Creating an Inter-hospital Resilient Network for Pandemic Response Based on Blockchain and Dynamic Digital Twins

This study proposes to develop new knowledge about how to configure digital information for pandemic rapid response, which can use blockchain and digital-driven approaches to facilitate analyses and develop a total solution. Developing and using the rich data implied by dynamic digital twins and blockchain is relevant to manage both patients and medical resources (e.g., doctors/nurses, PPE, beds and ventilators etc.) at the COVID-19 and post COVID period. This paper learns from the experiences of resources deployment/redeployment and pandemic response from UK Hospitals to explore the blockchain solutions for preparing healthcare systems ready for both efficient operation daily and in pandemic thorough (1) information integration of patient (privacy protected) flow and medical resource flow from healthcare and medical records; (2) optimizing the deployment of such resources based on hospitals, regions and local pandemic levels switching from normal to the outbreak. The main idea is to develop the novel framework for creating an inter-hospital resilient network for pandemic response based on blockchain and dynamic digital twin, which will set up innovative ways to best care for patients, protect NHS staff, and support government scientific decisions to beat COVID-19 now and manage the crisis in the future

A survey on multilayer networks modelled to assess robustness in infrastructure systems

The development of modern societies places particular demands on the consistent performance of infrastructure systems. Because multilayer network models are capable of representing the interdependencies between infrastructure components, they have been widely used to analyse the robustness of infrastructure systems. This present study is a systematic review of literature, published since 2010. It aims to investigate how multilayer network models have been used in analysing the robustness of infrastructure systems. According to findings, percolation theory was the most popular method used in about 57% of papers. Regarding the properties, coupling strength and node degree were the most common while directed links and feedback conditions were the least common. The following gaps were identified which provide opportunities for further research. These include the absence of models based on real-world data and the need for models that make fewer simplifying assumptions about complex systems. No papers considered all potential properties, and their effect on boosting or weakening each other’s effect. By considering all properties, the importance of different properties on the robustness of infrastructure systems can be quantified and compared in future studies

Optimizing resilience decision-support for natural gas networks under uncertainty

2019 Summer.Includes bibliographical references.Community resilience in the aftermath of a hazard requires the functionality of complex, interdependent infrastructure systems become operational in a timely manner to support social and economic institutions. In the context of risk management and community resilience, critical decisions should be made not only in the aftermath of a disaster in order to immediately respond to the destructive event and properly repair the damage, but preventive decisions should to be made in order to mitigate the adverse impacts of hazards prior to their occurrence. This involves significant uncertainty about the basic notion of the hazard itself, and usually involves mitigation strategies such as strengthening components or preparing required resources for post-event repairs. In essence, instances of risk management problems that encourage a framework for coupled decisions before and after events include modeling how to allocate resources before the disruptive event so as to maximize the efficiency for their distribution to repair in the aftermath of the event, and how to determine which network components require preventive investments in order to enhance their performance in case of an event. In this dissertation, a methodology is presented for optimal decision making for resilience assessment, seismic risk mitigation, and recovery of natural gas networks, taking into account their interdependency with some of the other systems within the community. In this regard, the natural gas and electric power networks of a virtual community were modeled with enough detail such that it enables assessment of natural gas network supply at the community level. The effect of the industrial makeup of a community on its natural gas recovery following an earthquake, as well as the effect of replacing conventional steel pipes with ductile HDPE pipelines as an effective mitigation strategy against seismic hazard are investigated. In addition, a multi objective optimization framework that integrates probabilistic seismic risk assessment of coupled infrastructure systems and evolutionary algorithms is proposed in order to determine cost-optimal decisions before and after a seismic event, with the objective of making the natural gas network recover more rapidly, and thus the community more resilient. Including bi-directional interdependencies between the natural gas and electric power network, strategic decisions are pursued regarding which distribution pipelines in the gas network should be retrofitted under budget constraints, with the objectives to minimizing the number of people without natural gas in the residential sector and business losses due to the lack of natural gas in non-residential sectors. Monte Carlo Simulation (MCS) is used in order to propagate uncertainties and Probabilistic Seismic Hazard Assessment (PSHA) is adopted in order to capture uncertainties in the seismic hazard with an approach to preserve spatial correlation. A non-dominated sorting genetic algorithm (NSGA-II) approach is utilized to solve the multi-objective optimization problem under study. The results prove the potential of the developed methodology to provide risk-informed decision support, while being able to deal with large-scale, interdependent complex infrastructure considering probabilistic seismic hazard scenarios

