Environmental monitoring is a critical process in areas potentially affected by natural disasters. Nowadays, the distributed processing of vast amounts of heterogeneous sensor data in real time is a challenging task. Event processing tools allow creating an event abstraction layer on top of sensor data. Users can define event patterns to filter in real-time the information they are interested in and avoid irrelevant data. Extreme events are usually related to other environmental occurrences, e.g. landslides are related (among others) to precipitations and earthquakes. To be able to determine whether an occurrence could potentially lead to an extreme event, domain knowledge is necessary. Ontologies are helpful for this task, since they are able to capture a representation of knowledge as a set of concepts and relations, within a specific domain. The research presented in this chapter aims at combining event-processing tools with semantic technologies to improve the discovery of environmental data.

Next generations of spatial information infrastructures call for more dynamic service composition, more sources of information, as well as stronger capabilities for their integration. Sensor networks have been identified as a major data provider for such infrastructures, while Semantic Web technologies have demonstrated their integration capabilities. Most sensor data is stored and accessed using the Observations & Measurements (O&M) standard of the Open Geospatial Consortium (OGC) as data model. However, with the advent of the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). The ongoing paradigm shift to Linked Sensor Data complements this attempt and also adds interlinking as a new challenge. In this demonstration paper, we briefly present a Linked Data model and a RESTful proxy for OGC's Sensor Observation Service (SOS) to improve integration and inter-linkage of observation data.

Temporal properties in Event Stream Processing are complex to be modeled and are still subject to research. This article addresses the implications that sensor measurements introduce to the sensor web and its applications with respect to their temporal properties. This includes the use and access of multiple different time properties, processing of multi-dimensional time information and strategies for Event Stream Processing. Each challenge is described and an approach is presented.

This paper presents an Event Driven Architecture for environmental monitoring and live decision support. Multiple OGC web services are integrated into this architecture, including the Web Processing Service and the Sensor Event Service. The system is demonstrated using a pilot from the EC funded GENESIS project. The architecture, the processing steps and the benefits of the system are described in detail.

Many sensor networks have been deployed to monitor Earth's environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.

Human observations have the potential to significantly improve the actuality and completeness of data about phenomena such as noise distribution in urban environments. The Human Sensor Web aims at providing approaches for creating and sharing human observations as well as sensor observations on the Web. One challenge is the integration of these observations for further analysis. The aspects presented in this paper are examined by the example of a noise mapping community.

Sensor data is stored and published using OGC’s Observation & Measurement specifications as underlying data model. With the advent of volunteered geographic information and the Semantic Sensor Web, work on an ontological, i.e. conceptual, model gains importance within the Sensor Web Enablement community. In contrast to a data model, an ontological approach abstracts from implementation details by focusing on modeling the real world from the perspective of a particular domain or application and, hence, restricts the interpretation of the used terminology towards their intended meaning. The shift to linked sensor data, however, requires yet another perspective. Two challenges have to be addressed, (i) how to refer to changing and frequently updated data sets such as stored in Sensor Observation Services using Uniform Resource Identifiers, and (ii) how to establish meaningful links between those data sets, i.e., observations, sensors, features of interest, observed properties, and further participants in the measurement process. In this short paper we focus on the problem of assigning meaningful URIs.

As sensor networks grow the amount of sensor measurements increases. This large amount of data is crucial for monitoring systems but useless if it is not trustworthy. In this paper different approaches for the validation of sensor measurements using standardized encodings and services specified by the Open Geospatial Consortium (OGC) are discussed. Approaches based on current OGC specifications for Sensor Web Enablement (SWE) are discussed and their drawbacks are identified. Based on these drawbacks a new architecture for in-stream validation of sensor measurements is introduced. This new approach uses event processing techniques and is based on encodings and services proposed in an OGC SWE discussion paper.

Abstract