Embedded Cyber-Physical Systems for Monitoring and Optimization of Sports Facilities Using Consumer IoT (CIoT) and Edge Computing

Monitoring and optimizing the sports venues is critical to improving operating effectiveness and security. Conventional sensor networks frequently deal with erratic sequences of data and resource inefficiency. The research addresses these challenges by proposing a novel Edge-Fit Pro (EFP) smart gym Consumer-Internet of Things (CIoT) device system. The major goal is to create a sophisticated framework that uses CIoT and edge-device computing to maximize sports facility monitoring, with a focus on precisely and effectively recognizing abnormalities in data collected by sensors. The proposed framework integrates CIoT for data collection and edge computing for real-time processing. EFP devices handle data analysis locally, reducing latency and energy consumption. The Intelligent Kookaburra Optimized Adjustable Long Short-Term Memory (IKO-ALSTM) model is introduced for anomaly detection. This deep learning model is optimized to enhance prediction accuracy and handle temporal dependencies in the data. Simulation results reveal that the EFP system achieves anomaly detection accuracy of 98.7%, with consistent node energy stability and a 42% reduction in processing latency compared to conventional systems. Integrating CIoT and edge computing with the IKO-ALSTM model provides a robust solution for monitoring and optimizing sports facilities, highlighting its practical significance and effectiveness.