Fault prediction recommender model for iot enabled office

Abstract

Internet of Things is gaining advancements in the domain of machine learning that has a positive impact on industry 5.0. The proliferation of IoT devices has led to the deployment of different sensors in different sectors like transportation, healthcare, industries, agriculture, etc. The extensive use of sensors in the IoT environment has demanded automating the prediction of sensor faults in IoT devices. Fault prediction has become a critical issue while monitoring IoT devices. So, there is a need to early predict the faults to ensure the integrity, accuracy, reliability and fidelity of IoT sensor nodes. Also, the amount of data captured by these IoT devices is incrementing day by day which leads to the requirement for Cloud computing. The proposed model monitors the real-time health of IoT devices via a machine-learning algorithm to make the devices more efficient and assures indoor comfort to the user s satisfaction. It also emphasizes solution recommendations for faults that occurred in real-life IoT-enabled devices to mitigate faults at an early stage which is a key requirement in smart offices nowadays. newline