Semantic Web of Things: an analysis of the application semantics for the IoT moving towards the IoT convergence

2014 ◽  
Vol 10 (2/3) ◽  
pp. 244 ◽  
Author(s):  
Antonio J. Jara ◽  
Alex C. Olivieri ◽  
Yann Bocchi ◽  
Markus Jung ◽  
Wolfgang Kastner ◽  
...  
Keyword(s):  
Semantic Web ◽  
Web Of Things ◽  
Application Semantics

Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

2018 ◽  
pp. 262-294 ◽  
Author(s):  
Floriano Scioscia ◽  
Michele Ruta ◽  
Giuseppe Loseto ◽  
Filippo Gramegna ◽  
Saverio Ieva ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Semantic Web ◽  
Knowledge Bases ◽  
Semantic Web Technologies ◽  
Experimental Performance ◽  
Web Of Things ◽  
Web Technologies ◽  
Constrained Environments ◽  
And Performance

The Semantic Web of Things (SWoT) aims to support smart semantics-enabled applications and services in pervasive contexts. Due to architectural and performance issues, most Semantic Web reasoners are often impractical to be ported: they are resource consuming and are basically designed for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper describes Mini-ME (the Mini Matchmaking Engine), a mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering, bonus, difference) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios and experimental performance evaluation are presented on PC (against other popular Semantic Web reasoners), smartphone and embedded single-board computer testbeds.

Semantic Web of Things for Industry 4.0

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 885-886
Author(s):  
Dhavalkumar Thakker ◽  
Pankesh Patel ◽  
Muhammad Intizar Ali ◽  
Tejal Shah
Keyword(s):  
Semantic Web ◽  
Industry 4.0 ◽  
State Of The Art ◽  
The State ◽  
Special Issue ◽  
Semantic Web Technologies ◽  
Web Of Things ◽  
Web Technologies

Welcome to this special issue of the Semantic Web (SWJ) journal. The special issue compiles four technical contributions that significantly advance the state-of-the-art in Semantic Web of Things for Industry 4.0 including the use of Semantic Web technologies and techniques in Industry 4.0 solutions.

Interoperability in semantic Web of Things: Design issues and solutions

2019 ◽  
Vol 32 (6) ◽  
pp. e3911 ◽  
Author(s):  
André Luis Meneses Silva ◽  
José de Jesús Pérez-Alcázar ◽  
Sergio Takeo Kofuji
Keyword(s):  
Semantic Web ◽  
Design Issues ◽  
Web Of Things

scholarly journals Data recoverability and estimation for perception layer in semantic web of things

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245847
Author(s):  
Rabia Afzaal ◽  
Muhammad Shoaib
Keyword(s):  
Semantic Web ◽  
Compressive Sensing ◽  
Estimation Method ◽  
Low Rank ◽  
Data Loss ◽  
Web Of Things ◽  
Sensory Data ◽  
Layered Architecture ◽  
Reconstruction Methods ◽  
Error Ratio

Internet of Things (IoT) is the growing invention in the current development of different domains like industries, e-health, and education, etc. Semantic web of things (SWoT) is an extension of IoT that enhance the communication by behaving intelligently. SWoT comprises 7 layered architecture. The perception layer is an important layer for collecting data from devices and to communicate with its associated layer. The data loss at the perception layer is very common due to inadequate resources, unpredictable link, noise, collision, and unexpected damage. To address this problem, we propose a method based on Compressive Sensing which recovers and estimates sensory data from a low-rank structure. The contribution of this paper is three folds. Firstly, we determine the problem of data acquisition and data loss at semantic sensory nodes in SWoT. Secondly, we introduce a compressive sensing based framework for SWoT that recovers the data accurately using low-rank features. Thirdly, the data estimation method is utilized to reduce the volume of the data. Proposed Compressive Sensing based Data Recoverability and Estimation (CS-RE) method is evaluated and compared with the existing reconstruction methods. The simulation results on real sensory datasets depict that the proposed method significantly outperforms existing methods in terms of error ratio and data recoverability accuracy.

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