Industrial Needs in the Fields of Artificial Intelligence, Internet of Things and Edge Computing

Sensors (Basel). 2022 Jun 14;22(12):4501. doi: 10.3390/s22124501.

Abstract

Industry 4.0 corresponds to the Fourth Industrial Revolution, resulting from technological innovation and research multidisciplinary advances. Researchers aim to contribute to the digital transformation of the manufacturing ecosystem both in theory and mainly in practice by identifying the real problems that the industry faces. Researchers focus on providing practical solutions using technologies such as the Industrial Internet of Things (IoT), Artificial Intelligence (AI), and Edge Computing (EC). On the other hand, universities educate young engineers and researchers by formulating a curriculum that prepares graduates for the industrial market. This research aimed to investigate and identify the industry's current problems and needs from an educational perspective. The research methodology is based on preparing a focused questionnaire resulting from an extensive recent literature review used to interview representatives from 70 enterprises operating in 25 countries. The produced empirical data revealed (1) the kind of data and business management systems that companies have implemented to advance the digitalization of their processes, (2) the industries' main problems and what technologies (could be) implemented to address them, and (3) what are the primary industrial needs and how they can be met to facilitate their digitization. The main conclusion is that there is a need to develop a taxonomy that shall include industrial problems and their technological solutions. Moreover, the educational needs of engineers and researchers with current knowledge and advanced skills were underlined.

Keywords: Artificial Intelligence; Edge Computing; Industry 4.0; Internet of Things; industrial needs; industrial problems.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence
  • Ecosystem
  • Industry
  • Internet of Things*
  • Technology

Grants and funding

This work has been partially funded by Programme Erasmus+, Knowledge Alliances, Application No 621639-EPP-1-2020-1-IT-EPPKA2-KA, PLANET4: Practical Learning of Artificial iNtelligence on the Edge for indusTry 4.0.