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This paper examines the feasibility of using ontologies to model generic sensor networks, based on the capabilities of the current generation of ontology tools. The creation of such an ontology, the current tool's capacity to adequately maintain it, and its potential functionality, as part of a larger semantic system are addressed here. These topics were addressed by constructing a generic sensor ontology and attempting to implement it as part of a larger semantic system. The process and the conclusions drawn from it are described below.
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 OGC's 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.
When searching for sensor data, sensor instances, or Sensor Web Enablement (SWE) services the description of the observed phenomenon plays an important role. Obviously, every user searching for sensor data needs to specify in which kind of sensor data he is interested. In current SWE applications, the information about the observed phenomenon is provided as a unique link encoded as a Uniform Resource Name (URN). However, relying on those URNs to perform string based search for sensor observables has serious drawbacks when it comes to realizing advanced sensor discovery tools as the meaning of the observables is ignored. This work presents an approach that makes use of semantic annotations of SWE resources. The presented solution relies on a dictionary for sensor observables, the Sensor Observable Registry (SOR). This dictionary comprises URNs identifying observables, definitions of these observables in natural language, and pointers to formal phenomenon definitions contained in ontologies. This makes it possible to rely on existing reasoning mechanisms for determining equivalent or related observables (e.g., specializations or generalizations) to the one specified by a user. Finally, an approach is presented, how the SOR can be used for enhancing the sensor discovery process by linking it to sensor catalogues and registries.
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.
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.
We propose that a semantic service-oriented approach is one of the best techniques to cope with challenges in wireless sensor network (WSN) applications. This paper offers a framework for sensor network services that aims to improve query processing. We expect this framework will address current challenges and issues preventing the wider uptake of WSN technology. More specifically, we propose a semantic service-oriented framework with a focus on query processing to allow distributed end-users to request streams of interest easily and efficiently, based on the principle of pushing the query down to the network nodes as much as possible. As such, the lifetime and utility of the sensor network will be maximised, ultimately leading to the success of WSN deployments. The importance of semantics, which aims to support sensor capability modelling and query writing has been highlighted. On the other hand, query rewriting is emphasised followed by examples to illustrate that query rewriting can significantly contribute to the overall power efficiency of WSNs.
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.
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.
Wireless sensor networks(WSNs) have been increasingly available for large-scale applications in which energy efficiency is an important performance measure. These applications include environmental monitoring and structure monitoring which demand multifarious data. Driven by the energy limitation nature of WSNs lots of research works have been done in aspects such as nodes deployment, routing protocol, topology control, data reduction, sleep scheduling, etc. However, heterogeneous, i.e. hybrid sensor nodes are combined together into semantic sensor networks to provide large-scale applications with content rich information. In this paper, we discuss the potential of energy efficiency that semantization could bring to sensor networks. First we have an overview of some related work and then address current approaches of energy conservation in WSNs as well as how semantization can contribute in each aspect of saving energy. Finally a recommendatory architecture of semantic sensor network is proposed. Semantization will be a promising solution to improve energy efficiency together with system performance.
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.
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.
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.
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.