An Analytical Approach on Artificial Intelligence Based Models for Anomaly Detection in Sensor Networks
Archana Ramrao Ugale, Amol Dnyaneshwar Potgantwar · 2022
Wireless sensor networks, also known as WSNs, have quickly transformed into one of the main important areas of research, significantly influencing the development of new technologies. Many resource-constrained sensor nodes function independently to collaborate and maintain wireless networks. Through these networks, essential source information is gathered and transferred to the end users or decision-makers. WSNs have been implemented in different critical applications, such as remote patient health monitoring systems, home automation systems, sales tracking systems, enemy target monitoring and tracking systems, and fire detection system implementations, where the trustworthiness of WSNs becomes very important. These kinds of applications desire to have both comprehensive and accurate data. WSNs may be prone to anomalies as a result of low-cost hardware and software that is unstable, as well as an adverse operating environment that may disrupt the network's communication. It is necessary to identify these anomalies because they can bring about malfunctions in the network and, as a result, impair the quality of the data that has been gathered. In this research, we investigate the anomalies present in WSN, discuss the desirable attributes of anomaly detection approaches, and examine the different anomaly detection strategies that can be applied to wireless sensor networks.