and the use of machine learning/deep learning it is made possible to learn the underlying patterns and make decisions based on what was learned from massive data generated from IoT sensors. When combined with cloud computing the whole pipeline can be automated and free of manual controls and operations. In this paper an implementation of an automated data engineering pipeline for anomaly detection of IoT sensor data is studied and proposed. We perform anomaly detection using the predicted sensor data. Taken together we show how in these application domains predictive failure classification can be achieved thus paving the way for predictive maintenance. The Internet of Things (IoT) is a system that connects physical computing devices, sensors software and other technologies. Data can be collected, transferred and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things.

IoT, LSTM, SoC, Cloud Computing, Machine Learning

"An End-to-End Multi-Protocol Data Engineering Pipeline Anomaly Detection in IoT Network using LSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.d740-d743, May-2023, Available :http://www.jetir.org/papers/JETIR2305396.pdf

"An End-to-End Multi-Protocol Data Engineering Pipeline Anomaly Detection in IoT Network using LSTM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppd740-d743, May-2023, Available at : http://www.jetir.org/papers/JETIR2305396.pdf