Metodología para la evaluación de soluciones de inversiones en infraestructuras interdependientes mediante técnicas de simulación

La presente investigación consiste en el diseño y construcción de una herramienta para estimar y evaluar medidas de desempeño de Sistemas Críticos Interdependientes (CIS) bajo condiciones de afectación y ante opciones de inversión, con la finalidad de validar modelos matemáticos para la optimización de inversión en CIS, ya desarrollados. Se hará uso de técnicas de simulación; modelos matemáticos, que proveerán soluciones previamente generadas; y algoritmos, con la finalidad de simular el comportamiento de una red de infraestructuras críticas y evaluar el desempeño de la misma considerando escenarios de gran afectación, fallas menores y proyectos de inversión. Los modelos utilizados en esta investigación hacen uso de relaciones de interdependencia para representar en mayor medida la interacción entre infraestructuras y el comportamiento del sistema.MaestríaMagister en Ingeniería Industria

Understanding hosting communities as a stakeholder in the provision of urban infrastructure to displaced populations

As the world’s population has shifted to urban areas, so has the accommodation of displaced populations due to natural disasters, violent conflicts, or persecution. Accommodating those displaced in the urban context places challenges on engineers and decision-makers not only in terms of responding to the unexpected infrastructure needs from displaced persons but also ensuring that the infrastructure provided to pre-existing residents of hosting communities are not negatively impacted. Understanding how communities hosting displaced persons react to the disruptions of providing infrastructure to those displaced is fundamental for engineers and decision-makers to provide sustainable infrastructure under such a disaster scenario. This dissertation seeks to understand how hosting communities responded to the disruptions of displaced persons due to the European Refugee Crisis during 2015 and 2016. To accomplish this, statistical and qualitative analyses were coupled to analyze data gathered from a survey deployed in 2016 to local German residents to understand contextual factors influencing hosting communities’ perceptions toward disruptions from displaced persons on urban infrastructure and alternatives to provide infrastructure. Further, hosting communities’ perceptions toward infrastructure alternatives were simulated and compared with decisions made by local authorities regarding alternatives to provide infrastructure as an incremental step toward the integration of hosting communities as stakeholders. This dissertation reveals that local authorities and decision-makers in charge of the provision of urban infrastructure to displaced populations should account for hosting communities as a valid stakeholder. The hosting communities’ perceptions indicate that infrastructure alternatives may have different levels of support or opposition depending on local context. This reinforces the importance to plan and develop alternatives that are in conversation with the interests and concerns from hosting communities’ residents, which can minimize potential sources of public opposition from hosting communities.Civil, Architectural, and Environmental Engineerin

Enabling NATO’s Collective Defense: Critical Infrastructure Security and Resiliency (NATO COE-DAT Handbook 1)

In 2014 NATO’s Center of Excellence-Defence Against Terrorism (COE-DAT) launched the inaugural course on “Critical Infrastructure Protection Against Terrorist Attacks.” As this course garnered increased attendance and interest, the core lecturer team felt the need to update the course in critical infrastructure (CI) taking into account the shift from an emphasis on “protection” of CI assets to “security and resiliency.” What was lacking in the fields of academe, emergency management, and the industry practitioner community was a handbook that leveraged the collective subject matter expertise of the core lecturer team, a handbook that could serve to educate government leaders, state and private-sector owners and operators of critical infrastructure, academicians, and policymakers in NATO and partner countries. Enabling NATO’s Collective Defense: Critical Infrastructure Security and Resiliency is the culmination of such an effort, the first major collaborative research project under a Memorandum of Understanding between the US Army War College Strategic Studies Institute (SSI), and NATO COE-DAT. The research project began in October 2020 with a series of four workshops hosted by SSI. The draft chapters for the book were completed in late January 2022. Little did the research team envision the Russian invasion of Ukraine in February this year. The Russian occupation of the Zaporizhzhya nuclear power plant, successive missile attacks against Ukraine’s electric generation and distribution facilities, rail transport, and cyberattacks against almost every sector of the country’s critical infrastructure have been on world display. Russian use of its gas supplies as a means of economic warfare against Europe—designed to undermine NATO unity and support for Ukraine—is another timely example of why adversaries, nation-states, and terrorists alike target critical infrastructure. Hence, the need for public-private sector partnerships to secure that infrastructure and build the resiliency to sustain it when attacked. Ukraine also highlights the need for NATO allies to understand where vulnerabilities exist in host nation infrastructure that will undermine collective defense and give more urgency to redressing and mitigating those fissures.https://press.armywarcollege.edu/monographs/1951/thumbnail.jp

