Michael Compton

Ontology paper: The SSN ontology of the W3C semantic sensor network incubator group

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 Search Techniques for Sensing as a Service Architecture for the Internet of Things

The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating “things” or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.

Farming the Web of Things

Here, a Web of Things case study for agriculture focuses on an experimental smart farm that uses a range of environmental sensors and livestock monitoring technologies. An ontology-enabled architecture that permits personal alert specification was applied on the farm and the results analyzed; the observations are published as a linked data cube, which supports longer-term analysis and data sharing from a local to national scale. This case study highlights the business opportunities for such technology for agriculture in the context of a national broadband rollout.

A model for efficient and accurate interaction with elastic objects in haptic virtual environments

This paper describes a method of modelling real-time interactions with elastic 3D objects represented by finite element models, which is particularly suitable for haptic virtual environments. The assumption we make is that the area of interaction of the external forces on the object is small. Our method provides a physically based solution and only requires the precomputation of the inverse of the stiffness matrix. It can be naturally coupled with a technique of local multiresolution collision detection, in order to increase geometrical accuracy while maintaining a low cost computation.Our model shows that under reasonable constraints, it is possible to meet the generally hard to reconcile requirements of having both a real-time and physically accurate simulation in a haptic virtual environment.

Semantic-Driven Configuration of Internet of Things Middleware

We are currently observing emerging solutions to enable the Internet of Things (IoT). Efficient and feature rich IoT middeware platforms are key enablers for IoT. However, due to complexity, most of these middleware platforms are designed to be used by IT experts. In this paper, we propose a semantics-driven model that allows non-IT experts (e.g. plant scientist, city planner) to configure IoT middleware components easier and faster. Such tools allow them to retrieve the data they want without knowing the underlying technical details of the sensors and the data processing components. We propose a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of automated context-aware configuration of filtering, fusion, and reasoning mechanisms in IoT middleware according to the problems at hand. We incorporate semantic technologies in solving the above challenges. We demonstrate the feasibility and the scalability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized into any other middleware platform. We evaluate CASCoM in agriculture domain and measure both performance in terms of usability and computational complexity.

A semantic approach to data translation

We propose an ontology-mediated approach for environmental data translation.We outline the principles underlying the design of a mediating ontology, and show the development of such an ontology.We propose a declarative formalism for representing spreadsheet-to-ontology mappings, and give an algorithm for the mapping evaluation.We propose a declarative formalism for representing ontology-to-XML mappings, and give an algorithm for the mapping evaluation.We have developed an ontology-mediated spreadsheet-to-XML translation tool, and showed its effectiveness with real environmental observations data. To facilitate the exchange of environmental observations, efforts have been made to develop standardised markup languages for describing and transmitting data from multiple sources. Along with this is often a need to translate data from different formats or vocabularies to these languages. In this paper, we focus on the problem of translating data encoded in spreadsheets to an XML-based standardised exchange language. We describe the issues with data that have to be resolved. We present a solution that relies on an ontology capturing semantic gaps between data and the target language. We show how to develop such an ontology and use it to mediate translation through a real scenario where water resources data have to be translated to a standard data transfer format. In particular, we provide declarative mapping formalisms for representing relationships between spreadsheets, ontologies, and XML schemas, and give algorithms for processing mappings. We have implemented our approach in AdHoc, an ontology-mediated spreadsheet-to-XML translation tool, and showed its effectiveness with real environmental observations data.

Towards Content-Aware SPARQL Query Caching for Semantic Web Applications

Applications are increasingly using triple stores as persistence backends, and accessing large amounts of data through SPARQL endpoints. To improve query performance, this paper presents an approach that reuses results of cached queries in a content-aware way for answering subsequent queries. With a focus on a common class of conjunctive SPARQL queries with filter conditions, not only does the paper provide an efficient method for testing whether a query can be evaluated on the result of a cached query, but it also shows how to evaluate the query. Experimental results show the effectiveness of the approach.

Role-based access control for data service integration

We describe the implementation of role-based access control in a data service integration system. Users in research or other projects may access a diverse collection of data sources but are to allowed access to only the part of the data collection that is necessary for their purposes. To simplify the administration of the access control, Role Based Access control is used, with the role hierarchy defined within and limited to each project. User queries to the integration system are analysed for their data access needs and those needs checked against the access control policies. The policies for the data held by individual data custodians can be managed and implemented by the custodian, or held in a central authorisation server in the integration system. The system is built around the Security Assertion Markup Language and eXtensible Access Control Markup Language standards. The access control architecture was developed for a health data integration system, but both the architecture and some of its components for authentication and authorisation could be readily reused in other similar systems.

A Use Case in Semantic Modelling and Ranking for the Sensor Web

Agricultural decision support systems are an important application of real-time sensing and environmental monitoring. With the continuing increase in the number of sensors deployed, selecting sensors that are fit for purpose is a growing challenge. Ontologies that represent sensors and observations can form the basis for semantic sensor data infrastructures. Such ontologies may help to cope with the problems of sensor discovery, data integration, and re-use, but need to be used in conjunction with algorithms for sensor selection and ranking. This paper describes a method for selecting and ranking sensors based on the requirements of predictive models. It discusses a Viticulture use case that demonstrates the complexity of semantic modelling and reasoning for the automated ranking of sensors according to the requirements on environmental variables as input to predictive analytical models. The quality of the ranking is validated against the quality of outputs of a predictive model using different sensors.