Search

The Internet of Things (IoT) primary objective is to make a hyper-connected world for various application domains. However, IoT suffers from a lack of interoperability leading to a substantial threat to the predicted economic value. Schema.org provides semantic interoperability to structure heterogeneous data on the Web. An extension of this vocabulary for the IoT domain (iot.schema.org) is an ongoing research effort to address semantic interoperability for the Web of Things (WoT). To design this vocabulary, a central challenge is to identify the main topics (concepts and properties) automatically from existing knowledge in IoT applications. We designed knowledge extraction for the WoT (KE4WoT) to automatically identify the most important topics from literature ontologies of three different IoT application domains: 1) smart home; 2) smart city; and 3) smart weather—based on our corpus consisting of 4500 full-text conference and journal articles to utilize domain-specific knowledge encoded within IoT publications. Despite the importance of automatically identifying the relevant topics for iot.schema.org, up to know there is no study dealing with this issue. To evaluate the extracted topics, we compare the descriptiveness of these topics for the ten most popular ontologies in the three domains with empirical evaluations of 23 domain experts. The results illustrate that the identified main topics of IoT ontologies can be used to sufficiently describe existing ontologies as keywords.

Modern cities are becoming “green and sustainable” once effective and optimized energy management of resources, gathering, and integration of data from smart energy and building are executed. The integration of heterogeneous technologies, and devices, require an interoperable solution to describe devices and data exchanged.SAREF is an ontology supported by the ETSI SmartM2M standard to achieve interoperability among IoT projects, architectures, etc. that can be extended to any IoT vertical domains such as smart buildings or energy. SAREF can be used to describe data sent through communication protocols or once that data must be processed (e.g., on the cloud, gateways, devices). ETSI does not provide tools yet that support the SAREF ontology to avoid engineers to develop from scratch. We design a SAREF-compliant sensor dictionary which also overcomes SAREF limitations. The sensor dictionary, applied to energy scenarios, is employed by a reasoner to infer meaningful knowledge from sensor data. Online demonstrators are available. The scenarios are also relevant for the Interconnect European-funded project that comprises 50 partners to design interoperable smart building and grids.

How to understand better the knowledge provided by Google results to build future “smart vehicle-centric” applications? What is the knowledge expertise required to build a smart vehicle application (e.g., driver assistance system)? Automotive companies (e.g., Toyota, BMW, Renault) are employing Internet of Things (IoT) and Semantic Web technologies to model the automotive sector. We aggregate this “common sense knowledge” in a automotive dataset which comprises 42 semantics-based projects between 2005 and 2019. The knowledge is already encoded with knowledge representation languages (e.g., RDF, RDFS, and OWL) and supported by the World Wide Web Consortium (W3C). However, only a subset of those projects share their expertise by publishing their ontologies online. For this reason, at the current time or writing, only 16 ontologies are processable. Our innovative Knowledge Extraction for the Automotive Sector (KEAS) methodology analyzes what are the most popular terms required to build a smart car, it provides: (1) a set of keyphrase that are synonyms to smart cars to find domain-specific knowledge, (2) synonyms are used to build a corpus of scientific publications to train the k-means machine learning algorithm, (3) a dataset of smart car ontologies that we collected, is analyzed by the k-means algorithm, and (4) the extraction of the most common terms from the ontology dataset for the automotive sector. Our KEAS findings can be used as a starting point for further domain-specific investigations (e.g., Volvo willing to integrate semantic web) and for future information extraction from structured knowledge.

Semantic IoT: Theory and Applicationspp 161–192Cite as PerfectO: An Online Toolkit for Improving Quality, Accessibility, andClassification of Domain-Based Ontologies Amélie Gyrard, Ghislain Atemezing& Martin Serrano Chapter First Online:13 April 2021 431Accesses 2Altmetric Part of theStudies in Computational Intelligencebook series (SCI,volume 941) Abstract Sensor-based applications are increasingly present in our everyday life. Due to the enormous quantity of sensor data produced, interpreting data and building interoperable sensor-based applications is needed. There are several problems to address the heterogeneity of (1) data format, (2) languages to describe sensor metadata, (3) models for structuring sensor datasets, (4) reasoning mechanisms and rule languages to interpret sensor datasets, and (5) applications. Semantic Web technologies (a.k.a, knowledge graphs), are immersed in an increasing number of online activities we perform today (e.g., search engines for gathering information). There is a need to find better ways to share data and distribute more meaningful and more accurate information. Innovative methodologies are needed to link and associate the data from different domains to improve knowledge discovery. Semantic knowledge graphs, made of datasets and ontologies, are intended to describe and organize heterogeneous data explicitly. If an ontology is widely used to structure data of a particular domain, the accessibility and the efficiency in sharing and reusing that information will increase. For this reason, we focused on theontology qualityused when building sensor-based applications. We designedPerfectO, aKnowledge Directory Servicestool, focusing on ontology best practices, which: (1) improves knowledge quality, (2) leverages usability, accessibility, and classification of the information, (3) enhances engineering experience, and (4) promotes engineering best practices.PerfectOimplementation is applied to the Internet of Things (IoT) domain because it covers more than 20 application domains (e.g., healthcare, smart building, smart farm) that use sensors.PerfectOenhances knowledge expertise quality implemented within any ontologies as demonstrated with the Linked Open Vocabularies for IoT (LOV4IoT) ontology catalog.

International audience; The Internet of Things (IoT) plays an ever-increasing role in enabling smart city applications. An ontology-based semantic approach can help improve interoperability between a variety of IoT-generated as well as complementary data needed to drive these applications. While multiple ontology catalogs exist, using them for IoT and smart city applications require significant amount of work. In this paper, we demonstrate how can ontology catalogs be more effectively used to design and develop smart city applications? We consider four ontology catalogs that are relevant for IoT and smart cities: 1) READY4SmartCities; 2) linked open vocabulary (LOV); 3) OpenSensingCity (OSC); and 4) LOVs for IoT (LOV4IoT). To support semantic interoperability with the reuse of ontology-based smart city applications, we present a methodology to enrich ontology catalogs with those ontologies. Our methodology is generic enough to be applied to any other domains as is demonstrated by its adoption by OSC and LOV4IoT ontology catalogs. Researchers and developers have completed a survey-based evaluation of the LOV4IoT catalog. The usefulness of ontology catalogs ascertained through this evaluation has encouraged their ongoing growth and maintenance. The quality of IoT and smart city ontologies have been evaluated to improve the ontology catalog quality. We also share the lessons learned regarding ontology best practices and provide suggestions for ontology improvements with a set of software tools.

Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain., This work was partially funded by the following agencies: Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center EASE (1320); Regional Catalan Agency ACCIO through the RIS3CAT 2016 project SIMBIOTS (COMRDI16-1-0017); Spanish State ´ Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656); Spanish Government through the project DPI2016-80077-R and grant 2017; FCT, through IDMEC, under LAETA, project UID/EMS/50022/2019; Project 0043- EUROAGE-4-E (POCTEP Programa Interreg V-A Spain-Portugal). J. Olszewska was partially supported by Innovate UK.

The current Internet of Things (IoT) ecosystem consists of non-interoperable products and services. The Web of Things (WoT) advances the IoT by allowing consumers to interact with the IoT ecosystem through the open and standard web technologies. But the Web alone does not solve the interoperability issues. It is widely acknowledged that Semantic Web Technologies hold the potential of achieving data and platform interoperability in both the IoT and WoT landscapes. In this context, the paper attempts to review and analyze the current state of ontology-based software tools for semantic interoperability.

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/.

An ever growing interest and wide adoption of Internet of Things (IoT) and Web technologies are unleashing a true potential of designing a broad range of high-quality consumer applications. Smart cities, smart buildings, and e-health are among various application domains which are currently benefiting and will continue to benefit from IoT and Web technologies in a foreseeable future. Similarly, semantic technologies have proven their effectiveness in various domains and a few among multiple challenges which semantic Web technologies are addressing are to (i) mitigate heterogeneity by providing semantic inter-operability, (ii) facilitate easy integration of data application, (iii) deduce and extract new knowledge to build applications providing smart solutions, and (iv) facilitate inter-operability among various data processes including representation, management and storage of data. In this tutorial, our focus will be on the combination of Web technologies, Semantic Web, and IoT technologies and we will present to our audience that how a merger of these technologies is leading towards an evolution from IoT to Web of Things (WoT) to Semantic Web of Things. This tutorial will introduce the basics of Internet of Things, Web of Things and Semantic Web and will demonstrate tools and techniques designed to enable the rapid development of semantics-based Web of Things applications. One key aspect of this tutorial is to familiarize its audience with the open source tools designed by different semantic Web, IoT and WoT based projects and provide the audience a rich hands-on experience to use these tools and build smart applications with minimal efforts. Thus, reducing the learning curve to its maximum. We will showcase real-world use case scenarios which are designed using semantically-enabled WoT frameworks (e.g. CityPulse, FIESTA-IoT and M3).

