A Real-Time Edge-Enabled IoT Framework with Federated Differential Privacy for Multi-Modal Crowd Monitoring in Mega Events Using SmartCrowd IoT
Mega-events present acute challenges in crowd safety, requiring sub-second monitoring, heterogeneous sensing, and strict privacy compliance at scale. We present SmartCrowd-IoT, a multi-modal crowd analytics framework built on a three-tier (sensor, edge, coordination) architecture incorporating (i) temporally aligned, reliability-aware weighted fusion across RGB, thermal, WiFi/BLE, acoustic, and RFID streams; and (ii) lightweight edge inference with federated differential privacy, enabling continuous model improvement without raw data leaving the venue. Evaluated on PETS2009, UCY, Mall, and a custom 61.3-h multi-modal corpus across three controlled mega-event simulations, SmartCrowd-IoT achieves 92.6% crowd-density accuracy, 77 ms end-to-end latency, 92.9% anomaly detection precision, and 83.4% backbone bandwidth reduction. Ablation studies confirm that both temporal alignment and reliability-aware fusion contribute significantly to these gains. The framework provides a deployable, privacy-by-design solution for mega-event crowd safety that scales to 200 edge nodes and 3000 sensors while maintaining sub-100 ms emergency response.