skip to main content

Title: Smart Cities and the Challenges of Cross Domain Risk Management: Considering Interdependencies Between ICT-Security and Natural Hazards Disruptions
Abstract Research purpose. Smart City technologies offer great promise for a higher quality of life, including improved public services, in an era of rapid and intense global urbanization. The use of intelligent or smart information and communication technologies to produce more efficient systems of services in those urban areas, captured under the broad rubric of “smart cities,” also create new vectors of risk and vulnerability. The aim of this article is to raise consideration of an integrated cross-domain approach for risk reduction based on the risks smart cities are exposed to, on the one hand, from natural disasters and, on the other, from cyber-attacks. Design / Methodology / Approach. This contribution describes and explains the risk profile for which smart cities are exposed to both natural disasters and cyber-attacks. The vulnerability of smart city technologies to natural hazards and cyber-attacks will first be summarized briefly from each domain, outlining those respective domain characteristics. Subsequently, methods and approaches for risk reduction in the areas of natural hazards and ICT security will be examined in order to create the basis for an integrated cross-domain approach to risk reduction. Differences are also clearly identified if an adaptation of a risk reduction pattern appears more » unsuitable. Finally, the results are summarized into an initial, preliminary integrated cross-domain approach to risk reduction. Findings. Risk management in the two domains of ICT security and natural hazards is basically similar. Both domains use a multilayer approach in risk reduction, both have reasonably well-defined regimes and established risk management protocols. At the same time, both domains share a policymaking and policy implementation challenge of the difficulty of appropriately forecasting future risk and making corresponding resource commitments to address future risk. Despite similarities, different concepts like the CIA Triad, community resilience, absorption capacity and so on exist too. Future research of these concepts could lead to improve risk management. Originality / Value / Practical implications. Cyber-attacks on the ICT infrastructure of smart cities are a major vulnerability – but relatively little systematic evaluation exists on the topic. Likewise, ICT infrastructure is vulnerable to natural disasters too – and the risk of more severe natural disasters in the context of a global trend toward massive cities is increasing dramatically. Explicit consideration of the issues associated with cross-domain integration of reduction of interdependent risk is a necessary step in ensuring smart city technologies also serve to promote longer-term community sustainability and resilience. « less
Award ID(s):
Publication Date:
Journal Name:
Economics and Culture
Page Range or eLocation-ID:
106 to 116
Sponsoring Org:
National Science Foundation
More Like this
  1. Due to the critical importance of Industrial Control Systems (ICS) to the operations of cities and countries, research into the security of critical infrastructure has become increasingly relevant and necessary. As a component of both the research and application sides of smart city development, accurate and precise modeling, simulation, and verification are key parts of a robust design and development tools that provide critical assistance in the prevention, detection, and recovery from abnormal behavior in the sensors, controllers, and actuators which make up a modern ICS system. However, while these tools have potential, there is currently a need for helper-tools to assist with their setup and configuration, if they are to be utilized widely. Existing state-of-the-art tools are often technically complex and difficult to customize for any given IoT/ICS processes. This is a serious barrier to entry for most technicians, engineers, researchers, and smart city planners, while slowing down the critical aspects of safety and security verification. To remedy this issue, we take a case study of existing simulation toolkits within the field of water management and expand on existing tools and algorithms with simplistic automated retrieval functionality using a much more in-depth and usable customization interface to accelerate simulationmore »scenario design and implementation, allowing for customization of the cyber-physical network infrastructure and cyber attack scenarios. We additionally provide a novel in tool assessment of network’s resilience according to graph theory path diversity. Further, we lay out a roadmap for future development and application of the proposed tool, including expansions on resiliency and potential vulnerability model checking, and discuss applications of our work to other fields relevant to the design and operation of smart cities.« less
  2. The collection and use of digital data by “smart city” programs raise complex operational and ethical questions that can best be addressed through carefully-monitored pilot studies before urban innovations are more widely adopted. We have created a network of single-owner campuses (academic, government, corporate and nonprofit) in the Cascadia megaregion that connects Portland (OR), Seattle (WA) and Vancouver (BC), where smart city products and services can be evaluated before deployment in neighborhoods and business districts. On the five initial campuses, we are co-locating assemblages of up to a dozen technologies through which issues of data interoperability, management, privacy and monopolization can be explored. The initial research and policy goals of this network are to educate the public about smart cities, improve accessibility for populations with disabilities, prepare city residents for natural disasters, and monitor urban tree canopies so they can better mitigate the urban heat island effect. If replicated in other regions, this testing approach can accelerate cities' responsible integration of data science solutions that can address both local and global problems.
  3. Megacities are socio-ecological systems (SES) that encompass complex interactions between residents, institutions, and natural resource management. These interactions are exacerbated by climate change as resources such as water become scarce or hazardous through drought and flooding. In order to develop pathways for improved sustainability, the disparate factors that create vulnerable conditions and outcomes must be visible to decision-makers. Nevertheless, for such decision-makers to manage vulnerability effectively, they need to define the salient boundaries of the urban SES, and the relevant biophysical, technological, and socio-institutional attributes that play critical roles in vulnerability dynamics. Here we explore the problem of hydrological risk in Mexico City, where vulnerabilities to flooding and water scarcity are interconnected temporally and spatially, yet the formal and informal institutions and actors involved in the production and management of vulnerability are divided into two discrete problem domains: land-use planning and water resource management. We analyze interviews with city officials working in both domains to understand their different perspectives on the dynamics of socio-hydrological risk, including flooding and water scarcity. We find governance gaps within land-use planning and water management that lead to hydro-social risk, stemming from a failure to address informal institutions that exacerbate vulnerability to flooding and watermore »scarcity. Mandates in both sectors are overlapping and confusing, while socio-hydrological risk is externalized to the informal domain, making it ungoverned. Integrated water management approaches that recognize and incorporate informality are needed to reduce vulnerability to water scarcity and flooding.« less
  4. Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction lossmore »while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.« less
  5. High-quality temperature data at a finer spatio-temporal scale is critical for analyzing the risk of heat exposure and hazards in urban environments. The variability of urban landscapes makes cities a challenging environment for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts; first, the spatio-temporal granularities are too coarse, and second, the inability to track the ambient air temperature (AAT) instead of land surface temperature (LST). Overcoming these limitations requires developing models for mapping the variability in heat exposure in urban environments. We investigated an integrated approach for mapping urban heat hazards by harnessing a diverse set of high-resolution measurements, including both ground-based and satellite-based temperature data. We mounted vehicle-borne mobile sensors on city buses to collect high-frequency temperature data throughout 2018 and 2019. Our research also incorporated key biophysical parameters and Landsat 8 LST data into Random Forest regression modeling to map the hyperlocal variability of heat hazard over areas not covered by the buses. The vehicle-borne temperature sensor data showed large temperature differences within the city, with the largest variations of up to 10 °C and morning-afternoon diurnal changes at a magnitude around 20 °C. Random Forest modeling on noontime (11:30more »am – 12:30 pm) data to predict AAT produced accurate results with a mean absolute error of 0.29 °C and successfully showcased the enhanced granularity in urban heat hazard mapping. These maps revealed well-defined hyperlocal variabilities in AAT, which were not evident with other research approaches. Urban core and dense residential areas revealed larger than 5 °C AAT differences from their nearby green spaces. The sensing framework developed in this study can be easily implemented in other urban areas, and findings from this study will be beneficial in understanding the heat vulnerabilities of individual communities. It can be used by the local government to devise targeted hazard mitigation efforts such as increasing green space, developing better heatsafety policies, and exposure warning for workers.« less