Semantic Reasoning for Context-Aware Internet of Things Applications

Maarala, Altti Ilari; Su, Xiang; Riekki, Jukka

doi:10.1109/jiot.2016.2587060

Cited by 122 publications

(74 citation statements)

References 37 publications

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“…Altogether, from different columns of Table I, 7 papers led to the IoT challenges, 5 papers led to Semantic Web challenges, and 11 papers reported on semantic technology challenges in the IoT domain. [6], [8] [5], [9], [10], [11], [12], [13], [14], [15] [1], [2], [3], [4], [16], [17], [18], [19], [20], [21], [22], [23], [24] III. BACKGROUND…”

Section: Survey Methodsmentioning

confidence: 99%

“…Ontologies, in addition to dictionaries, allow for more expressiveness of the concepts [18]. Ontologies are suitable for sharing, organising and representing knowledge in a formal way [10]. RDF Schema, OWL and similar semantic tools offer vocabulary for representing and designating RDF data.…”

Section: B Semantic Language and Reasoningmentioning

confidence: 99%

“…A reasoning engine (also called a reasoner) is a software tool that executes reasoning based on rules. Rules, in turn, are based on first-order predicate logic or description logic (DL) to derive conclusions from a sequence of statements (premises) [10].…”

Section: B Semantic Language and Reasoningmentioning

confidence: 99%

“…In most of current implementations, devices that combine aforementioned requirements generally can only operate within boundaries of a network of the same producer and do not possess sharing capabilities between different vendors and their platforms [10]. Knowledge can be utilised only for a particular domain in a particular case of appliance.…”

Section: Current State Of Iotmentioning

confidence: 99%

“…Brokers are server-side software components that exchange messages between a message transmitter and a receiver. Exchange solutions may contain built-in message routing based on content, as well as message summarisation and extraction [10]. Describing IoT data taxonomy, Quin et al [2] classify characteristics of IoT data and propose the following three categories: data generation, data quality, and data interoperability.…”

Section: Data Exchange In the Iotmentioning

confidence: 99%

See 4 more Smart Citations

A Survey on Challenges of Semantics Application in the Internet of Things Domain

Harlamova

Kirikova

Sandkuhl

2017

Applied Computer Systems

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Section: Survey Methodsmentioning

confidence: 99%

Section: B Semantic Language and Reasoningmentioning

confidence: 99%

Section: B Semantic Language and Reasoningmentioning

confidence: 99%

Section: Current State Of Iotmentioning

confidence: 99%

Section: Data Exchange In the Iotmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Challenges of Semantics Application in the Internet of Things Domain

Harlamova

Kirikova

Sandkuhl

2017

Applied Computer Systems

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IoT and semantic web technologies for event detection in natural disasters

Greco

Ritrovato

Tiropanis

et al. 2018

Concurrency and Computation

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Summary Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.

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A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

Park

Mun

Choi

et al. 2020

Concurrency and Computation

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Context-awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real-world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context-aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real-world object. K E Y W O R D Scontext-awareness, data abstraction, edge computing environment, embedding convergence, knowledge representation INTRODUCTIONRecent improvements in computational methodologies and technologies such as cloud computing, Internet of Things (IoT), 5G networks, and big data have enabled to build meaningful knowledge for artificial intelligence. [1][2][3][4][5] Among these improvements, there has been innovative approaches to develop the subsets of an architecture for application in computation domains. It is important to consider that bringing the advantages provides benefits to lives for human being and society for transformative innovation. [6][7][8] Because recent trends and challenges have focused on processing massive amounts of data, also known as big data, so that different approaches related to the collection, management, and application of such datasets have been conducted. Therefore, it is necessary to design the stages that are highly associated with handling these datasets. 9,10 As the result, both processing and providing meaningful context information are important techniques where massive data can be generated on state-of-the-art domains such as IoT, edge computing, or big data analysis. For context-aware systems, despite combining the key techniques mentioned above, there remain certain difficulties to overcome. [11][12][13] In general, context-awareness consists of resolving hardware heterogeneity, data acquisition, collection of

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Semantic Reasoning for Context-Aware Internet of Things Applications

Cited by 122 publications

References 37 publications

A Survey on Challenges of Semantics Application in the Internet of Things Domain

A Survey on Challenges of Semantics Application in the Internet of Things Domain

IoT and semantic web technologies for event detection in natural disasters

A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

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