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1. Analysis and Prevention of Security Vulnerabilities in a Smart City

   https://doi.org/10.1109/CCWC54503.2022.9720824  

   Lupton, Ben ; Zappe, Mackenzie ; Thom, Jay ; Sengupta, Shamik ; Feil-Seifer, Dave ( January 2022 , Computing and Communication Workshop and Conference (CCWC))

   In recent years, there has been a growing interest in so-called smart cities. These cities use technology to connect and enhance the lives of their citizens. Smart cities use many Internet of Things (loT) devices, such as sensors and video cameras, that are interconnected to provide constant feedback and up-to-date information on everything that is happening. Despite the benefits of these cities, they introduce a numerous new vulnerabilities as well. These smart cities are now susceptible to cyber-attacks that aim to “alter, disrupt, deceive, degrade, or destroy computer systems.” Through the use of an educational and research-based loT test-bed with multiple networking layers and heterogeneous devices connected to simultaneously support networking research, anomaly detection, and security principles, we can pinpoint some of these vulnerabilities. This work will contribute potential solutions to these vulnerabilities that can hopefully be replicated in smart cities around the world. Specifically, in the transportation section of our educational smart city several vulnerabilities in the signal lights, street lights, and the cities train network were discovered. To conduct this research two scenarios were developed. These consisted of inside the network security and network perimeter security. For the latter we were able to find extensive vulnerabilities that wouldmore »allow an attacker to map the entire smart city sub-network. Solutions to this problem are outlined that utilize an Intrusion Detection System and Port Mirroring. However, while we were able to exploit the city's Programmable Logic Controller (PLC) once inside the network, it was found that due to dated Supervisory Control and Data Acquisition (SCADA) systems, there were almost no solutions to these exploits.« less
2. An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning

   https://doi.org/10.1109/ICCC.2018.00010  

   Bura, Harshitha ; Lin, Nathan ; Kumar, Naveen ; Malekar, Sangram ; Nagaraj, Sushma ; Liu, Kaikai ( July 2018 , 2018 IEEE International Conference on Cognitive Computing (ICCC))

   The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems-including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when theymore »enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number.« less
3. Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China

   https://doi.org/10.1007/s41109‐020‐00345‐4  

   Small, Christopher ; Sousa, Daniel ( December 2021 , Applied Network Science)

   Abstract The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network componentsmore »(connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (10 4 to 10 5 km 2 ) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.« less
4. Spatiotemporal evolution of COVID-19 infection and detection within night light networks: comparative analysis of USA and China

   https://doi.org/10.1007/s41109-020-00345-4  

   Small, Christopher ; Sousa, Daniel ( February 2021 , Applied Network Science)

   The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network componentsmore »(connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (10⁴to 10⁵km²) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.

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5. Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling

   Ching, J ; Aliaga, D ; Mills, G ; Masson, V ; See, L ; Neophytou, M ; Middel, A ; Baklanov, A ; Ren, C ; Ng, E ; et al ( June 2019 , Urban climate)

   The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling withinmore »city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells.« less