Three paradigms to deal with urban water issues are compared. The analysis focuses on their definition and objectives, the role of different stakeholders, the issues they deal with, and the possible solutions suggested. The paradigms differ in scope (from the narrow focus of the sponge city paradigm to the broad goals of eco-city paradigm) and in terms of the governance structures used to coordinate different stakeholders. The smart and sponge paradigms mainly use existing government structures. In the eco-cities approach, the citizens want to be involved through newly created governance structures. Smart and eco-city initiatives emphasize the involvement of stakeholders, while in the sponge cities approach, the initiative is often taken by the local government. Finally, in terms of expected solutions, the paradigms want to create eco- or healthy cities or improve water management to create a more healthy urban environment. After identifying the issue, alternative water-related technologies are available, like generating energy from wastewater or separating grey and brown water. Cities require different governance structures, and managing information flows in an integrated way to solve water and other issues. The experience in Europe, China, and India may help other cities choose the right paradigm.

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With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method for identifying toxicological biological indicators of human exposure in smart cities based on public health data and deep learning to achieve accurate assessment and management of exposure risks. Initially, the study used a network of sensors within the smart city infrastructure to collect environmental monitoring data, including indicators such as air quality, water quality, and soil pollution. Using public health data, a database containing information on types and concentrations of environmental pollutants has been established. Convolutional neural network was used to recognize the pattern of environmental monitoring data, identify the relationship between different indicators, and build the correlation model between health indicators and environmental indicators. Identify biological indicators associated with environmental pollution exposure through training optimization. Experimental analysis showed that the prediction accuracy of the model reached 93.45%, which could provide decision support for the government and the health sector. In the recognition of the association pattern between respiratory diseases, cardiovascular diseases and environmental exposure factors such as PM2.5 and SO2, the fitting degree between the model and the simulation value reached more than 0.90. The research design model can play a positive role in public health and provide new decision-making ideas for protecting public health.

Climate change has already begun to take visible effect globally in recent years. Given the climate change paradox and urbanization trends, cities\' success would not only depend on smartness and sustainability, but also resilience to all forthcoming economic, environmental, or behavioral changes. Numerous technologies have surfaced and proved effective in CO2 removal from the local environment. However, the optimal placement of these smart filters is a complex task and require logical and strategic decision-making. Determining the optimal location is one of the key factors for establishing a network of smart air filters. This study used a GIS-based suitability analysis for identifying optimal locations for smart filters based on pollution hotspots (population and spatial proximity to industry, commercial centers, roads, high-traffic areas, and intersections). The spatial analysis involves the determination and preparation of input layers, ranking layers, assigning weights to each criterion, and generation of a suitability map. The sites with a higher suitability score (7 or above) are optimum sites for air filters. The sites are spatially distributed over different regions. The findings revealed that GIS-based suitability analysis can be an effective technique for placing smart filters within an urban environment. These findings can help decision-makers to prioritize the location considering environmental constraints. The proposed solution aims to pave the way for fostering resilient, smart, and sustainable cities through a community sensing platform targeting hotspots within spatial variations.

The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of artificial intelligence (AI) with the Internet of Things (IoT), is commonly used to promote efficient control and diagnosis during the outbreak, thereby minimizing possible losses. However, the presence of multi-source institutional data remains one of the major challenges hindering the practical usage of AIoT solutions for pandemic disease diagnosis. This paper presents a novel framework that utilizes multi-site data fusion to boost the accurateness of pandemic disease diagnosis. In particular, we focus on a case study of COVID-19 lesion segmentation, a crucial task for understanding disease progression and optimizing treatment strategies. In this study, we propose a novel multi-decoder segmentation network for efficient segmentation of infections from cross-domain CT scans in smart cities. The multi-decoder segmentation network leverages data from heterogeneous domains and utilizes strong learning representations to accurately segment infections. Performance evaluation of the multi-decoder segmentation network was conducted on three publicly accessible datasets, demonstrating robust results with an average dice score of 89.9% and an average surface dice of 86.87%. To address scalability and latency issues associated with centralized cloud systems, fog computing (FC) emerges as a viable solution. FC brings resources closer to the operator, offering low latency and energy-efficient data management and processing. In this context, we propose a unique FC technique called PANDFOG to deploy the multi-decoder segmentation network on edge nodes for practical and clinical applications of automated COVID-19 pneumonia analysis. The results of this study highlight the efficacy of the multi-decoder segmentation network in accurately segmenting infections from cross-domain CT scans. Moreover, the proposed PANDFOG system demonstrates the practical deployment of the multi-decoder segmentation network on edge nodes, providing real-time access to COVID-19 segmentation findings for improved patient monitoring and clinical decision-making.

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers\' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors\' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers\' privacy.

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin-Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs.

ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article\'s key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making.

In this paper, we explore the use of visible light positioning (VLP) technology in vehicles in intelligent transportation systems (ITS), highlighting its potential for maintaining effective line of sight (LOS) and providing high-accuracy positioning between vehicles. The proposed system (V2V-VLP) is based on a position-sensitive detector (PSD) and exploiting car taillights to determine the position and inter-vehicular distance by angle of arrival (AoA) measurements. The integration of the PSD sensor in vehicles promises exceptional positioning accuracy, opening new prospects for navigation and driving safety. The results revealed that the proposed system enables precise measurement of position and distance between vehicles, including lateral distance. We evaluated the impact of different focal lengths on the system performance, achieving cm-level accuracy for distances up to 35 m, with an optimum focal length of 25 mm, and under low signal-to-noise conditions, which meets the standards required for safe and reliable V2V applications. Several experimental tests were carried out to validate the results of the simulations.

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network\'s overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes.