Restful Web Service and Web-Based Data Visualization for Environmental Monitoring

Restful Web Service and Web-Based Data Visualization for Environmental Monitoring

Sungchul Lee (University of Nevada Las Vegas, USA), Ju-Yeon Jo (University of Nevada Las Vegas, USA) and Yoohwan Kim (University of Nevada Las Vegas, USA)
Copyright: © 2016 |Pages: 21
DOI: 10.4018/978-1-4666-9840-6.ch032
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Abstract

The Nevada Solar Energy-Water-Environment Nexus project collects a large amount of environmental data from a variety of sensors such as soil, atmosphere, biology, and ecology. Mostly, the environmental data is related to a development of renewable energy resources in the Nexus project. The environmental data can have an impact on other research fields if it can easily be shared with other researchers, students, teachers, and general users. Therefore, Nevada Climate Change Portal (NCCP) site was created for Nexus project with a purpose of sharing such data. However, there are some challenges to address in utilizing such data, collecting the data, and sharing the data among the users. In this research, the authors propose Extended Web Service Architecture for solving these challenges. The authors implement Arduino instead of CR1000 as a collector due to its cost effectiveness. The authors also use REST API to overcome the limitations of Arduino. Moreover, the authors experiment with popular Web-based data visualization tools such as Google Chart, Flex, OFC, and D3 to visualize NCCP data.
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Environmental data are growing bigger and becoming more important due to a significant development of environmental monitoring. Therefore, data portals, such as Climate Data Portal (Soreide, Sun, Kilonsky, & Denbo, 2001), NCCP, and GPS Explorer data portal, are becoming more important to share such data. Sensor Web Services-based observation/analysis/modeling is focused on collecting and sharing the environmental sensor data at the portal (Xianfeng, Chaoliang Kagawa & Raghavan, 2010. Bock, Crowell, Prawirodirdjo & Jamason, 2008).

However, majority of portals need to improve their data visualization for real-time visualization data Web Service. Most of visualization research is practiced with off-line tools. For example, Mathematical toolssuch as Matlab (Azemi, & Stook, 1996), Mathematica (Savory, 1995), GODIVA (Xiaosong, Winslett, Norris, & Xiangmin, 2004) and so on, are typically included in visualization routines and so are off-line visualization tools such as Origin (Yingqi, 2011), Mayavi (Ramachandran, & Varoquaux, 2011), and R-software (Voulgaropoulou, Spanos & Angelis 2012). These tools are not suitable for data portal as they are not based on on-line visualization.

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