Edge-Intelligent AIoT Framework for Predictive Pilgrim Crowd Safety, Thermal Risk Detection, and Sustainable Energy Optimization in Smart Temples

Abstract

Large-scale religious gatherings in temples often experience critical challenges related to crowd congestion, heat stress, inefficient energy utilization, and delayed emergency response, particularly during festival seasons and peak pilgrimage periods. Traditional crowd management approaches rely heavily on manual supervision, which limits real-time situational awareness and predictive decision-making capabilities. To address these limitations, this paper proposes an Edge-Intelligent Artificial Intelligence of Things (AIoT) framework for predictive pilgrim crowd safety, thermal risk detection, and sustainable energy optimization in smart temple infrastructures.The proposed system integrates heterogeneous IoT sensors, thermal monitoring units, environmental sensing modules, surveillance cameras, and edge-computing devices to enable continuous real-time monitoring of pilgrim movement and environmental conditions. An edge-based deep learning model is employed for crowd density estimation, queue analytics, abnormal crowd behavior identification, and early stampede risk prediction. Simultaneously, thermal sensing and environmental analytics are utilized to detect heat stress conditions and dynamically trigger ventilation and emergency response mechanisms. In addition, the framework incorporates intelligent energy optimization strategies for lighting, cooling, and resource utilization based on real-time occupancy and crowd flow patterns. The architecture leverages low-latency edge intelligence to minimize cloud dependency and improve response time during high-density events. Experimental analysis demonstrates that the proposed framework significantly improves crowd safety prediction accuracy, reduces emergency response delay, and enhances energy efficiency compared with conventional monitoring systems. The proposed AIoT framework provides a scalable, sustainable, and intelligent solution for next-generation smart religious infrastructures and large-scale pilgrimage management systems.