Approach to Anomaly Detection in Self-Organized Decentralized Wireless Sensor Network for Air Pollution Monitoring - Meleshko, Alexey; Desnitsky, Vasily; Kotenko, Igor (MATEC web of conferences, 2021)
The paper reveals the essence and features of the proposed approach to detecting anomalies in a self-organizing decentralized wireless sensor network (WSN). As a basis for detecting anomalies, the used WSN is intended to monitor atmospheric air pollution near industrial facilities and human life objects. The distinctive features of such a network are the decentralized nature of its structure and services, the autonomy and mobility of the network nodes, as well as the possibility of non-deterministic physical movement of nodes in space. The spontaneous nature of the dynamic formation of the network topology as well as the assignment of roles and private monitoring functions between the available network nodes determines such networks are subject to attacks that exploit the properties of network decentralization and its self-organization. The proposed approach to detecting anomalies is based on the collection and analysis of data from sensors and is designed to increase the security of self-organizing decentralized WSN by identifying anomalies that are critical in the context of the monitoring purposes.