Real-time environmental sensor data: An application to water quality using web services. (October 2016)

• Record Type: Journal Article
• Authors: Wong, Brandon P.; Kerkez, Branko
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 505
• Page End: 517
• Publication Date: 2016-10
• Subjects: Real-time data -- Web services -- Sensor networks -- Adaptive sampling -- Internet of things (IoT); 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.2016.07.020
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: While real-time sensor feeds have the potential to transform both environmental science and decision-making, such data are rarely part of real-time workflows, analyses and modeling tool chains. Despite benefits ranging from detecting malfunctioning sensors to adaptive sampling, the limited number and complexity of existing real-time platforms across environmental domains pose a barrier to the adoption of real-time data. We present an architecture built upon 1) the increasing availability of new technologies to expose environmental sensors as web services, and 2) the merging of these services under recent innovations on the Internet of Things (IoT) . By leveraging recent developments in the IoT arena, the environmental sciences stand to make significant gains in the use of real-time data. We describe a use case in the hydrologic sciences, where an adaptive sampling algorithm is successfully deployed to optimize the use of a constrained sensor network resource. Highlights: Leveraging Internet of Things (IoT) services to support environmental sensing. A real-time architecture for the measurement and control of environmental systems. Demonstrating benefits of real-time data through adaptive sampling of water quality.