A New Maintenance Framework of WSN for Consumer and e-Commerce: A Convergence of AI, ML, and IoT

The aim of this study is to integrate energy-efficient wireless sensor networks (WSNs) into consumer electronics manufacturing and e-commerce systems. The study focuses on two main objectives: developing predictive maintenance machine learning (ML) models for consumer electronics production, enhancing personalization and contextualization in e-commerce platforms using energy-efficient WSN architecture. By analyzing data from sensors embedded in manufacturing processes, the predictive maintenance models predict maintenance needs, which can optimize production efficiency, reduce unscheduled downtime, and improve resource allocation. This study also investigates the financial benefits of implementing predictive maintenance strategies, showing significant cost savings and improved resource allocation. The incorporation of WSN into e-commerce systems encompasses the entire consumer electronics lifecycle. By employing an energy-efficient WSN architecture, reliable data collection for predictive maintenance is achieved while minimizing environmental impact. Within the e-commerce domain, this integration enhances customer experiences by ensuring products are delivered in optimal condition, thereby fostering customer satisfaction and loyalty. Additionally, the architecture facilitates personalization and contextualization, providing consumers with customized recommendations based on their preferences and real-time contextual information.