Cyber security in smart cities: A review of deep learning-based applications and case studies

Urban Transportation Networks Resilience: Indicators, Disturbances, and Assessment Methods

Modern societies are increasingly dependent on the proper functioning of critical infrastructures (CIs). CIs produce and distribute essential goods or services, as for power transmission systems, water treatment and distribution infrastructures, transportation systems, communication networks, nuclear power plants, and information technologies. Being resilient becomes a key property for CIs, which are constantly exposed to threats that can undermine safety, security, and business continuity. Nowadays, a variety of approaches exist in the context of CIs’ resilience research. This paper provides a state-of-the-art review on the approaches that have a complete qualitative dimension, or that can be used as entry points for semi-quantitative analyses. The study aims to uncover the usage of qualitative research methods through a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The paper identifies four principal dimensions of resilience referred to CIs (i.e., techno-centric, organisational, community, and urban) and discusses the related qualitative methods. Besides many studies being focused on energy and transportation systems, the literature review allows to observe that interviews and questionnaires are most frequently used to gather qualitative data, besides a high percentage of mixed-method research. The article aims to provide a synthesis of literature on qualitative methods used for resilience research in the domain of CIs, detailing lessons learned from such approaches to shed lights on best practices and identify possible future research directions.

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Reviewing qualitative research approaches in the context of critical infrastructure resilience

The fourth industrial era, known as ‘Industry 4.0’ (I4.0), aided and abetted by the digital revolution, has attracted increasing attention among scholars and practitioners in the last decade. The adoption of I4.0 principles in Disaster Risk Management (DRM) research and associated industry practices is particularly notable, although its origins, impacts and potential are not well understood. In response to this knowledge gap, this paper conducts a systematic literature review and bibliometric analysis of the application and contribution of I4.0 in DRM. The systematic literature review identified 144 relevant articles and then employed descriptive and content analysis of a focused set of 70 articles published between 2011 and 2021. The results of this review trace the growing trend for adoption of I4.0 tools and techniques in disaster management, and in parallel their influence in resilient infrastructure and digital construction fields. The results are used to identify six dominant clusters of research activity: big data analytics, Internet of Things, prefabrication and modularization, robotics and cyber-physical systems. The research in each cluster is then mapped to the priorities of the Sendai framework for DRR, highlighting the ways it can support this international agenda. Finally, this paper identifies gaps within the literature and discusses possible future research directions for the combination of I4.0 and DRM.

Industry 4.0, Disaster Risk Management and Infrastructure Resilience: A Systematic Review and Bibliometric Analysis

Cyber-physical-social interdependencies and organizational resilience: A review of water, transportation, and cyber infrastructure systems and processes

Analysis and exploration of similar continuous data for various air-sampler sensor have been performed by developing a processing tool. The analysis and design for characterizing sensor data have been stated and described. Continuous 24X7 sensor data and metrological data leads the layout choices for the analytical design. Such design choices are helpful to understand the pattern of similar reading for various devices at a time. In this paper, we describe the use of the mentioned design choices for to identify pattern, unusual behavior. We used this tool and design choice to solve VAST 2017 mini challenge 2.

Visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation

Satellite images are helpful in remote sensing of land features. However, such multi-spectral images cannot be displayed using readily available imaging tools. We developed a tool in Processing that is able to read in multi-spectral images and display each band as a grayscale image. This tool also allows for mapping of any of the bands to red, green or blue channel of the displayed image. In this paper, we describe how such tool can be used in identifying land features as well as assist in finding changes over time. We used our tool to successfully solve the VAST challenge 2017 mini-challenge 3.

Multi-Spectral Satellite Image Analysis for Feature Identification and Change Detection VAST Challenge 2017: Honorable Mention for Good Facilitation of Single Image Analysis

Anwesh Tuladhar

Papers

Cyber-physical-social interdependencies and organizational resilience: A review of water, transportation, and cyber infrastructure systems and processes

Visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation

Multi-Spectral Satellite Image Analysis for Feature Identification and Change Detection VAST Challenge 2017: Honorable Mention for Good Facilitation of Single Image Analysis