Toward a Full Exploitation of IoT in Smart Cities: A Review of IoT Anomaly Detection Techniques

Abstract

The world is currently progressing towards a new connectivity era where billions of sensors are connected over a network called the Internet of Things (IoT). IoT enables a wide range of physical objects and devices to be connected and monitored with insufficient spatial and temporal detail. Despite their potential to improve multiple application domains, anomalies in the devices’ behaviors pose a significant challenge, especially in the smart city’s domain. Many research works have been devoted to determining such anomalous behaviors; however, there is a lack of comprehensive review focusing on anomaly detection techniques using statistical and machine learning methods in the smart cities domain. This work aims to fill this gap by presenting a review of anomaly detection techniques using statistical and machine learning methods. This paper explains the essential contexts related to IoT, followed by a review of the IoT anomaly detection techniques and their challenges, types, and detection modes. The paper then presents a summary of the related works related to smart cities. Finally, the open challenges and future directions were highlighted.