Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data.

No abstract

This study aims to bring about a novel approach to the analysis of Sustainable Development Goals (SDGs) based solely on the appearance of news. Our purpose is to provide a monitoring tool that enables world news to be detected in an SDG-oriented manner, by considering multilingual as well as wide geographic coverage. The association of the goals with news basis the World Bank Group Topical Taxonomy, from which the selection of search words approximates the 17 development goals. News is extracted from The GDELT Project (Global Database of Events, Language and Tone) which gathers both printed as well as online news from around the world. 60 851 572 relevant news stories were identified in 2019. The intertwining of world news with SDGs as well as connections between countries are interpreted and highlight that even in the most SDG-sensitive countries, only 2.5% of the news can be attributed to the goals. Most of the news about sustainability appears in Africa as well as East and Southeast Asia, moreover typically the most negative tone of news can be observed in Africa. In the case of climate change (SDG 13), the United States plays a key role in both the share of news and the negative tone. Using the tools of network science, it can be verified that SDGs can be characterized on the basis of world news. This news-centred network analysis of SDGs identifies global partnerships as well as national stages of implementation towards a sustainable socio-environmental ecosystem. In the field of sustainability, it is vital to form the attitudes and environmental awareness of people, which strategic plans cannot address but can be measured well through the news.

This paper aims to identify the regional potential of Industry 4.0 (I4.0). Although the regional background of a company significantly determines how the concept of I4.0 can be introduced, the regional aspects of digital transformation are often neglected with regard to the analysis of I4.0 readiness. Based on the analysis of the I4.0 readiness models, the external regional success factors of the implementation of I4.0 solutions are determined. An I4.0+ (regional Industry 4.0) readiness model, a specific indicator system is developed to foster medium-term regional I4.0 readiness analysis and foresight planning. The indicator system is based on three types of data sources: (1) open governmental data; (2) alternative metrics like the number of I4.0-related publications and patent applications; and (3) the number of news stories related to economic and industrial development. The indicators are aggregated to the statistical regions (NUTS 2), and their relationships analyzed using the Sum of Ranking Differences (SRD) and Promethee II methods. The developed I4.0+ readiness index correlates with regional economic, innovation and competitiveness indexes, which indicates the importance of boosting regional I4.0 readiness.

The data article presents a dataset suitable to measure regional Industry 4.0 (I4.0+) readiness. The I4.0+ dataset includes 101 indicators with 248 958 observations, aggregated to NUTS 2 statistical level) based on open data in the field of education (ETER, Erasmus), science (USPTO, MA-Graph, GRID), government (Eurostat) and media coverage (GDELT). Indicators consider the I4.0-specific domain of higher education and lifelong learning, innovation, technological investment, labour market and technological readiness as indicators. A composite indicator, the I4.0+ index was constructed by the Promethee method, to identify regional rank regarding their I4.0 performance. The index is validated with economic (GDP) and innovation indexes (Regional Innovation Index).