Developments in (wireless) sensor and actuator networks and the capabilities to manufacture low cost and energy efficient networked embedded devices have lead to considerable interest in adding real world sense to the Internet and the Web. Recent work has raised the idea towards combining the Internet of Things (i.e. real world resources) with semantic Web technologies to design future service and applications for the Web. In this paper we focus on the current developments and discussions on designing Semantic Sensor Web, particularly, we advocate the idea of semantic annotation with the existing authoritative data published on the semantic Web. Through illustrative examples, we demonstrate how rule-based reasoning can be performed over the sensor observation and measurement data and linked data to derive additional or approximate knowledge. Furthermore, we discuss the association between sensor data, the semantic Web, and the social Web which enable construction of context-aware applications and services, and contribute to construction of a networked knowledge framework.

We describe a semantic data validation tool that is capable of observing incoming real-time sensor data and performing reasoning against a set of rules specific to the scientific domain to which the data belongs. Our software solution can produce a variety of different outcomes when a data anomaly or unexpected event is detected, ranging from simple flagging of data points, to data augmentation, to validation of proposed hypotheses that could explain the phenomenon. Hosted on the Jena Semantic Web Framework, the tool is completely domain-agnostic and is made domain-aware by reference to an ontology and Knowledge Base (KB) that together describe the key resources of the system being observed. The KB comprises ontologies for the sensor packages and for the domain; historical data from the network; concepts designed to guide discovery of internet resources unavailable in the local KB but relevant to reasoning about the anomaly; and a set of rules that represent domain expert knowledge of constraints on data from different kinds of instruments as well as rules that relate types of ecosystem events to properties of the ecosystem. We describe an instance of such a system that includes a sensor ontology, some rules describing coastal storm events and their consequences, and how we relate local data to external resources. We describe in some detail how a specific actual eventan unusually high chlorophyll readingcan be deduced by machine reasoning to be consistent with being caused by benthic diatom resuspension, consistent with being caused by an algal bloom, or both.

This paper presents a practical approach to developing comprehensive sensor ontologies based upon deep knowledge models rather than capturing only superficial sensor attributes. It is proposed that the representation and utilization of deep sensor ontologies would enable a variety of sensor information system applications including sensor parts compatibility determination, dynamic sensor selection and tasking, and reasoning about systems of sensors in which data must be fused and queried from a variety of sensor types within a myriad of environments.

Modern smart buildings utilize sensor networks for facilities management applications such as energy monitoring. However as buildings become progressively more embedded with sensor networks, the challenge of managing and maintaining the sensor networks themselves becomes ever more significant. As a cost-sensitive activity, facilities management deployments are less likely to deploy node redundancy and specialized technical staff to maintain these networks. Hence there are strong requirements for the network to efficiently self-diagnose, self-heal and integrate with standard buildings management systems. This paper introduces a solution for WSN management in smart buildings that addresses these issues. It is based on the deployment of the open framework middleware for sensor networks coupled with a structured knowledge and rule-based fault analysis engine to perform network event correlation and root cause analysis. The system also explicitly interfaces with a building management system (BMS) or the scheduling of network maintenance activities such as sensor battery replacement.

The understanding of complex environmental phenomena, such as deforestation and epidemics, requires observations at multiple scales. This scale dependency is not handled well by today's 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.

It is important to provide adaptive data processing in wireless sensor networks in order to deal with various applications. In this paper, we propose a WIreless Sensor Networks Ontology (WISNO) for flexible modeling of sensor data. WISNO contains two-tier ontologies, a front-end for coarse-grained analysis and a back-end for high-level fine-grained data processing. We also describes the WISNO reasoning rules that adopts description logic and SWRL for managing data automatically.

In this paper, we present our work towards the development and evaluation of an ontology for searching distributed and heterogeneous sensor networks data. In particular, we propose a two layer prototype ontology that utilizes the IEEE Suggested Upper Merged Ontology (SUMO) as a root definition of general concepts and associations and two sub- ontologies: the sensor data sub-ontology and the sensor hierarchy sub-ontology. The proposed ontology was implemented using Protege 2000 and eventually evaluated using the RDQL language (RDF Data Query Language). The performance analysis demonstrated the ability of the ontology-based search to improve both the precision and recall rates and enhance the interoperability between different sensor networks domains through the use of the universal SUMO ontology.

Wireless sensor networks (WSN) generate large volumes of raw data which possess natural heterogeneity. WSNs are normally application specific with no sharing or reusability of sensor data among applications. In order for applications and services to be developed independently of particular WSNs, sensor data need to be enriched with semantic information. In this paper, we propose a semantic Web architecture for sensor networks (SWASN). This information oriented architecture allows the sensor data to be understood and processed in a meaningful way by a variety of applications with different purposes. We develop ontologies for sensor data and use the Jena API for processing which includes querying and inference over sensor data. By studying a building fire emergency scenario, we show that semantic Web technologies can provide high level information extraction and inference of sensor data.

In this paper, we describe a meta-framework that helps guide development of sensor network (SN) cyberinfrastructure in a way that enables emerging sensor infrastructures, including advances in sensor hardware, communication, monitoring applications, and knowledge representation, to interoperate. This framework is guided by the DAST principle. That is, the overall goal of any SN infrastructure is essentially the same: to acquire the right Data from the right Area using the right Sensors at the right Time. In conformity with this principle, our meta-framework integrates SN infrastructures along axes related to the answers to five questions: Why has processing been requested? What are the goals of the processing? Where is it carried out? How is it carried out? And, when will the results be provided? The infrastructure components are integrated by using various data standards and technologies currently available from various SN research groups, and mapping them to an overarching knowledge-based meta-framework. In concrete terms, we show in this paper how four distinct sensor technology projects under development in our research lab are used to fit these five axes of SN infrastructure and how they can be indirectly integrated through the use of software agent-based tools, which embody the meta-framework: an ontology-based decision support system that applies models of SN infrastructure to its evaluation techniques; SN configuration tools that enable network configurations to be exported into common geospatial standards; a transformation engine that converts these SN configurations, along with collected data, into a representation that meshes with our infrastructure models so that they may be used within our decision support environment; and a Virtual SN to handle many of the management and control aspects of SNs.

In this paper we present a proposal that combines the benefits of autonomic and semantic sensor networks to build a semantic middleware for autonomic wireless sensor networks. The key feature of the proposed middleware is a rule-based reasoning engine based on ontology and fuzzy logic. We also propose a semantic-aware topology control based on computing semantic neighborhoods relationships. The middleware was tailored to provide support for Structural Health Monitoring applications. However, it has a flexible architecture and it can be extensible to several other application domains such as ambient intelligence, habitat monitoring and fire detection. We use the oil platform structural health monitoring domain as a case study. The paper presents the middleware architecture and the proposed ontologies.