Wireless Communications and Mobile Computing

Semantic Sensor Data Annotation and Integration on the Internet of Things


Publishing date
01 Aug 2021
Status
Closed
Submission deadline
02 Apr 2021

Lead Editor

1Fujian University of Technology, Fuzhou, China

2Norwegian University of Science and Technology, Trondheim, Norway

3Swinburne University of Technology, Victoria, Australia

4Chaoyang University of Technology, Taichung, Taiwan

This issue is now closed for submissions.
More articles will be published in the near future.

Semantic Sensor Data Annotation and Integration on the Internet of Things

This issue is now closed for submissions.
More articles will be published in the near future.

Description

The rapid increase in the number of network-enabled devices and sensors deployed in physical environments is changing information communication networks. It is predicted that within the next decade, billions of devices will generate myriad real-world data for many applications and services in a variety of areas, such as smart grids, smart homes, e-health, the automotive industry, transport, logistics, and environmental monitoring. The related technologies and solutions that enable the integration of real-world data and services into current information networking technologies are often described under the umbrella term the Internet of Things (IoT).

As most IoT devices operate in real-world environments, the exposed services are not as reliable and stable as well-engineered and maintained business services, and the quality of information and services in the IoT domain can vary over time. The heterogeneity of underlying devices and networks also makes it difficult to provide one-fits-all solutions to represent data and services that emerge from IoT networks. This brings significant challenges to data integration, data fusion, and discovery mechanisms that require interoperable and machine-interpretable data and quality descriptions. A potential solution to this challenge is to model IoT data using machine-interpretable and interoperable formats. The existing work often uses solutions that are adapted from the Semantic Web (SW) and semantic data modelling to overcome the interoperability issues and to provide semantically rich descriptions for IoT data. Recent advancements in this area are discussed in several existing works such as the Semantic Sensor Web (SSW) and Linked Sensor Data (LSD) on the Linked Open Data (LOD) cloud. Research on IoT data so far has largely focused on knowledge representation, i.e., how to semantically describe capabilities of IoT devices and services, data annotation, and publication, i.e., how to create and publish semantically annotated IoT data and linked data models.

The aim of this Special Issue is to gather research looking into both knowledge representation and publication, as well as work looking at other key issues like modelling and integrating observation and measurement data, streaming sensor data, and providing discovery mechanisms to enable distributed query mechanisms, which all help to enable end-to-end solutions for publication and consumption of the sensory data emerging from IoT resources. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

  • Sensor knowledge modelling and representation
  • Sensor data analysis and knowledge discovery
  • Sensor ontology engineering and sensor data annotation
  • Sensor ontology alignment and linked sensor data integration
  • Applications of semantic sensor data annotation and integration

Articles

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Research on Security Level Evaluation Method for Cascading Trips Based on WSN

Hui-Qiong Deng | Jie Luo | ... | Pei-Qiang Li
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  • - Article ID 6639558
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Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

Zhendong Liu | Dongyan Li | ... | Xiaofeng Li
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  • - Volume 2021
  • - Article ID 6627588
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Attention Mechanism-Based CNN-LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation

Yuan Xie | Jisheng Zhao | ... | Longge Li
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  • - Article ID 6625758
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A Data-Driven and Knowledge-Driven Method towards the IRP of Modern Logistics

Tiexin Wang | Yi Wu | ... | Wenjing Liu
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  • - Article ID 6631074
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Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

Yanke Du | Shuo Sun | ... | Chi-Hua Chen
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  • - Article ID 6655125
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An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

Dazhi Jiang | Zhihui He | ... | Linyan Xu
  • Special Issue
  • - Volume 2021
  • - Article ID 6651112
  • - Research Article

Energy Efficiency Opposition-Based Learning and Brain Storm Optimization for VNF-SC Deployment in IoT

Hejun Xuan | Xuelin Zhao | ... | Yanling Li
  • Special Issue
  • - Volume 2021
  • - Article ID 8842508
  • - Research Article

Soil Medium Electromagnetic Scattering Model for the Study of Wireless Underground Sensor Networks

Frank Kataka Banaseka | Hervé Franklin | ... | Isaac Wiafe
  • Special Issue
  • - Volume 2020
  • - Article ID 8894852
  • - Research Article

A Random Walk-Based Energy-Aware Compressive Data Collection for Wireless Sensor Networks

Keming Dong | Chao Chen | Xiaohan Yu
  • Special Issue
  • - Volume 2020
  • - Article ID 8854649
  • - Research Article

Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network

Canyang Guo | Genggeng Liu | Chi-Hua Chen
Wireless Communications and Mobile Computing
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