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

Vulnerability-Based Impact Criticality Estimation for Industrial Control Systems

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system

Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models

Critical infrastructure systems are becoming increasingly interdependent, which can exacerbate the impacts of disruptive events through cascading failures, hindered asset repairs and network congestion. Current resilience assessment methods fall short of fully capturing such interdependency effects as they tend to model asset reliability and network flows separately and often rely on static flow assignment methods. In this paper, we develop an integrated, dynamic modelling and simulation framework that combines network and asset representations of infrastructure systems and models the optimal response to disruptions using a rolling planning horizon. The framework considers dependencies pertaining to failure propagation, system-of-systems architecture and resources required for operating and repairing assets. Stochastic asset failure is captured by a scenario tree generation algorithm whereas the redistribution of network flows and the optimal deployment of repair resources are modelled using a minimum cost flow approach. A case study on London’s metro and electric power networks shows how the proposed methodology can be used to assess the resilience of city-scale infrastructure systems to a local flooding incident and estimate the value of the resilience loss triangle for different levels of hazard exposure and repair capabilities

A review of cyber threats and defence approaches in emergency management

Emergency planners, first responders and relief workers increasingly rely on computational and communication systems that support all aspects of emergency management, from mitigation and preparedness to response and recovery. Failure of these systems, whether accidental or because of malicious action, can have severe implications for emergency management. Accidental failures have been extensively documented in the past and significant effort has been put into the development and introduction of more resilient technologies. At the same time researchers have been raising concerns about the potential of cyber attacks to cause physical disasters or to maximise the impact of one by intentionally impeding the work of the emergency services. Here, we provide a review of current research on the cyber threats to communication, sensing, information management and vehicular technologies used in emergency management. We emphasise on open issues for research, which are the cyber threats that have the potential to affect emergency management severely and for which solutions have not yet been proposed in the literature

Disaster risk management of interdependent infrastructure systems for community resilience planning

This research focuses on developing methodologies to model the damage and recovery of interdependent infrastructure systems under disruptive events for community resilience planning. The overall research can be broadly divided into two parts: developing a model to simulate the post-disaster performance of interdependent infrastructure systems and developing decision frameworks to support pre-disaster risk mitigation and post-disaster recovery planning of the interdependent infrastructure systems towards higher resilience. The Dynamic Integrated Network (DIN) model is proposed in this study to simulate the performance of interdependent infrastructure systems over time following disruptive events. It can consider three different levels of interdependent relationships between different infrastructure systems: system-to-system level, system-to-facility level and facility-to-facility level. The uncertainties in some of the modeling parameters are modeled. The DIN model first assesses the inoperability of the network nodes and links over time to simulate the damage and recovery of the interdependent infrastructure facilities, and then assesses the recovery and resilience of the individual infrastructure systems and the integrated network utilizing some network performance metrics. The recovery simulation result from the proposed model is compared to two conventional models, one with no interdependency considered, and the other one with only system-level interdependencies considered. The comparison results suggest that ignoring the interdependencies between facilities in different infrastructure systems would lead to poorly informed decision making. The DIN model is validated through simulating the recovery of the interdependent power, water and cellular systems of Galveston City, Texas after Hurricane Ike (2008). Implementing strategic pre-disaster risk mitigation plan to improve the resilience of the interdependent infrastructure systems is essential for enhancing the social security and economic prosperity of a community. Majority of the existing infrastructure risk mitigation studies or projects focus on a single infrastructure system, which may not be the most efficient and effective way to mitigate the loss and enhance the overall community disaster resilience. This research proposes a risk-informed decision framework which could support the pre-disaster risk mitigation planning of several interdependent infrastructure systems. The characteristics of the Interdependent Infrastructure Risk Mitigation (IIRM) decision problem, such as objective, decision makers, constraints, etc., are clearly identified. A four-stage decision framework to solve the IIRM problem is also presented. The application of the proposed IIRM decision framework is illustrated using a case study on pre-disaster risk mitigation planning for the interdependent critical infrastructure systems in Jamaica. The outcome of the IIRM problem is useful for the decision makers to allocate limited risk mitigation budget or resources to the most critical infrastructure facilities in different systems to achieve greater community disaster resilience. Optimizing the post-disaster recovery of damaged infrastructure systems is essential to alleviate the adverse impacts of natural disasters to communities and enhance their disaster resilience. As a result of infrastructure interdependencies, the complete functional restoration of a facility in one infrastructure system relies on not only the physical recovery of itself, but also the recovery of the facilities in other systems that it depends on. This study introduces the Interdependent Infrastructure Recovery Planning (IIRP) problem, which aims at optimizing the assignment and scheduling of the repair teams for an infrastructure system with considering the repair plan of the other infrastructure systems during the post-disaster recovery phase. Key characteristics of the IIRP problem are identified and a game theory-based IIRP decision framework is presented. Two recovery time-based performance metrics are introduced and applied to evaluate the efficiency and effectiveness of the post-disaster recovery plan. The IIRP decision framework is illustrated using the interdependent power and water systems of the Centerville virtual community subjected to seismic hazard

