Open Access

REVIEW

A Deep Dive into Anomaly Detection in IoT Networks, Sensors, and Surveillance Videos in Smart Cities

Hafiz Burhan Ul Haq¹, Waseem Akram², Haroon ur Rashid Kayani³, Khalid Mahmood^4,*, Chihhsiong Shih⁵, Rupak Kharel^6,7, Amina Salhi⁸

1 Department of Information Technology, Faculty of Computer Sciences, Lahore Garrison University, Lahore, 54000, Pakistan
2 Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, 64002, Taiwan
3 School of Informatics & Robotics, Institute for Art and Culture, Thokar Niaz Baig, Main Raiwand Road, Lahore, 54000, Pakistan
4 Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliu, 64002, Taiwan
5 Department of Computer Science, Tunghai University, Taichung, 407224, Taiwan
6 School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK
7 The Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, 70000, Vietnam
8 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Khalid Mahmood. Email:

Computers, Materials & Continua 2026, 87(2), 4 https://doi.org/10.32604/cmc.2025.073188

Received 12 September 2025; Accepted 15 December 2025; Issue published 12 March 2026

Abstract

The Internet of Things (IoT) is a new model that evolved with the rapid progress of advanced technology and gained tremendous popularity due to its applications. Anomaly detection has widely attracted researchers’ attention in the last few years, and its effects on diverse applications. This review article covers the various methods and tools developed to perform the task efficiently and automatically in a smart city. In this work, we present a comprehensive literature review (2011 onwards) of three major types of anomalies: network anomalies, sensor anomalies, and video-based anomalies, along with their methods and software tools. Furthermore, anomaly detection methods such as machine learning and deep learning are presented in this work, highlighting their detection strategy techniques, features, applications, issues, and challenges. Moreover, a generic algorithm is also developed to ease the user achieve the task more specifically by targeting a specific domain as well as approach. Comparative studies of three anomaly methods and their analysis identify research discovery areas with their applications. As a result, researchers and practitioners can familiarize themselves with the existing methods for solving real problems, improving methods, and developing new optimum methods for anomaly detection in diverse applications.

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Keywords

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