An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0

Pace, Pasquale; Aloi, Gianluca; Gravina, Raffaele; Caliciuri, Giuseppe; Fortino, Giancarlo; Liotta, Antonio

Abstract

Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge, a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

Citation

Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481-489. https://doi.org/10.1109/tii.2018.2843169

• Journal Article Type: Article
• Acceptance Date: May 27, 2018
• Online Publication Date: Jun 1, 2018
• Publication Date: 2019-01
• Deposit Date: Jul 29, 2019
• Publicly Available Date: Aug 6, 2019
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 15
• Issue: 1
• Pages: 481-489
• DOI: https://doi.org/10.1109/tii.2018.2843169
• Keywords: Body sensor networks, cloud computing, edge computing, heart rate variability, internet of things
• Public URL: http://researchrepository.napier.ac.uk/Output/1995589
• Publisher URL: https://doi.org/10.1109%2Ftii.2018.2843169