A systems approach to analyze the robustness of infrastructure networks to complex spatial hazards

Ph. D. ThesisInfrastructure networks such as water supply systems, power networks, railway networks, and road networks provide essential services that underpin modern society’s health, wealth, security, and wellbeing. However, infrastructures are susceptible to damage and disruption caused by extreme weather events such as floods and windstorms. For instance, in 2007, extensive disruption was caused by floods affecting a number of electricity substations in the United Kingdom, resulting in an estimated damage of GBP£3.18bn (US$4bn). In 2017, Hurricane Harvey hit the Southern United States, causing an approximated US$125bn (GBP£99.35bn) in damage due to the resulting floods and high winds. The magnitude of these impacts is at risk of being compounded by the effects of Climate Change, which is projected to increase the frequency of extreme weather events. As a result, it is anticipated that an estimated US$3.7tn (GBP£2.9tn) in investment will be required, per year, to meet the expected need between 2019 and 2035. A key reason for the susceptibility of infrastructure networks to extreme weather events is the wide area that needs to be covered to provide essential services. For example, in the United Kingdom alone there are over 800,000 km of overhead electricity cables, suggesting that the footprint of infrastructure networks can be as extended as that of an entire Country. These networks possess different spatial structures and attributes, as a result of their evolution over long timeframes, and respond to damage and disruption in different and complex ways. Existing approaches to understanding the impact of hazards on infrastructure networks typically either (i) use computationally expensive models, which are unable to support the investigation of enough events and scenarios to draw general insights, or (ii) use low complexity representations of hazards, with little or no consideration of their spatial properties. Consequently, this has limited the understanding of the relationship between spatial hazards, the spatial form and connectivity of infrastructure networks, and infrastructure reliability. This thesis investigates these aspects through a systemic modelling approach, applied to a synthetic and a real case study, to evaluate the response of infrastructure networks to spatially complex hazards against a series of robustness metrics. In the first case study, non-deterministic spatial hazards are generated by a fractal method which allows to control their spatial variability, resulting in spatial configurations that very closely resemble natural phenomena such as floods or windstorms. These hazards are then superimposed on a range of synthetic network layouts, which have spatial structures consistent with real infrastructure networks reported in the literature. Failure of network components is initially determined as a function of hazard intensity, and cascading failure of further components is also investigated. The performance of different infrastructure configurations is captured by an array of metrics which cover different aspects of robustness, ranging from the proneness to partitioning to the ability to process flows in the face of disruptions. Whereas analyses to date have largely adopted low complexity representations of hazards, this thesis shows that consideration of a high complexity representation which includes hazard spatial variability can reduce the robustness of the infrastructure network by nearly 40%. A “small-world” network, in which each node is within a limited number of steps from any other node, is shown to be the most robust of all the modelled networks to the different structures of spatial hazard. The second case study uses real data to assess the robustness of a power supply network operating in the Hull region in the United Kingdom, which is split in high and low voltage lines. The spatial hazard is represented by a high-resolution wind gust model and tested under current and future climate scenarios. The analysis reveals how the high and low voltage lines interact with each other in the event of faults, which lines would benefit the most from increased robustness, and which are most exposed to cascading failures. The second case study also reveals the importance of the spatial footprint of the hazard relative to the location of the infrastructure, and how particular hazard patterns can affect low voltage lines that are more often located in exposed areas at the edge of the network. The impact of Climate Change on windstorms is highly uncertain, although it could further reduce network robustness due to more severe events. Overall the two case studies provide important insights for infrastructure designers, asset managers, the academic sector, and practitioners in general. In fact, in the first case study, this thesis defines important design principles, such as the adoption of a small-world network layout, that can integrate the traditional design drivers of demand, efficiency, and cost. In the second case study, this thesis lays out a methodology that can help identify assets requiring increased robustness and protection against cascading failures, resulting in more effective prioritized infrastructure investments and adaptation plans

Improving cyber security in industrial control system environment.

