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Article

An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities

1
Department of Mathematics and Physics, University of Campania, 81100 Caserta, CE, Italy
2
Distributed Systems and Internet Technology Lab DISIT, University of Florence, 50121 Firenze, FI, Italy
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 435; https://doi.org/10.3390/s21020435
Received: 18 November 2020 / Revised: 30 December 2020 / Accepted: 1 January 2021 / Published: 9 January 2021
(This article belongs to the Special Issue Smart Cities of the Future: A Cyber Physical System Perspective)
Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission. View Full-Text
Keywords: smart resilient city; big multimedia data; complex system; big data analysis; disaster resilience; evidence driven decision support system; functional resonance analysis method; Internet of Everything; tree value logic smart resilient city; big multimedia data; complex system; big data analysis; disaster resilience; evidence driven decision support system; functional resonance analysis method; Internet of Everything; tree value logic
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MDPI and ACS Style

Bellini, E.; Bellini, P.; Cenni, D.; Nesi, P.; Pantaleo, G.; Paoli, I.; Paolucci, M. An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities. Sensors 2021, 21, 435. https://doi.org/10.3390/s21020435

AMA Style

Bellini E, Bellini P, Cenni D, Nesi P, Pantaleo G, Paoli I, Paolucci M. An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities. Sensors. 2021; 21(2):435. https://doi.org/10.3390/s21020435

Chicago/Turabian Style

Bellini, Emanuele, Pierfrancesco Bellini, Daniele Cenni, Paolo Nesi, Gianni Pantaleo, Irene Paoli, and Michela Paolucci. 2021. "An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities" Sensors 21, no. 2: 435. https://doi.org/10.3390/s21020435

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