Christoph Stasch

New Generation Sensor Web Enablement

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.

Semantic Enablement for Spatial Data Infrastructures

Building on abstract reference models, the Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information. These standards act as a basis for the implementation of specific services and Spatial Data Infrastructures (SDI). Research on geo-semantics plays an increasing role to support complex queries and retrieval across heterogeneous information sources, as well as for service orchestration, semantic translation, and on-the-fly integration. So far, this research targets individual solutions or focuses on the Semantic Web, leaving the integration into SDI aside. What is missing is a shared and transparent Semantic Enablement Layer for SDI which also integrates reasoning services known from the Semantic Web. Instead of developing new semantically enabled services from scratch, we propose to create profiles of existing services that implement a transparent mapping between the OGC and the Semantic Web world. Finally, we point out how to combine SDI with linked data.

Discovery Mechanisms for the Sensor Web

This paper addresses the discovery of sensors within the OGC Sensor Web Enablement framework. Whereas services like the OGC Web Map Service or Web Coverage Service are already well supported through catalogue services, the field of sensor networks and the according discovery mechanisms is still a challenge. The focus within this article will be on the use of existing OGC Sensor Web components for realizing a discovery solution. After discussing the requirements for a Sensor Web discovery mechanism, an approach will be presented that was developed within the EU funded project “OSIRIS”. This solution offers mechanisms to search for sensors, exploit basic semantic relationships, harvest sensor metadata and integrate sensor discovery into already existing catalogues.

A RESTful proxy and data model for linked sensor data

Abstract The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration. So far, sensor data are stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies toward their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (1) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (2) how to establish meaningful links between those data sets, that is, observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGCs Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.

Semantic Challenges for Sensor Plug and Play

The goal of the Sensor Web Enablement (SWE) initiative of the Open Geospatial Consortium (OGC) is the definition of web service interfaces and data encodings to make sensors discoverable, taskable and accessible on the World Wide Web. The SWE specifications enable a standardized communication and interaction with arbitrary types of sensors and sensor systems. The central concepts within OGCs Sensor Web architecture are sensors, observations and features of interest. Sensors and their observations can be registered and stored through the Sensor Observation Service (SOS) to make them accessible for clients. So far, mechanisms are missing which support a semantic matching between features of interest stored in a database and referred to by an observation. The same applies for the matching between observations as sensor outputs and the properties of the features of interest. By taking a use case from disaster management, we outline the challenges and demonstrate how semantically annotated SWE data models and service interfaces support semantic matching. The result is a roadmap towards a semantically enabled sensor plug & play within the Sensor Web.

A Stimulus-Centric Algebraic Approach to Sensors and Observations

The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observations at multiple scales. This scale dependency is not handled well by todays rather technical sensor definitions. Geosensor networks are normally defined as distributed ad-hoc wireless networks of computing platforms serving to monitor phenomena in geographic space. Such definitions also do not admit animals as sensors. Consequently, they exclude human sensors, which are the key to volunteered geographic information, and they fail to support connections between phenomena observed at multiple scales. We propose definitions of sensors as information sources at multiple aggregation levels, relating physical stimuli to observations. An algebraic formalization shows their behavior as well as their aggregations and generalizations. It is intended as a basis for defining consistent application programming interfaces to sense the environment at multiple scales of observations and with different types of sensors.

Meaningful spatial prediction and aggregation

The appropriateness of spatial prediction methods such as Kriging, or aggregation methods such as summing observation values over an area, is currently judged by domain experts using their knowledge and expertise. In order to provide support from information systems for automatically discouraging or proposing prediction or aggregation methods for a dataset, expert knowledge needs to be formalized. This involves, in particular, knowledge about phenomena represented by data and models, as well as about underlying procedures. In this paper, we introduce a novel notion of meaningfulness of prediction and aggregation. To this end, we present a formal theory about spatio-temporal variable types, observation procedures, as well as interpolation and aggregation procedures relevant in Spatial Statistics. Meaningfulness is defined as correspondence between functions and data sets, the former representing data generation procedures such as observation and prediction. Comparison is based on semantic reference systems, which are types of potential outputs of a procedure. The theory is implemented in higher-order logic (HOL), and theorems about meaningfulness are proved in the semi-automated prover Isabelle. The type system of our theory is available as a Web Ontology Language (OWL) pattern for use in the Semantic Web. In addition, we show how to implement a data-model recommender system in the statistics tool environment R. We consider our theory groundwork to automate semantic interoperability of data and models. We introduce a new notion of meaningfulness of spatial prediction and aggregation.Observation, prediction, and aggregation procedures are formalized as functions.We show how datasets can be described as results of executing such procedures.We propose formal checks of meaningfulness based on functional correspondence.Meaningfulness checks on the type level are implemented in the R software and OWL.

Spatio-temporal aggregation of European air quality observations in the Sensor Web

An increasing amount of observations from different applications such as long-term environmental monitoring or disaster management is published in the Web using Sensor Web technologies. The standardization of these technologies eases the integration of heterogeneous observations into several applications. However, as observations differ in spatio-temporal coverage and resolution, aggregation of observations in space and time is needed. We present an approach for spatio-temporal aggregation in the Sensor Web using the Geoprocessing Web. In particular, we define a tailored observation model for different aggregation levels, a process model for aggregation processes and a Spatio-Temporal Aggregation Service. The presented approach is demonstrated by a case study of delivering aggregated air quality observations on-demand in the Sensor Web.

Ontology-Based Integration of Sensor Web Services in Disaster Management

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Decision support for tsunami early warning in Indonesia : the role of OGC standards

As part of the German contribution to the Indonesian Tsunami Early Warning System InaTEWS, an innovative Decision Support System (DSS) has been developed in order to support the tsunami early warning process in an unique way. The paper describes the modular and open environment in which the DSS operates in, its main tasks and components, the Graphical User Interface (GUI) and the focus on standardization and interoperability which led the design and development of the DSS. Emphasis is put on situation assessment and information fusion of geographic, risk and vulnerability, sensor and simulation data. The work presented here is embedded in the German-Indonesian Tsunami Early Warning System (GITEWS) project funded by the German Federal Ministry of Education and Research (BMBF).