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Quantify Resilience Enhancement of UTS through Exploiting Connected Community and Internet of Everything Emerging Technologies
- Source :
- ACM Transactions on Internet Technology. 18:1-34
- Publication Year :
- 2017
- Publisher :
- Association for Computing Machinery (ACM), 2017.
-
Abstract
- This work aims at investigating and quantifying the Urban Transport System (UTS) resilience enhancement enabled by the adoption of emerging technology such as Internet of Everything (IoE) and the new trend of the Connected Community (CC). A conceptual extension of Functional Resonance Analysis Method (FRAM) and its formalization have been proposed and used to model UTS complexity. The scope is to identify the system functions and their interdependencies with a particular focus on those that have a relation and impact on people and communities. Network analysis techniques have been applied to the FRAM model to identify and estimate the most critical community-related functions. The notion of Variability Rate (VR) has been defined as the amount of output variability generated by an upstream function that can be tolerated/absorbed by a downstream function, without significantly increasing of its subsequent output variability. A fuzzy-based quantification of the VR based on expert judgment has been developed when quantitative data are not available. Our approach has been applied to a critical scenario as flash flooding considering two cases: when UTS has CC and IoE implemented or not. However, the method can be applied in different scenarios and critical infrastructures. The results show a remarkable VR enhancement if CC and IoE are deployed.
- Subjects :
- Resilience
Relation (database)
Computer Networks and Communications
Emerging technologies
Computer science
media_common.quotation_subject
Distributed computing
02 engineering and technology
Functional Resonance Analysis Method
computer.software_genre
Fuzzy logic
Interdependence
Fuzzy Logic
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Upstream (networking)
Data mining
Resilience (network)
Function (engineering)
computer
media_common
Network analysis
Subjects
Details
- ISSN :
- 15576051 and 15335399
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- ACM Transactions on Internet Technology
- Accession number :
- edsair.doi.dedup.....cc107eb34e9202d19d7e098a858ade34
- Full Text :
- https://doi.org/10.1145/3137572