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Context aware computing for the internet of things: A survey
- Communications Surveys Tutorials, IEEE
, 2014
"... Abstract—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 significant increment of the growth rate in the fu ..."
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Cited by 38 (7 self)
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Abstract—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 significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT. Index Terms—Internet of things, context awareness, sensor networks, sensor data, context life cycle, context reasoning, context modelling, ubiquitous, pervasive, mobile, middleware. I.
The Stimulus-Sensor-Observation Ontology Design Pattern and its Integration into the Semantic Sensor Network Ontology
"... Abstract. This paper presents an overview of ongoing work to develop a generic ontology design pattern for observation-based data on the Semantic Web. The core classes and relationships forming the pattern are discussed in detail and are aligned to the DOLCE foundational ontology to improve semantic ..."
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Cited by 33 (5 self)
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Abstract. This paper presents an overview of ongoing work to develop a generic ontology design pattern for observation-based data on the Semantic Web. The core classes and relationships forming the pattern are discussed in detail and are aligned to the DOLCE foundational ontology to improve semantic interoperability and clarify the underlying ontological commitments. The pattern also forms the top-level of the the Semantic Sensor Network ontology developed by the W3C Semantic Sensor Network Incubator Group. The integration of both ontologies is discussed and directions of further work are pointed out. 1
Five challenges for semantic sensor web
- Semantic Web - Interoperability, Usability, Applicability
, 2010
"... Abstract. The combination of sensor networks with the Web, web services and database technologies, was named some years ago as the Sensor Web or the Sensor Internet. Most efforts in this area focused on the provision of platforms that could be used to build sensor-based applications more efficiently ..."
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Cited by 22 (1 self)
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Abstract. The combination of sensor networks with the Web, web services and database technologies, was named some years ago as the Sensor Web or the Sensor Internet. Most efforts in this area focused on the provision of platforms that could be used to build sensor-based applications more efficiently, considering some of the most important challenges in sensor-based data management and sensor network configuration. The introduction of semantics into these platforms provides the opportu-nity of going a step forward into the understanding, management and use of sensor-based data sources, and this is a topic be-ing explored by ongoing initiatives. In this paper we go through some of the most relevant challenges of the current Sensor Web, and describe some ongoing work and open opportunities for the introduction of semantics in this context.
Observation-driven geo-ontology engineering
- Transaction in GIS
, 2012
"... Big Data, Linked Data, Smart Dust, Digital Earth, and e-Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge ..."
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Cited by 19 (7 self)
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Big Data, Linked Data, Smart Dust, Digital Earth, and e-Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge out of them instead of drowning in information, and how do we make our findings reproducible and reusable by others? In a network of knowledge, topics span across scientific disciplines and the idea of domain ontologies as common agreements seems like an illusion. In this work, we argue that these trends require a radical paradigm shift in ontology engineering away from a small number of authoritative, global ontologies developed top-down, to a high number of local ontologies that are driven by application needs and developed bottom-up out of observation data. Similarly as the early Web was replaced by a social Web in which volunteers produce data instead of purely consuming it, the next generation of knowledge infrastructures has to enable users to become knowledge engineers themselves. Surprisingly, existing ontology engineering frameworks are not well suited for this new perspective. Hence, we propose an observation-driven ontology engineering framework, show how its layers can be realized using specific methodologies, and relate the framework to existing work on geo-ontologies. 1
Enabling Query Technologies for the Semantic Sensor Web
"... Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which t ..."
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Cited by 17 (7 self)
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Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes. Keywords:
Reasoning about Sensors and Compositions
- in: 2nd International Semantic Sensor Networks Workshop, CEUR-WS
"... Abstract. This paper discusses an OWL ontology for specifying sensors. The ontology is intended as the basis for the semantic representation of sensors and as the formal description for reasoning about sensors and observations. The paper describes the ontology, presents two example sensor descriptio ..."
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Cited by 17 (4 self)
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Abstract. This paper discusses an OWL ontology for specifying sensors. The ontology is intended as the basis for the semantic representation of sensors and as the formal description for reasoning about sensors and observations. The paper describes the ontology, presents two example sensor descriptions and shows how standard reasoning and querying techniques can be used to perform tasks including classification and composition. In conjunction with the technical material the trade-offs required to express complex material in OWL is also discussed. 1
A RESTful proxy and data model for linked sensor data
- International Journal of Digital Earth
, 2011
"... The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facil ..."
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Cited by 15 (6 self)
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The vision of a Digital Earth calls for more dynamic information systems, new sources of information, and stronger capabilities for their integration. Sensor networks have been identified as a major information source for the Digital Earth, while Semantic Web technologies have been proposed to facilitate integration.. So far, sensor data is stored and published using the Observations & Measurements standard of the Open Geospatial Consortium (OGC) as data model. With the advent of Volunteered Geographic Information and the Semantic Sensor Web, work on an ontological model gained importance within Sensor Web Enablement (SWE). In contrast to data models, an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain. Ontologies restrict the interpretation of vocabularies towards their intended meaning. The ongoing paradigm shift to Linked Sensor Data complements this attempt. Two questions have to be addressed: (i) how to refer to changing and frequently updated data sets using Uniform Resource Identifiers, and (ii) how to establish meaningful links between those data sets, i.e., observations, sensors, features of interest, and observed properties? In this paper, we present a Linked Data model and a RESTful proxy for OGC’s Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.
Towards semantic robot description languages
- In IEEE International Conference on Robotics and Automation (ICRA
, 2011
"... Abstract — There is a semantic gap between simple but high-level action instructions like “Pick up the cup with the right hand ” and low-level robot descriptions that model, for example, the structure and kinematics of a robot’s manipulator. instructions to parametrized algorithms and rigid body par ..."
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Cited by 15 (9 self)
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Abstract — There is a semantic gap between simple but high-level action instructions like “Pick up the cup with the right hand ” and low-level robot descriptions that model, for example, the structure and kinematics of a robot’s manipulator. instructions to parametrized algorithms and rigid body parts of a robot within their control programs. By linking descriptions of robot components, i.e. sensors, actuators and control programs, via capabilities to actions in an ontology we equip robots with knowledge about themselves that allows them to infer the required components for performing a given action. Thereby a robot that is instructed by an end-user, a programmer, or even another robot to perform a certain action, can assess itself whether it is able and how to perform the requested action. This self-knowledge for robots could considerably change the way of robot control, robot interaction, robot programming, and multi-robot communication. I.
Semantics for the internet of things: Early progress and back to the future
- International Journal on Semantic Web & Information Systems
, 2012
"... 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, ..."
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Cited by 13 (2 self)
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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, informatics and engineering, among many others. Much of the IoT initiative is supported by the capabilities of manufacturing low-cost and energy-efficient hardware for devices with communication capacities (e.g., sensors and RFID tags), the maturity of wireless sensor network technologies, and the interests in integrating the physical and cyber worlds. IoT consists of interconnected “Things ” and their virtual representations addressable by using standard communication protocols. 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. The semantic Web community has worked on combining knowledge engineering and AI techniques to represent, integrate, and reason upon data and knowledge in the past decades. Semantic technologies based on machine-interpretable representation formalism have shown promise for describing objects, sharing and integrating information, and infering new knowledge together with other intelligent processing techniques. The addition of semantics has also helped create machine-interpretable and self-descriptive data in the IoT domain. 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 we review some of the recent developments on applying the semantic technologies to IoT – in particular, information modeling, ontology design, and processing of semantic data – and discuss the challenges. 1.
