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The Web has long exceeded its original purpose of a distributed hypertext system and has become a global, data sharing and processing platform. This development is confirmed by remarkable milestones such as the Semantic Web, Web services, social networks and mashups. In parallel with these developments on the Web, the Internet of Things (IoT), i.e., sensors and actuators, has matured and has become a major scientific and economic driver. Its potential impact cannot be overestimated-for example, in logistics, cities, electricity grids and in our daily life, in the form of sensor-laden mobile phones-and rivals that of the Web itself. While the Web provides ease of use of distributed resources and a sophisticated development and deployment infrastructure, the IoT excels in bringing real-time information from the physical world into the picture. Thus a combination of these players seems to be the natural next step in the development of even more sophisticated systems of systems. While only starting, there is already a significant amount of sensor-generated, or more generally dynamic information, available on the Web. However, this information is not easy to access and process, depends on specialised gateways and requires significant knowledge on the concrete deployments, for example, resource constraints and access protocols. To remedy these problems and draw on the advantages of both sides, we try to make dynamic, online sensor data of any form as easily accessible as resources and data on the Web, by applying well-established Web principles, access and processing methods, thus shielding users and developers from the underlying complexities. In this paper we describe our Linked Stream Middleware (LSM, http://lsm.deri.ie/), which makes it easy to integrate time-dependent data with other Linked Data sources, by enriching both sensor sources and sensor data streams with semantic descriptions, and enabling complex SPARQL-like queries across both dataset types through a novel query processing engine, along with means to mashup the data and process results. Most prominently, LSM provides (1) extensible means for real-time data collection and publishing using a cloud-based infrastructure, (2) a Web interface for data annotation and visualisation, and (3) a SPARQL endpoint for querying unified Linked Stream Data and Linked Data. We describe the system architecture behind LSM, provide details of how Linked Stream Data is generated, and demonstrate the benefits and efficiency of the platform by showcasing some experimental evaluations and the system's interface.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL~2 ontology to describe sensors and observations --- the SSN ontology. 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.

Providing adequate support for context acquisition and management is a non-trivial task that complicates application development. To mitigate this, existing middleware systems rely on a centralized context service that manages the context of all devices located within a certain area. However, in many cases, centralized context management impacts privacy as it requires users to share their context with a possibly untrusted device. In this paper, we show how this problem can be avoided by a peer-based approach to context management. To validate the approach, we have implemented it on top of the BASE middleware and used it extensively in the PECES European research project. The evaluation shows that given a customizable implementation, the approach provides high flexibility while being suitable for a broad spectrum of devices.

The Internet and the World Wide Web have transformed modern life by connecting the real world. With the massive connectivity extension of the Net to a much wider world wide web of the manifold physical objects, we are on the verge of the next evolution of the Internet, so called the Internet of Everything (IoE). However, enabling seamless interoperability is still a biggest challenge towards building the next generation of IoE applications. Believing in Linked Data as a promising solution to address this challenge, we propose the idea of “Linking Everything” by extending Linked Data Principles to interlink “everything” into a hypergraph. Via this book chapter, we present how to make this hypergraph programmable via the emerging Semantic Web technologies.