Hybrid systems modeling for critical infrastructures interdependency analysis

Critical infrastructure systems (CISs) are large scale and complex systems, across which many interdependencies exist. As a result, several modeling and simulation approaches are being employed to study the concurrent operation of multiple CISs and their interdependencies. Complementary to existing literature, this work develops and implements a modeling and simulation framework based on open hybrid automata to analyze CISs interdependencies. With the proposed approach, it is possible to develop accurate models of infrastructure components, and interlink them together based on their dependencies; in effect creating larger and more complex models that incorporate interdependencies. By implementing specific setups using varying operating conditions, one can study the cascading effects of interdependencies, perform a detailed vulnerability assessment and conduct an extensive planning exercise. To demonstrate the applicability of the proposed framework, a setup with three different types of CISs (i.e., power, telecom and water) components is investigated. Extensive simulation results are used to provide insights on the cascading effects, vulnerabilities and maintenance planning strategies

Identifying and Mitigating Security Risks in Multi-Level Systems-of-Systems Environments

In recent years, organisations, governments, and cities have taken advantage of the many benefits and automated processes Information and Communication Technology (ICT) offers, evolving their existing systems and infrastructures into highly connected and complex Systems-of-Systems (SoS). These infrastructures endeavour to increase robustness and offer some resilience against single points of failure. The Internet, Wireless Sensor Networks, the Internet of Things, critical infrastructures, the human body, etc., can all be broadly categorised as SoS, as they encompass a wide range of differing systems that collaborate to fulfil objectives that the distinct systems could not fulfil on their own. ICT constructed SoS face the same dangers, limitations, and challenges as those of traditional cyber based networks, and while monitoring the security of small networks can be difficult, the dynamic nature, size, and complexity of SoS makes securing these infrastructures more taxing. Solutions that attempt to identify risks, vulnerabilities, and model the topologies of SoS have failed to evolve at the same pace as SoS adoption. This has resulted in attacks against these infrastructures gaining prevalence, as unidentified vulnerabilities and exploits provide unguarded opportunities for attackers to exploit. In addition, the new collaborative relations introduce new cyber interdependencies, unforeseen cascading failures, and increase complexity. This thesis presents an innovative approach to identifying, mitigating risks, and securing SoS environments. Our security framework incorporates a number of novel techniques, which allows us to calculate the security level of the entire SoS infrastructure using vulnerability analysis, node property aspects, topology data, and other factors, and to improve and mitigate risks without adding additional resources into the SoS infrastructure. Other risk factors we examine include risks associated with different properties, and the likelihood of violating access control requirements. Extending the principals of the framework, we also apply the approach to multi-level SoS, in order to improve both SoS security and the overall robustness of the network. In addition, the identified risks, vulnerabilities, and interdependent links are modelled by extending network modelling and attack graph generation methods. The proposed SeCurity Risk Analysis and Mitigation Framework and principal techniques have been researched, developed, implemented, and then evaluated via numerous experiments and case studies. The subsequent results accomplished ascertain that the framework can successfully observe SoS and produce an accurate security level for the entire SoS in all instances, visualising identified vulnerabilities, interdependencies, high risk nodes, data access violations, and security grades in a series of reports and undirected graphs. The framework’s evolutionary approach to mitigating risks and the robustness function which can determine the appropriateness of the SoS, revealed promising results, with the framework and principal techniques identifying SoS topologies, and quantifying their associated security levels. Distinguishing SoS that are either optimally structured (in terms of communication security), or cannot be evolved as the applied processes would negatively impede the security and robustness of the SoS. Likewise, the framework is capable via evolvement methods of identifying SoS communication configurations that improve communication security and assure data as it traverses across an unsecure and unencrypted SoS. Reporting enhanced SoS configurations that mitigate risks in a series of undirected graphs and reports that visualise and detail the SoS topology and its vulnerabilities. These reported candidates and optimal solutions improve the security and SoS robustness, and will support the maintenance of acceptable and tolerable low centrality factors, should these recommended configurations be applied to the evaluated SoS infrastructure