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Working with AIoT Solutions in Embedded Software Applications. Recommendations, Guidelines, and Lessons Learned

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Bibliographische Detailangaben
Titel:	Working with AIoT Solutions in Embedded Software Applications. Recommendations, Guidelines, and Lessons Learned Att arbeta med AI i inbyggda system. Rekommendationer och riktlinjer.
Autoren:	Gratorp, Christina
Weitere Verfasser:	Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Technology and Society, Environmental and Energy Systems Studies, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för teknik och samhälle, Miljö- och energisystem, Originator
Quelle:	Intelligent Secure Trustable Things Studies in Computational Intelligence. 1147(1):309-329
Schlagwörter:	Natural Sciences, Computer and Information Sciences, Computer Sciences, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datavetenskap (Datalogi)
Beschreibung:	This chapter aims to be a broad introduction for embedded systems professionals that wish to add machine learning to traditional embedded software. It briefly describes the foundation for a stable and secure IoT communication platform, touching on important areas such as the MQTT protocol and data extraction. The discussion is based on a case study for a digitalized marine vessel, and focuses on guidelines and recommendations for how to work with machine learning models in industrial embedded software applications.
Zugangs-URL:	https://link.springer.com/chapter/10.1007/978-3-031-54049-3_17
Datenbank:	SwePub

Beschreibung
Abstract:	This chapter aims to be a broad introduction for embedded systems professionals that wish to add machine learning to traditional embedded software. It briefly describes the foundation for a stable and secure IoT communication platform, touching on important areas such as the MQTT protocol and data extraction. The discussion is based on a case study for a digitalized marine vessel, and focuses on guidelines and recommendations for how to work with machine learning models in industrial embedded software applications.
ISSN:	1860949X 18609503