Fault Prediction Recommender Model for IoT Enabled Sensors Based Workplace
Mudita Uppal, Deepali Gupta, Amena Mahmoud, M. A. Elmagzoub, Adel Sulaiman, Mana Saleh Al Reshan, Asadullah Shaikh, Sapna Juneja
Abstract
Industry 5.0 benefits from advancements being made in the field of machine learning and the Internet of Things. Different sensors have been installed in a variety of IoT devices present in different industries such as transportation, healthcare, manufacturing, agriculture, etc. The sensors present in these devices should automatically predict errors due to the extensive use of sensors in urban living. To ensure the integrity, precision, security, dependability and fidelity of sensor nodes, it is, therefore, necessary to foresee faults before they occur. Additionally, as more data is being collected by these devices every day, cloud computing becomes more necessary for sustainable urban living. The proposed model emphasizes solution recommendations for faults that occurred in real-life smart devices to mitigate faults at an early stage, which is a key requirement in today’s smart offices. The proposed model monitors the real-time health of IoT devices through an ML algorithm to make devices more efficient and increase the quality of life. Through the use of K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes and Random Forest approach, the proposed fault prediction recommender model has been evaluated and Random Forest shows the highest accuracy compared to other classifiers. Several performance indicators such as recall, accuracy, F1 score and precision were utilized to examine the performance of the model. The results have demonstrated the effectiveness of ML techniques applied to sensors in predicting faults in smart offices with Random Forest being observed as the best technique with a maximum accuracy of 94.27%. In future, deep learning can also be applied to bigger datasets to provide more accurate results.