An Integrated Event Detection and Decision Support System for Managing the Health of Ocean and Climatic Sensor

Real-time online coral sensor and climatic data are vital for long term ecological observatory research. It is difficult to maintain a continuous real-time online data stream using various underwater sensors feeding to a server. To solve this difficulty, we integrated an event detection and decision support system (DSS) to help researchers to make decisions about sensor maintenance and to proactively prevent system malfunctions. This paper describes the components of pre-processing data in-situ, using behavior learning from historical data, creating anomaly detection, and verifying sensor data. This online system is flexible, for both health monitoring and decision-making. When data loss or sensor malfunctions occur, then an automatic email will be sent to researchers, to show both sensor health and anomaly reports. Researchers will then make a decision on who will fix the problems, and plan for maintenance on site. The integrated decision support system and event detection uses various tools and techniques, such as Mathematica for analysis and open source software (PHP, MySQL, and Highcharts) for visualization. This is the first practical system that uses heterogeneous sensors integrated with flexible cloud storage, which shows both alerts regarding system health and anomaly reports through social media, and helps researchers in decision-making, in order to reduce maintenance time and costs, and to enable long term use of sensor data in order to expand understanding on climate change.

[1]  Taghi M. Khoshgoftaar,et al.  A Review of Prognostics and Health Monitoring Techniques for Autonomous Ocean Systems , 2010 .

[2]  M Mourad,et al.  A method for automatic validation of long time series of data in urban hydrology. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[3]  Pierre Bect,et al.  Identification of abnormal events by data monitoring: Application to complex systems , 2015, Comput. Ind..

[4]  Samuel H. Huang,et al.  System health monitoring and prognostics — a review of current paradigms and practices , 2006 .

[5]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[6]  David J. Hill,et al.  Anomaly detection in streaming environmental sensor data: A data-driven modeling approach , 2010, Environ. Model. Softw..

[7]  Bartosz Balis,et al.  The UrbanFlood Common Information Space for Early Warning Systems , 2011, ICCS.

[8]  Daniel A. Menascé,et al.  Resource Allocation for Autonomic Data Centers using Analytic Performance Models , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[9]  Tony Fountain,et al.  Coral sensor network at Racha Island, Thailand , 2011 .

[10]  Kirk Martinez,et al.  Environmental Sensor Networks , 2005 .

[11]  Pascal Poncelet,et al.  Anomaly detection in monitoring sensor data for preventive maintenance , 2011, Expert Syst. Appl..

[12]  Jó Ueyama,et al.  Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks , 2015, Comput. Geosci..

[13]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[14]  Gang Wang,et al.  An autonomic provisioning framework for outsourcing data center based on virtual appliances , 2008, Cluster Computing.

[15]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[16]  Kirk Martinez,et al.  Environmental Sensor Networks: A revolution in the earth system science? , 2006 .