4W1H in IoT Semantics

Bajaj, Garvita; Agarwal, Rachit; Singh, Pushpendra; Georgantas, Nikolaos; Issarny, Valérie

doi:10.1109/access.2018.2878100

Cited by 27 publications

(17 citation statements)

References 66 publications

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“…Accordingly, ranking knowledge in IoE sensors is a matter of eliciting the main characteristics in IoE applications. In order to understand the IoE domain, we applied specific questions by answering the 4 ws (what, when, who, and where) and 1 h (how) identified using the 4W1H methodology [ 91 , 92 ]. This methodology addresses the challenge imposed due to the high heterogeneity of existing IoE devices.…”

Section: Proposed Ioe Taxonomymentioning

confidence: 99%

“…The deployment of IoE applications in real-world scenarios creates a massive amount of data from real-time interactions, usually at high data rates. It faces challenges as temporal data consistency related to the coherency between the value of the data in the system and its environment state [ 107 ]; and high latency during interactions [ 39 ], when inferred contexts evolve with time [ 91 , 94 ], and the exchanged data may not be accurate.…”

Section: Proposed Ioe Taxonomymentioning

confidence: 99%

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Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.

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Section: Proposed Ioe Taxonomymentioning

confidence: 99%

Section: Proposed Ioe Taxonomymentioning

confidence: 99%

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Costa

Oliveira

Souza

2021

Sensors

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“…• The framework takes as input the ontologies associated with the target use case, which relate to (see § IV for detail): (i) the physical entities, (ii) the cyber entities used for sensing and actuation, and (iii) the constraints and optimization parameters for the IoT system. The ontologies leverage as much as possible existing standards (see [18] for a survey). In particular, there exist various ontology standards for characterizing application domains as well as IoT networks.…”

Section: The Lattice Frameworkmentioning

confidence: 99%

“…As we sketch in Section I, leveraging the ontology-based formalization of the physical and cyber entities composing an IoT system is a classical approach to overcome the heterogeneity of the Things. As such, and although this is beyond the scope of this paper, the LATTICE framework may exploit existing ontologies [18], while the characterization of data flows follows from state of the art programming models associated with edge-based IoT systems [29].…”

Section: Characterizing the Problem Space Using Ontologiesmentioning

confidence: 99%

“…The use of ontologies in IoT systems is not new; it has been stressed as a major enabler since the early days of the IoT. Precisely, ontologies primarily serve two related, yet complementary, purposes in the deployment of IoT systems: (i) reasoning about the gathered data by characterizing the concepts of the specific physical domain under scrutiny [15], [16], and (ii) interoperability by characterizing the connected things through the relevant metadata [17], [18]. However, the complexity of IoT systems is not only due to their heterogeneity, their scale and uncertainty are two other complexity factors.…”

Section: Introductionmentioning

confidence: 99%

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LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

Issarny

Billet

Bouloukakis

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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The Internet of Things is expected to contribute to a "smarter world" by connecting the physical to the virtual, i.e., enabling advanced knowledge engineering over the big data gathered about the physical world. However, such a promise comes along with high resource consumption, spanning the network, storage and computational resources, not to mention possible security and privacy threats. As a result, it tends to be admitted that the IoT smartness will not be accommodated at scale by a centralized cloud-based approach. Instead, the deployment of IoT systems needs to leverage a highly distributed system architecture, which optimizes the distribution of the computation-from the edge to the cloud-according to the unique business requirements in terms of financial cost, latency, availability, etc. Toward that goal, this paper introduces the LATTICE framework, which aims at taming the complexity of configuring edge-based IoT systems. LATTICE builds upon ontologies that have proven useful to characterize the constituents of IoT systems in the required domain-specific way. However, LATTICE also revisits the exploitation of ontologies-i.e., the formal description of the real world, spanning the physical and cyber entities-across the development life-cycle of the IoT systems. As a first evidence, this paper introduces an automated approach to the optimization of IoT system configurations at the edge, provided the ontological description of the target IoT system.

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A comprehensive survey on semantic interoperability for Internet of Things: State‐of‐the‐art and research challenges

Rahman

Hussain

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) is highly heterogeneous in terms of devices, communication technologies, protocols, data formats, and semantics. Data generated from various sources are represented using different semantics. It makes semantic interoperability as one of the significant issues in provisioning seamless communication and services over diverse IoT platforms. Semantic models help in exchanging semantically annotated information among such heterogeneous applications in a meaningful way. Seamless communication among different types of applications is commonly accomplished by using middleware, ontology, and semantic web technologies. In this paper, we present a comprehensive survey of different semantic interoperability solutions in the context of IoT. The pivotal semantic models available in the literature are compared with the help of a classification framework. This study identifies various open research issues and challenges for facilitating interoperable IoT communications. For the purpose of implementation and performance evaluation of various semantic models, we also suggest some important tools and frameworks. After pointing out different prospects and problems of the existing semantic interoperability schemes, the factors for improvement are identified indicating future research directions.

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4W1H in IoT Semantics

Cited by 27 publications

References 66 publications

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy

LATTICE: A Framework for Optimizing IoT System Configurations at the Edge

A comprehensive survey on semantic interoperability for Internet of Things: State‐of‐the‐art and research challenges

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