article · Sustainability
Workplaces and urban environments increasingly rely on Internet of Things devices and sensors across various sectors, including manufacturing, transportation, and healthcare. Maintaining the reliability, precision, and security of these sensor nodes requires anticipating faults before they disrupt operations. A fault prediction recommender model was developed to monitor the real-time health of smart office devices and provide solution recommendations to resolve issues at an early stage. The approach was assessed using multiple machine learning classifiers, specifically K-Nearest Neighbour, Decision Tree, Gaussian Naive Bayes, and Random Forest. Evaluated across metrics including precision, recall, accuracy, and F1 score, the Random Forest model outperformed the alternative techniques, reaching an overall accuracy of 94.27 per cent. This demonstrated that machine learning algorithms can successfully identify sensor faults in connected workplaces to maintain operational efficiency.
Modern workplaces increasingly rely on interconnected sensors to automate daily operations, but unexpected device failures can disrupt productivity and compromise data integrity. Predicting sensor breakdowns before they happen helps organisations maintain reliable, secure smart office systems. By identifying faults early and recommending corrective actions, predictive maintenance models can improve device efficiency and support more dependable urban living environments.
The model targets smart office environments and facilities management teams seeking automated fault detection and maintenance recommendations for connected devices. While tested on sensor data using standard classification algorithms with strong accuracy, the work appears to be early-stage or lab-tested research. Bringing it toward commercial deployment would require integrating the algorithm into operational cloud or workplace management platforms and evaluating performance on larger datasets.
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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.
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DOI: 10.3390/su15021060
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