An Indicator-Based Approach to Assessing Resilience of Smart Critical Infrastructures

Jovanović, A.; Øien, Knut; Choudhary, Amit Kumar

doi:10.1007/978-3-319-68606-6_17

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…This allows the calculation of the system resilience index, a synthetic proxy indicator for resilience, able to quantify the system adaptive capacity [ 21 , 22 , 25 , 39 ]. It is worth noticing that the definition of the indicators should follow a consensus-driven process involving all the actors and stakeholders that in one way or another are connected to these indicators, both, because they can be the evaluators of these indicators or the evaluated [ 34 , 46 ]. Thus, the quantification of the enhancements of the adaptive capacity obtained with the introduction of ICT in smart city aims at representing valuable information for decision-makers at strategic, tactical, and operational levels.…”

Section: Methodsmentioning

confidence: 99%

“…Today, emerging technological innovations such as IoE (including social media, location-based systems, and big data analytics—BDA) have opened up the opportunity for real time monitoring (i.e., what it is happening? ), thus, a dynamic evidence driven system resilience management [ 2 , 14 , 34 ], and predictive analytics based on increasingly reliable big data (i.e., what it will happen?) is now at hand.…”

Section: Related Workmentioning

confidence: 99%

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

Bellini

Cenni

et al. 2021

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Bellini

Cenni

et al. 2021

Sensors

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“…The resilience or vulnerability indicators are possibly already applied in related disciplinary of climate change, such as natural risk, human security, environmental sustainability, etc. [46,47].…”

Section: Indicator For Resilience Assessmentmentioning

confidence: 99%

Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

et al. 2022

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Urban transport infrastructures (TIs) play a central role in an urban society that faces more and more disasters. TIs, part of critical infrastructures (CIs), are highly correlated with urban disaster management in terms of their resilience when cities are facing a crisis or disaster. According to many studies, indicator assessment has been frequently used for the resilience management of CIs in recent decades. Defining and characterizing indicators can be useful for disaster managers as it could help monitor and improve the capacities and performance of TIs. The purpose of this paper, therefore, is (1) to identify and summarize the existing indicators of TIs resilience from the currently available literature, and (2) to discuss the possible future studies of the resilience indicator of TIs. The first results indicated that there are some barriers to identify indicators following the common search method through keywords. Additionally, the indicators found are mainly related to technical information, the disruption stage, and internal TIs. Finally, due to the complexity of indicator assessment, sub-indicators and indicator spatialization are widely used in the resilience assessment of urban TIs studies.

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“…The description of the development of the RIL method and the resilience indicators are based on Øien et al (2017a-c). Earlier versions of the Smart-Resilience methodology are also presented in Jovanović et al (2017a;2018).…”

Section: Introductionmentioning

confidence: 99%

Resilience assessment of smart critical infrastructures based on indicators

Øien¹,

Bodsberg²,

Jovanović³

2018

Safety and Reliability – Safe Societies in a Changing World

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An Indicator-Based Approach to Assessing Resilience of Smart Critical Infrastructures

Cited by 12 publications

References 10 publications

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

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

Resilience Indicator of Urban Transport Infrastructure: A Review on Current Approaches

Resilience assessment of smart critical infrastructures based on indicators

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