After a thorough analysis of existing Internet of Things (IoT) related ontologies, in this paper we propose a holistic and a light-weight ontology that aims to achieve semantic interoperability among various fragmented testbeds (that store data in their proprietary format) in IoT domain. Such ontology currently finds its implementation in the EU H2020 FIESTA-IoT project that aims to provide federation and interoperability to testbeds using semantics. The proposed ontology leverages from the Noy et al. methodology of building ontology. With the aim of including more ontologies in the future, if needed, our ontology currently reuses core concepts from various popular ontologies and taxonomies, such as Semantic Sensor Network (SSN), M3-lite (a lite version of M3 and also an outcome of this study), WGS84, IoT-lite and DUL. In addition to this, we also introduce a set of tools that aims to help external testbeds adapt their respective datasets to the common ontology defined throughout this paper. This work is partially funded by the European project \Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT)" from the European Union's Horizon 2020 Programme with the Grant Agreement No. CNECTICT-643943. The Authors would like to also thank the FIESTA-IoT consortium for fruitful discussions.

Ontology-based applications are becoming more and more popular and are usually domain-specific (e.g., eHealth or domotic). Designing ontologies and semantic-based applications manually is tedious and cumbersome for non-technical expert or semantic web beginners. Internet of Things (IoT) is a new field aiming to connect the physical world surrounded by devices such as sensors to the web to automatically interact with them and build innovative applications. The main challenges are to automate as much as possible the tasks of: (1) reusing the background knowledge previously designed by domain experts, (2) facilitating the tasks of IoT developers willing to integrate semantic web technologies into their applications, and (3) designing interoperable semantic-based IoT applications. Stemming from Linked Open Data and Linked Open Vocabularies, we designed Linked Open Vocabularies for Internet of Things (LOV4IoT), a catalogue of ontologies/datasets/rules relevant for IoT available online. LOV4IoT has been extended with more domains and ontology-based projects, a semantic-based dataset and a bot to enhance automation, and web services. Moreover, we demonstrate several use cases of the LOV4IoT dataset: (1) building Semantic Web of Things applications, (2) extracting frequent terms used in existing ontologies, and (3) stakeholders who can exploit, reuse and combine domain ontologies. Finally, we evaluated this dataset with users who exploit this dataset for their own purposes.

Semantic Web of Things is a new field combining Semantic Web and Internet of Things technologies to be surrounded by smart objects and applications connected to the Web. On one hand, one of the Linked Open Data applications, called DataHub aims at referencing datasets, on the other hand, the Linked Open Vocabularies (LOV) references more than 400 ontologies. However, we discovered that more than 200 ontology-based projects relevant for IoT are not referenced on such tools since domain experts are not aware of them nor of the semantic web best practices. We propose the Machine-to-Machine Measurement (M3) framework, available online, to rapidly design and develop semantic-based cross-domain IoT applications by reusing as much as possible the domain knowledge (ontologies, datasets and rules). To achieve this goal, there are challenging steps: (1) referencing and classifying semantic-based projects relevant for IoT, (2) re-engineering a dataset of interoperable domain rules to deduce high-level abstractions from sensor data, (3) re-engineering an interoperable cross-domain knowledge to combine domains, and (4) assisting developers in designing IoT applications by designing pre-defined templates. In this article, we are focused on referencing and classifying semantic-based projects relevant for IoT by designing the Linked Open Vocabularies for Internet of Things (LOV4IoT) dataset. We also design a dataset of interoperable domain rules to deduce high-level abstractions from sensor data by designing the Sensor-based Linked Open Rules (S-LOR). This work has been applied to two uses cases: (1) redesigning a security and cross-domain knowledge base to assist users in suggesting security mechanisms to secure their applications, and (2) designing semantic-based IoT applications embedded in Android-powered devices.

The Internet of Things (IoT) is driving the technology and advances of the Internet and every day it is becoming more popular to talk about IoT systems in multiple domains: Smart Cities, Agriculture, and Industrial Internet, etc. Increasingly, IoT systems will need to interact and be interconnected for offering the always-promoted everything-connected paradigm. Current IoT systems rely on semantic web technologies for integrating data and ensure web services interoperability. However there are yet a gap to ensure semantic interoperability among IoT systems. Most of the existing proposed (open) approaches and solutions lack on formal methodologies for interoperability in technology and standard format of the data. We studied and analyzed most available semantic-based IoT approaches to identify the main requirements hindering IoT semantic interoperability. In this paper, we present SEG 3.0 a methodology to federate, unify and provide semantic interoperability. SEG 3.0 emerges from methodologies for ontology engineering and the idea of unification and federated systems. We propose SEG 3.0 and apply it to Internet of Things (IoT) and particularly on use cases for smart cities as proof of concept. Firstly, we define characteristics required for the methodology. Secondly, we describe the processes and the different formal steps. Thirdly, we provide a proof of concept framework and architecture applying this methodology, thus the benefits of using SEG 3.0 methodology in IoT domains are described. Finally, we demonstrate that the SEG 3.0 methodology is applied to three use cases: (1) the M3 framework to assist developers in designing semantic-based IoT applications, (2) the VITAL EU project for smart cities, and (3) the FIESTA-IoT EU project for IoT semantic interoperability. SEG 3.0 is a formal methodology generic enough to be applied to other domains than IoT and smart cities, since the main benefit of the SEG 3.0 is integrating heterogeneous data and adding value to it to build innovative applications.

We discuss in this paper, semantic web methodologies, best practices and recommendations beyond the IERC Cluster Semantic Interoperability Best Practices and Recommendations (IERC AC4). The semantic web community designed best practices and methodologies which are unknown from the IoT community. In this paper, we synthesize and highlight the most relevant work regarding ontology methodologies, engineering, best practices and tools that could be applied to Internet of Things (IoT). To the best of our knowledge, this is the first work aiming at bridging such methodologies to the IoT community and go beyond the IERC AC4 cluster. This research is being applied to three uses cases: (1) the M3 framework assisting IoT developers in designing interoperable ontology-based IoT applications, (2) the FIESTA-IoT EU project encouraging semantic interoperability within IoT, and (3) a collaborative publication of legacy ontologies.

International audience; Securing the Internet of Things, more precisely, the ETSI Machine to Machine (M2M) architecture is a difficult task, since there is a need to secure heterogeneous wireless communications (cellular, wireless, wired), devices (sensor or mobile phone) and applications (programming language, framework, database). In this article, we present the state of the art concerning the security ontologies in various domains (Web, MANET, 2G/GSM, 3G/UMTS, 4G/LTE, Wi-Fi, Intrusion Detection System). Since, most of the existing security ontologies are not published online or do not follow semantic web best practices, we have designed the STAC (Security Toolbox: Attack & Countermeasure) ontology-based security knowledge respecting the semantic web guidelines. The STAC ontology, dataset and application have been designed to help software developers or designers to choose security mechanisms fitting their needs to secure Internet of Things (IoT) applications. STAC is published online (http://sensormeasurement.appspot.com/?p=stac).

International audience; We present a security ontology to help non-security expert software designers or developers to: (1) design secure software and, (2) to understand and be aware of main security concepts and issues. Our security ontology defines the main security concepts such as attacks, countermeasures, security properties and their relationships. Countermeasures can be cryptographic concepts (encryption algorithm, key management, digital signature, hash function), security tools or security protocols. The purpose of this ontology is to be reused in numerous domains such as security of web applications, network management or communication networks (sensor, cellular and wireless). The ontology and a user interface (to use the ontology) are available online.

International audience; Linked Open Data refers to a set of best practices for the publication and interlinking of structured data on the Web in order to create a global interconnected data space called Web of Data. To ensure the resources featured in a dataset are richly described and, at the same time, protected against malicious users, we need to specify the conditions under which a dataset is accessible. Being able to specify access terms should also encourage data providers to publish their data. We introduce a lightweight vocabulary, called Social Semantic SPARQL Security for Access Control Ontology (S4AC), allowing the definition of fine-grained access control policies formalized in SPARQL, and enforced when querying Linked Data. In particular, we define an access control model providing the users with means to define policies for restricting the access to specific RDF data, based on social tags, and contextual information.