Recent Advances on Semantic IoT Data Integration
Xingsi Xue, Jeng‐Shyang Pan, Pei‐Wei Tsai, Samiappan Dhanalakshmi · Internet Technology Letters · 2024
The burgeoning Internet of Things (IoT) ecosystem, marked by the exponential growth in network-enabled devices and sensors, is generating an unprecedented volume of real-world data across a spectrum of applications, from smart grids to e-health.This proliferation poses significant challenges in data integration and management due to the variable reliability and stability of IoT services and the heterogeneity of devices and networks.Semantic Web (SW) technologies and semantic data modeling emerge as vital solutions, offering interoperable and machine-interpretable formats to enhance data representation and address interoperability issues.Recent initiatives like the Semantic Sensor Web (SSW) and Linked Sensor Data (LSD) have made strides in knowledge representation and data annotation, focusing on the capabilities and publication of semantically rich IoT data.However, there remains a critical need to extend these advancements to encompass the integration and management of diverse IoT data types, such as observational and measurement data, and streaming sensor data.This special issue invites contributions in areas including Sensor Knowledge Modeling and Representation, Sensor Data Analysis and Knowledge Discovery, Sensor Ontology Engineering and Data Annotation, Sensor Ontology Alignment, and Linked Sensor Data Integration, as well as the application of Machine Learning and Deep Learning in Semantic Sensor Data Annotation and Integration.By addressing these topics, the special issue aims to bridge the existing gaps in IoT data integration, facilitating the development of more robust, efficient, and intelligent IoT systems and solutions.