Refereed article in conference proceedings (A4)
Artificial Intelligence at the Edge in the Blockchain of Things
List of Authors: Tuan Nguyen Gia, Anum Nawaz, Jorge Peña Queralta, Hannu Tenhunen, Tomi Westerlund
Editors: Gregory M.P. O'Hare, Michael J. O'Grady, John O’Donoghue, Patrick Henn
Conference name: International Conference on Wireless Mobile Communication and Healthcare
Publication year: 2020
Journal: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Book title *: Wireless Mobile Communication and Healthcare: 8th EAI International Conference, MobiHealth 2019, Dublin, Ireland, November 14-15, 2019, Proceedings
Volume number: 320
Start page: 267
End page: 280
ISBN: 978-3-030-49288-5
eISBN: 978-3-030-49289-2
ISSN: 1867-8211
DOI: http://dx.doi.org/10.1007/978-3-030-49289-2_21
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/44657532
Traditional cloud-centric architectures for Internet-of-Things applications are being replaced by distributed approaches. The Edge and Fog computing paradigms crystallize the concept of moving computation towards the edge of the network, closer to where the data originates. This has important benefits in terms of energy efficiency, network load optimization and latency control. The combination of these paradigms with embedded artificial intelligence in edge devices, or Edge AI, enables further improvements. In turn, the development of blockchain technology and distributed architectures for peer-to-peer communication and trade allows for higher levels of security. This can have a significant impact on data-sensitive and mission-critical applications in the IoT. In this paper, we discuss the potential of an Edge AI capable system architecture for the Blockchain of Things. We show how this architecture can be utilized in health monitoring applications. Furthermore, by analyzing raw data directly at the edge layer, we inherently avoid the possibility of breaches of sensitive information, as raw data is never stored nor transferred outside of the local network
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