The Intelligent River©: Implementation of Sensor Web Enablement technologies across three tiers of system architecture: Fabric, middleware, and application

• Main Authors: White, David L, Esswein, Samuel, Hallstrom, Jason O, Ali, Farha, Parab, Shashank, Eidson, Gene, Gemmill, Jill, Post, Christopher
• Format: Conference Proceeding
• Language: English
• Subjects: Capacitive sensors; Energy management; Fabrics; Intelligent sensors; Middleware; Resource management; Sensor systems and applications; Sensor Web data management interoperable middleware; Sensor Web data visualization; Service oriented architecture; SWE standards and implementations; Water resources; Web services
• DOI: 10.1109/CTS.2010.5478493

Population growth, energy demand, and climate change are placing an unprecedented strain on water resources, requiring a fundamental shift in how these resources are managed. More precisely, resource management programs must embrace a new paradigm, one with realtime environmental monitoring at its core. The Intelligent River© is an environmental and hydrological observation system engineered to support research and management of water resources at watershed scales. The system architecture is comprised of three primary tiers: (i) a field-deployed sensor fabric and uplink infrastructure, (ii) real-time data streaming middleware, and (iii) repository, presentation, and web services. Sensor Web Enablement (SWE) adoption decisions revolve around balancing efficiency concerns and implementation time with capability and standards compliance. In this context, our team has examined, applied, and evaluated SWE technologies to enable data archival, access, and discovery. We have found varying levels of success with SWE adoption across the three tiers. At the fabric layer, platform configurability and ease-of-integration have been important engineering drivers. SensorML arose as a natural candidate solution; however, its resource requirements are largely incompatible with our target hardware platforms. At the middleware layer, recent efforts have focused on the use of SensorML and a metadata catalog to perform metadata annotation. This solution appends SensorML elements onto incoming observations, supporting data processing and semantic resolution. During early development of middleware technologies, we linked sensor platforms with web services using the transactional profile of the Sensor Observation Service (SOS) to perform data insertion and retrieval queries. At the application level, SOS is used to support data discovery and access, and Sensor Alert Service (SAS) is used to provide near-real time notifications of sensor status and QA/QC failures. In this paper, we report on our experiences, both positive and negative, and outline potential solutions to some of the most important obstacles we have encountered.