Integrating industrial control system (ICS) with information technology (IT) and internet technologies has made industrial control system environments (ICSEs) more vulnerable to cyber-attacks. Increased connectivity has brought about increased security threats, vulnerabilities, and risks in both technology and people (human) constituents of the ICSE. Regardless of existing security solutions which are chiefly tailored towards technical dimensions, cyber-attacks on ICSEs continue to increase with a proportionate level of consequences and impacts. These consequences include system failures or breakdowns, likewise affecting the operations of dependent systems. Impacts often include; marring physical safety, triggering loss of lives, causing huge economic damages, and thwarting the vital missions of productions and businesses. This thesis addresses uncharted solution paths to the above challenges by investigating both technical and human-factor security evaluations to improve cyber security in the ICSE. An ICS testbed, scenario-based, and expert opinion approaches are used to demonstrate and validate cyber-attack feasibility scenarios. To improve security of ICSs, the research provides: (i) an adaptive operational security metrics generation (OSMG) framework for generating suitable security metrics for security evaluations in ICSEs, and a list of good security metrics methodology characteristics (scope-definitive, objective-oriented, reliable, simple, adaptable, and repeatable), (ii) a technical multi-attribute vulnerability (and impact) assessment (MAVCA) methodology that considers and combines dynamic metrics (temporal and environmental) attributes of vulnerabilities with the functional dependency relationship attributes of the vulnerability host components, to achieve a better representation of exploitation impacts on ICSE networks, (iii) a quantitative human-factor security (capability and vulnerability) evaluation model based on human-agent security knowledge and skills, used to identify the most vulnerable human elements, identify the least security aspects of the general workforce, and prioritise security enhancement efforts, and (iv) security risk reduction through critical impact point assessment (S2R-CIPA) process model that demonstrates the combination of technical and human-factor security evaluations to mitigate risks and achieve ICSE-wide security enhancements. The approaches or models of cyber-attack feasibility testing, adaptive security metrication, multi-attribute impact analysis, and workforce security capability evaluations can support security auditors, analysts, managers, and system owners of ICSs to create security strategies and improve cyber incidence response, and thus effectively reduce security risk.PhD in Manufacturin

Seismic risk assessment of complex urban critical infrastructure networks

Existing practice on seismic risk assessment of critical infrastructure systems is reviewed in terms of exposure, hazard, fragility, performance and interdependencies and a framework is proposed for seismic risk assessment model development for the insurance sector. The application of the framework is demonstrated with the electric power and water supply systems in Christchurch, New Zealand. This includes the development of the first ground motion residual spatial correlation model for the region and a simplified method for predicting the occurrence of liquefaction. Empirical data from the 2010-11 Canterbury earthquake sequence are used to derive new fragility functions for substations, buried cables, wells, pumping stations and pipes, in terms of both ground shaking and permanent ground deformation. The model is tested against performance observations from the February 22nd 2011 MW 6.2 Christchurch earthquake and achieves reasonable results when interdependencies between substations and pumps are modelled by nodal analysis with mapped substation supply zones rather than proximity rules. The model is applied to construct a future risk projection and in a 10,000-year stochastic catalogue, the electric power network exhibits high reliability with performance loss in only 2% of events. The water supply system is less reliable, due to the effect of ground shaking and liquefaction on pipes and the effect of power loss on the functionality of pumps, which is shown to increase disconnections by up 30%. A new metric, the interdependency index, is proposed to measure the degree of the dependency of the water supply system on electric power. It is adapted from the Leontief input-output method for infrastructure interdependency modelling and makes use of the system performance results acquired from the future risk projection, by assuming a linear relationship between the change in performance of the water supply system due to power loss and the performance of the electric power network

Strategic planning of supply chains in global emergency logistics

This study discusses the basis of effort considered in defining and analysing the Critical Success Factors (CSFs) essential for ensuring that disaster aid logistics are both effective and appropriate. The study classifies, first, the elements which are most significant to Emergency Aid Organisations and Humanitarian Relief in providing an effective response in disaster situations, next, the variables which affect the efficiency of each. From field and desk research, the extent to which CSFs are understood and recognised within relief activities is evaluated. Furthermore, it merges the concept of Just In Time (JIT) and the campaign system in emergency supply chain, so that when the disaster happens the affected country can request help from the nearest regional warehouse, which will supply the relief material and the required stuff to support and assist the victims in the disaster area. The regional warehouse places an order to the continent warehouse to replenish the material that is distributed to the disaster area. This study develops a forecasting tool based on identifying probability distributions. The estimates of the parameters are used to calculate natural disaster forecasts. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning. Subsequently, a simulation model has been developed to integrate the forecasting tool with the proposed distribution network and the inventory stock. The simulation model has two stages; the first one is finding the demand, type of disaster and the location based on the forecasting models, followed by comparing the demand result with the actual number to validate the stage. Next stage of the model connects the demands with proposed distribution network and the inventory stock to find the waiting time to deliver the relief material. The proposed model does not exceed two days of waiting time. This study investigates how natural disasters disturb supply chain processes in the Asia-Pacific context and how universal supply chains develop the risks of natural disasters. The study first discusses the emergence and development of global supply chains in the Asia-Pacific region and then examines how these new developments globalize disaster risks and bring extra vulnerability to businesses, particularly to their production networks. Following this, the study describes the impact of natural disasters on the global supply chains, on the basis of two natural disasters that occurred in 2011 in the region: the Great East Japan earthquake and the South-East Asian floods (focusing on the flood of Thailand). Finally, two policy options are proposed to enhance disaster resilience for business in the context of globalization