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This dissertation documents the design and implementation of a near-real-time geospatial in situ sensor network for monitoring stormwater runoff at the Green Learning Station. The project solves the need by the Environmental Protection Agency and Cincinnati Metropolitan Sewer District for an affordable and standardized network. The project also makes contributions to geospatial standards and sensor web research. This dissertation uses open innovation, including open standards, to help reduce cost and complexities of environmental sensor networking architectures. Article 1 focuses on the technical implementation of the in situ sensor network helping to fill the research gap of applied end-to-end in situ sensing. This gap is further highlighted by the inadequacies within international geospatial standards. Article 2 discusses the greatest hardware challenge within sensor webs, embedded devices. The Green Learning Station project solved this challenge, bridged the gap between sensor protocols and standard communication protocols, with Common-off-the-Shelf (COTS) routers. The modified routers enable the development of the client/server architecture for environmental sensor networking outlined in Article 3. The client software is designed for embedded devices while the web services were designed with the Representational State Transfer (REST) approach. The Green Learning Station design and implementation is unique because of the open innovation approach to geospatial in situ sensor webs by a team of engineers with expertise at every layer of the architecture. This expertise enabled the inclusion of spatial standards and spatial data throughout the architecture. This approach creates a standardized and affordable geospatial sensor network as an example for others to study and expand upon for a variety of monitoring solutions.

The emergence of dynamic information sources - like social, mobile and sensors, has led to ginormous streams of real time data on the web also called, the era of Big Data [1]. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years [1]. Gigaom article on Big data shows, how the total information generated by these dynamic information sources has completely surpassed the total storage capacity. Thus keeping in mind the problem of ever-increasing data, this thesis focuses on semantically integrating and analyzing multiple, multimodal, heterogeneous streams of weather data with the goal of creating meaningful thematic abstractions in real-time. This is accomplished by implementing an infrastructure for creating and mining thematic abstractions over massive amount of real-time sensor streams. Evaluation section shows 69% data reduction with this approach.

We have constructed a prototype Open Geospatial Consortium (OGC) standards-based Arctic Climatology Sensor Network Prototype (ACSNP) in response to recent developments in sensor technology and Internet Protocol Suite (TCP/IP) wireless communications in Barrow, Alaska for the National Science Foundation (NSF). The OGC standards enable increased, interoperability, scalability, and extensibility for geospatial information at a reduced cost. Our approach for the prototype is to integrate established technologies to create near-real-time geographic information networks (GINs). We linked a variety of meteorological and image sensors to wide area wireless networks in Barrow, Alaska. The network is a TCP/IP-based 700 Mhz WipLL network consisting of a 16 kilometer diameter local cloud as well as Iridium Open Port Units, which allow for global connectivity, at other remote research stations and on ice breakers. The Department of Energy (DOE) building in Barrow is the location of two automatically populated mirrored File Transfer Protocol (FTP) servers running Microsoft Server 2003 within a virtualized environment. High availability for the GIN is met through the use of virtualization as well as redundant power supplies and hardware-based security. The data are automatically harvested from the remote site over redundant 2XT-1 satellite links to the central data center in Cincinnati, Ohio where it is formatted to comply with the OGC database initiatives to create an OGC-compliant geodatabase within Microsoft SQL Server 2008. This cyberinfrastructure is remotely monitored 24X7 tracking network components and mission critical applications providing notification of potential capacity, connection and performance problems. The final web publication is the result of a three part system; geodatabases, web services and web applications. A data harvester is used for automating data retrieval and distribution into a geodatabase. The harvester allows for centralized control and monitoring of transfers through a browser interface and provides a comprehensive built-in scheduler and produces complete reports. A function of the database is the conversion of raw noncompliant sensor data into the standardized OGC geodatabase. For web services we use ESRI’s ArcGIS Server technology for retrieval and publication utilizing ESRI’s compliance with OGC web services. These web services may then be embedded within clients, such as ESRI’s ArcGIS Desktop and Google Earth for analysis, and web applications. The Arctic Climatology Sensor Network Prototype is accessible at OpenSensorMap.com.

The Hydrologic Information System Project of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) has successfully created a large-scale prototype Hydrologic Information System (HIS). This system catalogs and provides access to over 23 million time series of hydrologic data, which are distributed across the United States at various academic, research, and governmental data providers. The service-oriented architecture that enables the HIS comprises distributed hydrologic data servers, a centralized series catalog, and various client software applications, and is supported by WaterML, a standardized language for transmission of hydrologic data. The current architectural model, termed the Network-Observations Model, of the HIS relies on a searchable central catalog of series metadata. Harvesting series metadata from large federal data providers, such as the USGS, EPA, and NCDC, has proven a laborious undertaking and involves custom database migration tools. This time-consuming harvesting task, coupled with a multitude of custom-coded solutions at the central series catalog has led to concerns with the long-term sustainability of the current architectural model. A new architectural model, termed the Services Stack Model, is proposed in this thesis. In the proposed model, a catalog of services metadata, rather than of series metadata is used to connect hydrologic data consumers with data providers. Internationally-recognized web service and data encoding standards, including the upcoming WaterML2.0 specification, from the Open Geospatial Consortium are used as the backbone of the new model. The proposed model will hopefully lead to greater acceptance of the CUAHSI-HIS, and result in increased sustainability and reduced maintenance of the system in the long-term.