Unsupervised Anomaly Detection in IoT Systems for Smart Cities

Anomaly detection‐based intelligent computing in internet of things and network applications

“…Such systems typically only use relatively a small amount of samples to train a goal‐oriented model which could be heavily affected by minor anomalies. Therefore, in the IoT and emerging network applications, it is urgent to detect anomalies from the collected samples to alleviate the side effect on model training 3,4 …”

“…Therefore, in the IoT and emerging network applications, it is urgent to detect anomalies from the collected samples to alleviate the side effect on model training. 3,4 Anomalies can be defined as the patterns that do not conform to expected behavior. 5,6 Actually, anomalies can appear in different forms in real applications, f.i., illegal intrusions on the Internet of services, 7 irregular behaviors in the scenario of smart city, 8 and abnormal events in smart agriculture.…”

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

“…Today, anomaly detection is broadly used in many research areas such as health monitoring [1], [2], [3], [4], [5], [6] for example heart disease diagnosis [1] and neuromuscular disorders diagnosis [5], environment monitoring such as sewer pipeline fault identification [7] and solar farms anomalies detection [8], and machine condition monitoring [9], [10] for example machinery fault diagnosis [11], [12], [13], [14], [9]. Depending on the anomaly detection problem, it is required to design algorithms which are able to identify anomalies in different types of data such as image [15], [2], [16], [17], video [7], sound signal [9] speech signal [18], sensor signal [19], [5], text [20], spatio-temporal data [4], streaming data [21] and time-series [22], [23]. Hence, it seems that no general solution works for all of the anomaly detection problems.…”

Developing a Data-Driven Unsupervised Pattern Recognition Approach for Sensor Signal Anomaly Detection

“…Today, anomaly detection is broadly used in many research areas such as health monitoring [1], [2], [3], [4], [5], [6] for example heart disease diagnosis [1] and neuromuscular disorders diagnosis [5], environment monitoring such as sewer pipeline fault identification [7] and solar farms anomalies detection [8], and machine condition monitoring [9], [10] for example machinery fault diagnosis [11], [12], [13], [14], [9]. Depending on the anomaly detection problem, it is required to design algorithms which are able to identify anomalies in different types of data such as image [15], [2], [16], [17], video [7], sound signal [9] speech signal [18], sensor signal [19], [5], text [20], spatio-temporal data [4], streaming data [21] and time-series [22], [23]. Hence, it seems that no general solution works for all of the anomaly detection problems.…”

“…Data-driven methods can be categorized into three main categories: 1) supervised [14], [1], [8], [27], 2) semi-supervised [28], and 3) unsupervised [6], [26], [3], [23] anomaly detection. Since the labelled data, which is required for supervised anomaly detection, is often not available or in other words, collecting sufficient anomolous samples is infeasible in most of the cases, many researchers have tried to detect anomalies without labelled data.…”