TY  - EJOU
AU  - Haq, Hafiz Burhan Ul 
AU  - Akram, Waseem 
AU  - Kayani, Haroon ur Rashid 
AU  - Mahmood, Khalid 
AU  - Shih, Chihhsiong 
AU  - Kharel, Rupak 
AU  - Salhi, Amina 

TI  - A Deep Dive into Anomaly Detection in IoT Networks, Sensors, and Surveillance Videos in Smart Cities
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Anomalies; challenges; Internet of Things (IoT); learning methods; security

DO  - 10.32604/cmc.2025.073188