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Special Issue "Semantics for Sensors, Networks and Things"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 November 2019).

Special Issue Editors

Dr. Danh Le Phuoc
E-Mail Website
Guest Editor
TU Berlin,10623 Berlin, Germany
Dr. Arne Bröring
E-Mail Website
Guest Editor
Siemens AG — Corporate Technology, Munich, Germany
Interests: internet of things; semantic web technologies; the sensor web; participatory sensing; as well as mobile and location based services
Dr. Jeff Z. Pan
E-Mail Website
Guest Editor
Department of Computing Science, The University of Aberdeen, AB24 3UE Aberdeen, UK
Interests: artificial intelligence; knowledge graph; approximate reasoning; learning and reasoning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Semantics is becoming an appealing instrument for enabling the interoperability and composability at high-level information abstraction for sensors and networks in the Internet of Things/Everything. There have been several disconnected research communities and standardisation bodies that have proposed various semantic-based approaches in the interest of sharing common understandings among human (users, knowledge engineers, developers, etc.) and physical things (sensors, networks, things, etc.). The most active of these is Semantic Web, which advocates the use of ontologies and knowledge graphs (backed by Description Logics, Datalog, etc.) as core ingredients for modelling data as well as network and system design. Interestingly, the experts in sensor network and networking technologies also proposed the use of logic programming such as Prolog and Datalog for better abstractions of data, constraints, and system compositions. In parallel, sensor fusion and knowledge fusion seem to be the big topics of many communities, as they will be considerably beneficial for semantic abstractions (ontologies or taxonomies such as W3C/OGC Sensor Network Ontology, Thing Description Ontology of W3C Web of Things, ETSI OneM2M Ontology, Schema.org) in association with emerging achievements in building knowledge graphs. At the system perspective, the pervasive and autonomous computing community has shown interest in using ontologies for modelling contextual information, and now this community is coming back with new incarnations of edge/fog computing and autonomous systems (autonomous vehicles, Industry 4.0, smart cities, large-scale cyber-physical-systems) which need semantic abstractions as the decoupling design paradigm in order to enable dynamic integration in real time.

In this context, we welcome submissions from any area that touches a subset of the aforementioned aspects on semantics in sensors, networks, multimedia objects, and things in general. Beyond usual research and survey papers, the submission of papers that apply novel approaches in industry use cases are highly welcome. These papers should demonstrate validated (measureable) advantages of the applied approach in comparison to current industry solutions (if any) or clearly showcase the gained advantages in real-world setups. We also encourage the submission of papers that discuss benefits and challenges of large-scale pilots and industrial-grade testbeds. Applications may lay in domains such as (but not limited to) smart cities, environmental monitoring, manufacturing (Industry 4.0), mobility and automotive, building automation, eHealth, and energy distribution.

Dr. Danh Le Phuoc
Dr. Arne Bröring
Dr. Jeff Z. Pan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

Article
Pushing the Scalability of RDF Engines on IoT Edge Devices
Sensors 2020, 20(10), 2788; https://doi.org/10.3390/s20102788 - 14 May 2020
Cited by 1 | Viewed by 1129
Abstract
Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be [...] Read more.
Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J. Full article
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
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Article
A Dynamic Dashboarding Application for Fleet Monitoring Using Semantic Web of Things Technologies
Sensors 2020, 20(4), 1152; https://doi.org/10.3390/s20041152 - 20 Feb 2020
Cited by 3 | Viewed by 1256
Abstract
In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need [...] Read more.
In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored—these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early. Full article
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
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Article
Networking-Aware IoT Application Development
Sensors 2020, 20(3), 897; https://doi.org/10.3390/s20030897 - 07 Feb 2020
Cited by 2 | Viewed by 1503
Abstract
Various tools support developers in the creation of IoT applications. In general, such tools focus on the business logic, which is important for application development, however, for IoT applications in particular, it is crucial to consider the network, as they are intrinsically based [...] Read more.
Various tools support developers in the creation of IoT applications. In general, such tools focus on the business logic, which is important for application development, however, for IoT applications in particular, it is crucial to consider the network, as they are intrinsically based on interconnected devices and services. IoT application developers do not have in depth expertise in configuring networks and physical connections between devices. Hence, approaches are required that automatically deduct these configurations. We address this challenge in this work with an architecture and associated data models that enable networking-aware IoT application development. We evaluate our approach in the context of an application for oil leakage detection in wind turbines. Full article
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
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Article
eWoT: A Semantic Interoperability Approach for Heterogeneous IoT Ecosystems Based on the Web of Things
Sensors 2020, 20(3), 822; https://doi.org/10.3390/s20030822 - 04 Feb 2020
Cited by 4 | Viewed by 1966
Abstract
With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of [...] Read more.
With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in large ecosystems. Full article
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
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Article
Real-Time Compliant Stream Processing Agents for Physical Rehabilitation
Sensors 2020, 20(3), 746; https://doi.org/10.3390/s20030746 - 29 Jan 2020
Cited by 5 | Viewed by 1205
Abstract
Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate [...] Read more.
Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines. Full article
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
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Article
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data
Sensors 2019, 19(20), 4362; https://doi.org/10.3390/s19204362 - 09 Oct 2019
Cited by 2 | Viewed by 1442
Abstract
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches [...] Read more.
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context. Full article
(This article belongs to the Special Issue Semantics for Sensors, Networks and Things)
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