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1. IoT Security and Safety Testing Toolkits for Water Distribution Systems

   https://doi.org/10.1109/IOTSMS53705.2021.9704886  

   O'Toole, Sean ; Sewell, Cameron ; Mehrpouyan, Hoda ( December 2021 , 2021 8th International Conference on Internet of Things: Systems, Management and Security (IOTSMS))

   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. Digital City Testbed Center: Using campuses as smart city testbeds in the binational Cascadia region

   https://doi.org/10.1109/SMARTCOMP50058.2020.00078  

   Fink, Jonathan ( September 2020 , 2020 IEEE International Conference on Smart Computing (SMARTCOMP))

   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. Governing the gaps in water governance and land-use planning in a megacity: The example of hydrological risk in Mexico City

   https://doi.org/https://doi.org/10.1016/j.cities.2018.06.009  

   Lerner, Amy M. ; Eakin, Hallie C. ; Tellman, Elizabeth ; Bausch, Julia Chrissie ; Hernández Aguilar, Bertha ( January 2018 , Cities)

   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. Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks

   https://doi.org/10.1109/RWS47064.2019.8971816  

   Zhou, Xingyu ; Li, Yi ; Barreto, Carlos A. ; Li, Jiani ; Volgyesi, Peter ; Neema, Himanshu ; Koutsoukos, Xenofon ( November 2019 , 2019 Resilience Week (RWS))

   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. Urban ambient air temperature estimation using hyperlocal data from smart vehicle-borne sensors

   Yin, Y ; Hashemi, N ; Grundstein, A ; Mishra, D. R ; Ramaswamy, L ; Dowd, J ( July 2020 , Computers environment and urban systems)

   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