SSN Applications

The W3C Semantic Sensor Network Incubator Group has recently developed the Semantic Sensor Network (SSN) ontology that enables expressive representation of sensors, sensor observations, and knowledge of the environment. The SSN ontology is encoded in the Web Ontology Language (OWL) and has begun to achieve broad adoption and application within the sensors community. It is currently being used by various organizations, from academia, government, and industry, for improved management of sensor data on the Web, involving annotation, integration, publishing, and search.

Related Material

• Semantic Sensor Network XG (Wiki, Final Report)
• The SSN Ontology of the W3C Semantic Sensor Network Incubator Group (Journal of Web Semantics, 2012)

Applications and Projects using the Semantic Sensor Network Ontology

Internet of Things Environment for Service Creation and Testing (IoT.est)

The EU FP7 IoT.est project aims to create a test-driven service creation and deployment environment for Internet of Things (IoT) enabled business services. An ontology for describing the IoT domain concepts such as IoT Resources and Services has been developed. The ontology will be used to support resource and services discovery, composition, testing, adaptation and compensation processes.

Use of SSN: Semantic Sensor Network (SSN) ontology is reused in the project to describe the Sensor resources and the systems they form as well as observation and measurement information of sensors.

Contact Person: Wei Wang, CCSR, University of Surrey

Semantic Perception: A Semantic Web Approach to Machine Perception

Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. By drawing inspiration from cognitive models of perception, we can improve machine perception by defining an ontology of perception to enable integration of external knowledge (i.e., Linked Data) to better enable machines to perceive.

Use of SSN: By providing formal definition for concepts in the domain of sensing (such as observable property and feature of interest), the Semantic Sensor Network (SSN) ontology serves as a foundation for an ontology of perception. The SSN ontology provides the terminology for describing observations and knowledge of the environment, both of which are critical for perceptual interpretation.

Contact: Cory Henson, Kno.e.sis, Wright State University

Related Material

• Semantic Perception: Converting Sensory Observations to Abstractions (IEEE Internet Computing, Special Issue on Context-Aware Computing, March/April 2012)
• An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web (Applied Ontology, 2011)

SECURE: Semantics Empowered Rescue Environment

SECURE is a Semantic Web enabled system for collecting and processing sensor data within a rescue environment. The real-time system collects heterogeneous raw sensor data from rescue robots through a wireless sensor network. The raw sensor data is converted to RDF using the Semantic Sensor Network (SSN) ontology and further processed to generate abstractions used for event detection in emergency scenarios.

Use of SSN: The observation data originating from temperature sensors, carbon-dioxide sensors, carbon-monoxide sensors, etc. are encoded in RDF, conformant to SSN, which enables integration and analysis to derive knowledge of events, such as fire.

Contact: Cory Henson, Kno.e.sis, Wright State University

Related Material

• SECURE: Semantics Empowered Rescue Environment (SSN, 2011)

SemSorGrid4Env: Semantic Sensor Grids for Environmental Applications

The main objective of the SemSorGrid4Env FP7 project is to specify, design, implement and deploy a service-oriented architecture and middleware which allows application developers to build semantic-based sensor network applications for environmental management. The project addresses the following challenges (i) Discovering relevant sources of data based on their content, e.g. features of interest and the region covered by the dataset; (ii) Reconciling heterogeneity in the data sources, e.g. the modality, data model, or interface of the data source, and enabling users to retrieve data using domain concepts; and (iii) Integrating and/or mashing up data from multiple sources to enable more knowledge about a situation to become available. Our proposed approach makes extensive use of semantic technologies to reconcile the heterogeneity of data sources whilst offering services for correlating data from independent sources. This enables user-level applications to generate queries over ontologies which are then translated into queries to be executed over the data sources.

Use of SSN: We use ontologies to represent the common data model for the information space since they facilitate: (i) Describing the different infrastructure services and data sources as well as any domain-dependent information; (ii) Having a shared vocabulary to interoperate both across the internal infrastructure services, and between that infrastructure and external sources that adopt alternative approaches, e.g. ogc-swe based ones; and (iii) Discovering, accessing, and integrating information that is shared within the infrastructure. These ontologies satisfy different knowledge representation requirements: (i) To represent sensor networks and their observed informa- tion about properties of certain features of interest. This is covered by the SSN ontology. The SSN reuses the DOLCE+DnS UltraLite upper ontology. (ii) To represent the services provided by the infrastructure and the datasets they provide access to. This is covered by the Service module that reuses the SWEET upper ontologies and includes concepts from the ISO19119 standard on geographic information services.

Contact: Oscar Corcho, OEG-UPM

Related Material

• SemsorGrid4Env project
• A Semantically Enabled Service Architecture for Mashups over Streaming and Stored Data (ESWC, 2011)
• Semantic Access to Sensor Observations through Web APIs (IEEE Semantic Computing, 2011)

Swiss Experiment: Semantic Metadata and Querying

The Swiss Experiment Platform aims to develop innovative hardware and software technologies and implement them for collaborative environmental research. SwissEx Platform development can be split into two separate topics

• Software infrastructure: An integrated, generic metadata and data storage solution with advanced metadata-based data manipulation and tagging (openly available for public use).
• New sensor and network technology: Self organising sensor networks with advanced in-network processing solutions and the development of existing sensors to take advantage of the latest in technology.

Use of SSN: We have used the SSN ontology to represent sensor metadata: sensor descriptions, observed properties and features, platforms, etc. It has also been used to represent sensor observations, through queries that are executing using SPARQL-Stream query rewriting. This technology allows users expressing their queries in SPARQL dialect for streams, while internally uses GSN as Stream management system.

Contact: Jean-Paul Calbimonte, OEG-UPM LSIR

Related Material

• Swiss Experiment
• Semantic Sensor Data Search in a Large Scale Federated Sensor Network (SSN, 2011)

SPITFIRE: Semantic-Service Provisioning for the Internet of Things

The EU FP7 funded SPITFIRE project aims at developing unified concepts, methods, and software infrastructures facilitating the efficient development of applications that span and integrate the Internet and the embedded world. SPITFIRE's results will help to significantly reduce the effort required for development of robust, interoperable, and scalable applications in the Internet of Things. Up to now

• we have investigated on creating useful links between sensor data and the LOD cloud;
• we have eased the semantic annotation phase by both online tools, REST APIs and machine learning techniques to semi-automate this step;
• we have stored the semantic annotations on the sensor themselves and investigated approaches to query them while coping with resource constrained environments. The aim here is to reach a contextualized sensor self-awareness to enable network seamless integration and sensor plug-and-play;
• we have aggregated semantic sensor data into representations of real-world things called Semantic Entities, which enable a more realistic interaction between humans and the sensed objects;
• we have provided RESTful direct access to the Semantic Entities;
• we have defined an ontology that extend the SSN ontology with both network, context and energy related concepts.

The outcomes are all work in progress that we are going to integrate in a consolidated use case for energy-saving in building automation.

Use of SSN: The SSN ontology is used to describe both sensor network devices, their capabilities, the system they are part of, the deployment, sensor observation and measurements.

Contact: Myriam Leggieri, Michael Hausenblas, Manfred Hauswirth.

Related Material

• SPITFIRE: Toward a Semantic Web of Things (IEEE Communication Magazine),
• inContext-Sensing: LOD augmented sensor data (ISWC, 2011).