Refereed article in conference proceedings (A4)

Artificial Intelligence at the Edge in the Blockchain of Things




List of AuthorsTuan Nguyen Gia, Anum Nawaz, Jorge Peña Queralta, Hannu Tenhunen, Tomi Westerlund

EditorsGregory M.P. O'Hare, Michael J. O'Grady, John O’Donoghue, Patrick Henn

Conference nameInternational Conference on Wireless Mobile Communication and Healthcare

Publication year2020

JournalLecture 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 number320

Start page267

End page280

ISBN978-3-030-49288-5

eISBN978-3-030-49289-2

ISSN1867-8211

DOIhttp://dx.doi.org/10.1007/978-3-030-49289-2_21

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/44657532


Abstract

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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Last updated on 2022-07-04 at 18:38