IoT-aided smart city architecture for anomaly detection

Jiaojie Yuan and Jiewen Zhao

International Journal of Critical Infrastructures, 2025, vol. 21, issue 5, 495-514

Abstract: Anomaly detection in smart cities is critical for mitigating human fall-related injuries and fatalities, particularly within IoT devices. Despite numerous vision-based fall detection methods, challenges persist regarding accuracy and computation costs, especially in resource-constrained IoT environments. This paper proposes a novel fall detection approach leveraging the Yolo algorithm, known for its efficiency in minimising computation costs while maintaining high accuracy. By utilising a diverse fall image dataset, the method undergoes rigorous training and evaluation, employing standard performance metrics. The results reveal impressive precision, recall, and mean average precision (mAP) values of 93%, 89%, and 95%, respectively. Notably, the Yolo algorithm's computational efficiency ensures minimal resource utilisation, making it suitable for real-time deployment in IoT devices within smart city infrastructures. Consequently, this method presents a promising solution for enhancing fall detection accuracy while optimising computational resources, thus advancing safety measures in urban environments.

Keywords: anomaly detection; fall detection; vision system; Yolo; smart city; internet of things; IoT; mean average precision; mAP; algorithm's computational efficiency. (search for similar items in EconPapers)
Date: 2025
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