A Smart IoT-Based Communication and Optimization System for Hajj and Umrah Services

The management of Hajj and Umrah services requires reliable communication, continuous location awareness, and rapid emergency response because pilgrims move in dense and dynamic environments. This study presents a smart Internet of Things (IoT) based communication and optimization system for Hajj and Umrah services. The system integrates GPS-enabled wearable devices, a panic button emergency mechanism, cellular data transmission, a centralized cloud dashboard, and a network-aware deep neural network (DNN) component for congestion risk prediction. Unlike a purely sensing-oriented tracking system, the proposed framework explicitly incorporates communication indicators, including end-to-end latency, packet delivery ratio, jitter, and bandwidth utilization, as part of the monitoring and prediction pipeline. The prototype was implemented using a wearable tracking device and a web-based dashboard, while the prediction component was evaluated on a simulation dataset containing spatiotemporal mobility and network performance variables. Functional verification produced a black-box functional success rate of 95.6%, which is reported only as an implementation reliability indicator. The congestion risk classifier achieved 80.0% accuracy with macro-averaged precision, recall, and F1 score values of 0.72, 0.77, and 0.74, respectively. The evaluation also defines baseline comparisons, confusion matrix analysis, ROC-based assessment, and network-level stress scenarios to clarify the scientific interpretation of the results. The findings indicate that a network-aware IoT architecture can improve situational awareness and support timely operational decision-making for large-scale pilgrimage services, while full validation still requires larger real-world deployment data.