The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations -the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

No abstract

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations - the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects

Sensor Observation Service (SOS) is a Web service specification defined by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) group in order to standardize the way sensors and sensor data are discovered and accessed on the Web. This standard goes a long way in providing interoperability between repositories of heterogeneous sensor data and applications that use this data. Many of these applications, however, are ill equipped at handling raw sensor data as provided by SOS and require actionable knowledge of the environment in order to be practically useful. There are two approaches to deal with this obstacle, make the applications smarter or make the data smarter. We propose the latter option and accomplish this by leveraging semantic technologies in order to provide and apply more meaningful representation of sensor data. More specifically, we are modeling the domain of sensors and sensor observations in a suite of ontologies, adding semantic annotations to the sensor data, using the ontology models to reason over sensor observations, and extending an open source SOS implementation with our semantic knowledge base. This semantically enabled SOS, or SemSOS, provides the ability to query high-level knowledge of the environment as well as low-level raw sensor data.

No abstract

insight generation, and just about anything that humans, as intelligent beings, seek to do. We've used the term computing for human experience (CHE) 1 to capture technology's human-centric role. CHE emphasizes the unobtrusive, supportive, and assistive part technology plays in improving human experience; here, technology "takes into account the human world and allows computers themselves to disappear in the background." 2 We can distinguish this from Licklider's vision of human-computer collaboration, Eglebert's vision of augmenting human intellect and-more recently-ambient intelligence, and Vannever Bush's and McCarthy's machine-centric vision of making computing more intelligent so that it thinks and behaves like humans.Here, we present an emerging paradigm called physical-cyber-social (PCS) computing. It encompasses a holistic treatment of data, information, and knowledge from the PCS worlds to integrate, correlate, interpret, and provide contextually relevant abstractions to humans. We view PCS as the next phase of computing systems, building on current progress in cyber-physical systems, sociotechnical systems, and cyber-social systems to support CHE. PCS incorporates deeper and richer semantic interdependence and interplay between sensors and devices at physical layers; richer technology-mediated social interactions; and the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning to bridge machine and human perceptions.PCS computing requires that we move away from traditional data processing to multitier computation along the data-information-knowledge-wisdom (DIKW) dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable to humans.We illustrate PCS computing for healthcare with a focus on semantic perception, 3 which converts low-level, heterogeneous, multimodal, and contextually relevant data into higher-level abstractions that can provide insights and assist humans in making complex decisions. Case StudyConsider the case of Ram, a 60-year-old Asian male, who receives a blood pressure screening from his doctor and discovers that the reading is slightly higher than expected (90 diastolic, measured in mmHg). Let's look at two questions that Ram might have: What is the normal blood pressure of an Asian male of his age? What is the best way to manage a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from other people with similar characteristics and demographics (physical). We should also consider the ethnic, social, cultural, and economic background for similarity (social). Moreover, in addition to expert knowledge, the knowledge and experience of similar people dealing with the same health issue are important (cyber). Neither an average doctor nor current cyber-physical systems can answer these questions, but PCS computing can address them in a holistic manner. Patient empowerment and p...

No abstract

The Internet of Things (IoT) has recently received considerable interest from both academia and industry that are working on technologies to develop the future Internet. It is a joint and complex discipline that requires synergetic efforts from several communities such as telecommunication industry, device manufacturers, semantic Web, and informatics and engineering. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities, the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. However, the heterogeneity of the “Things” makes interoperability among them a challenging problem, which prevents generic solutions from being adopted on a global scale. Furthermore, the volume, velocity and volatility of the IoT data impose significant challenges to existing information systems. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and inferring new knowledge together with other intelligent processing techniques. However, the dynamic and resource-constrained nature of the IoT requires special design considerations to be taken into account to effectively apply the semantic technologies on the real world data. In this article the authors review some of the recent developments on applying the semantic technologies to IoT.