Predictive Maintenance Optimization in Zigbee-Enabled Smart Home Networks: A Machine Learning-Driven Approach Utilizing Fault Prediction Models

The present research proposes a new machine learning-driven method that uses failure prediction models to address the critical need for reliable service scheduling beyond Zigbee-enabled smart home networks. The intention of employing predictive maintenance approaches and advanced analytics is to decrease costs and delay periods associated with equipment breakdowns. Smart home networks rely more on legitimate maintenance methods to control and run IoT devices, enhancing convenience and energy economy. The suggested methodology enables proactive upkeep by detecting system faults using modern machine learning techniques such as Firefly Optimization and XGBoost. Within the smart house network, data is collected from sensors and monitoring equipment and pre-processed to extract useful information for fault prediction. The XGBoost technique improves prediction accuracy by identifying hidden correlations in records. The algorithms are skilled in using historical records to hit upon traits that suggest probable faults or disasters in Zigbee-enabled devices. By combining XGBoost as well as Firefly Optimization into fault identification algorithms, this approach attempts to give well-timed and accurate forecasts, taking into consideration faster repairs and retaining the serviceability and dependability of clever home equipment. This approach differs because it employs exceptional machine learning algorithms, preprocessing of data, and hyperparameter changes to provide regular upkeep, hence extending the lifespan and reliability of smart home devices. The proposed framework is developed by python. The performance evaluation of the suggested models indicates their efficacy, having an accuracy score of 98%, highlighting their ability to address anticipates about servicing devices in Zigbee-enabled smart homes.