Smart Meter infrastructures are emerging systems that measure, col- lect, and analyze utility data and communicate with the network’s backbone on a fixed schedule. Such infrastructures are a vital part towards real Intelligent Cities. In this article we propose an edge- processing oriented Internet of Things architecture for smart meter networks that helps reduce data communication while keeping the sys- tem secure, reliable and responsive. We discuss our system architecture based on a real-world water metering deployment of 48 water meters inside a University Campus, using off-the-shelf wM-Bus water meters. We also provide a study of how our solution can face the same problems regardless of the size of the water meter network, scaling up to cities of millions of citizens and measuring points, reducing traffic and data sizes event by 80%.
Sparks-Edge: Analytics for intelligent city water metering / Amaxilatis, D.; Chatzigiannakis, I.; Tselios, C.; Tsironis, N.. - 2492:(2019), pp. 19-29. (Intervento presentato al convegno 2019 Joint Poster and Workshop Sessions of AmI, AmI 2019 and 2019 European Conference on Ambient Intelligence tenutosi a Rome; Italy).
Sparks-Edge: Analytics for intelligent city water metering
Chatzigiannakis I.
Co-primo
Membro del Collaboration Group
;
2019
Abstract
Smart Meter infrastructures are emerging systems that measure, col- lect, and analyze utility data and communicate with the network’s backbone on a fixed schedule. Such infrastructures are a vital part towards real Intelligent Cities. In this article we propose an edge- processing oriented Internet of Things architecture for smart meter networks that helps reduce data communication while keeping the sys- tem secure, reliable and responsive. We discuss our system architecture based on a real-world water metering deployment of 48 water meters inside a University Campus, using off-the-shelf wM-Bus water meters. We also provide a study of how our solution can face the same problems regardless of the size of the water meter network, scaling up to cities of millions of citizens and measuring points, reducing traffic and data sizes event by 80%.File | Dimensione | Formato | |
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