Building complex event processing capability for intelligent environmental monitoring. (June 2019)

• Record Type: Journal Article
• Authors: Sun, Alexander Y.; Zhong, Zhi; Jeong, Hoonyoung; Yang, Qian
• Is Part Of: Environmental modelling & software. Volume 116(2019)
• Page Start: 1
• Page End: 6
• Publication Date: 2019-06
• Subjects: Intelligent monitoring -- Complex event processing -- Machine learning -- Anomaly detection -- Geological carbon sequestration; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.02.015
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Rapid evolution of Internet-of-Things is driving the increased deployment of smart sensors in environmental applications, contributing to many big data characteristics of environmental monitoring. Most of the current environmental monitoring systems are not designed to handle real-time datastreams, and the best practices for datastream processing and predictive analytics are yet to be established. This work presents a complex event processing (CEP) engine for detecting anomalies in real time, and demonstrates it using a series of real monitoring data from the geological carbon sequestration domain. We show that the service-based CEP engine is instrumental for enabling environmental intelligent monitoring systems to ingest heterogeneous datastreams with scalable performance. Our CEP framework requires minimal coding from the user and can be easily extended to other similar environmental monitoring applications. Highlights: Wide use of smart sensors leads to explosive growth of data quantity and types. We present an intelligent monitoring system enabled by complex event processing (CEP). Machine learning algorithms are used to perform CEP. Our system is built on an open-source stack of high-performance scalable components. We demonstrate the system for a use case in geological carbon storage monitoring.