Smart Cities

The increasing use of mobile networks is an opportunity to collect and model users’ movement data for extracting knowledge about life and health while considering privacy leakage risk. This study aims to approximate the lifestyles of urban residents, employing statistical information derived from their movements among various Points of Interest (PoI). Our investigations comprehend a multidimensional analysis of key urban factors to provide insights into the population’s daily routines, preferences, and characteristics. To this end, we developed a framework called LEAF that models lifestyles by interpreting anonymized cell phone mobility data and integrating it with information from other sources, such as geographical layers of land use and sets of PoI. LEAF presents the information in a vector space model capable of responding to spatial queries about lifestyle. We also developed a consolidated lifestyle pattern framework to systematically identify and analyze the dominant activity patterns in different urban areas. To evaluate the effectiveness of the proposed framework, we tested it on movement data from individuals in a medium-sized city and compared the results with information collected through surveys. The RMSE of $5.167$ between the proposed framework’s results and survey-based data indicates that the framework provides a reliable estimation of lifestyle patterns across diverse urban areas. Additionally, summarized patterns of criteria ordering were created, offering a concise and intuitive representation of lifestyles. The analysis revealed high consistency between the two methods in the derived patterns, underscoring the framework’s robustness and accuracy in modeling urban lifestyle dynamics. Full article

The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles. Full article

Small wind turbines placed at city locations are affected by variable-speed winds that frequently change direction. Architectural constructions, buildings of different heights and abrupt orography of Cities make the winds that occur at City locations more variable than in flat lands or at sea. However, the performance of Small-wind turbines under this type of variable wind has not been deeply studied in the specialised literature. Therefore, this article analyses the behaviour of small wind turbines under variable and gusty winds of cities, also considering three types of power electronics conversion configurations: the generally used Maximum Power Point Tracking (MPPT) configuration, the simple only-rectifier configuration and an intermediate configuration in terms of complexity called pseudo-MPPT. This general-purpose analysis is applied to a specific type of wind turbine, i.e., the Ayanz wind turbine with screw blades, which presents adequate characteristics for city locations such as; safety, reduced visual and acoustic impacts and bird casualties avoidance. Thus, a wide simulation and experimental tests-based analysis are carried out, identifying the main factors affecting the maximisation of energy production of small wind turbines in general and the Ayanz turbine in particular. It is concluded that the mechanical inertia of the wind turbine, often not even considered in the energy production analysis, is a key factor that can produce decrements of up to 25% in energy production. Then, it was also found that electric factors related to the power electronics conversion system can strongly influence energy production. Thus, it is found that an adequate design of a simple pseudo-MPPT power conversion system could extract even 5% more energy than more complex MPPT configurations, especially in quickly varying winds of cities. Full article

Reducing carbon emissions is a critical issue for the near future as climate change is an imminent reality. To reduce our carbon footprint, society must change its habits and behaviours to optimise energy consumption, and the current progress in embedded systems and artificial intelligence has the potential to make this easier. The smart building concept and intelligent energy management are key points to increase the use of renewable sources of energy as opposed to fossil fuels. In addition, cyber-physical systems (CPSs) provide an abstraction of the management of services that allows the integration of both virtual and physical systems in a seamless control architecture. In this paper, we propose to use multiagent reinforcement learning (MARL) to model the CPS services control plane in a smart house, with the purpose of minimising, by shifting or shutdown services, the use of non-renewable energy (fuel generator) by exploiting solar production and batteries. Furthermore, our proposal dynamically adapts its behaviour in real time according to current and historic energy production, thus being able to handle occasional changes in energy production due to meteorological phenomena or unexpected energy consumption. In order to evaluate our proposal, we have developed an open-source smart building energy simulator and deployed our use case. Finally, several simulations with different configurations are evaluated to verify the performance. The simulation results show that the reinforcement learning solution outperformed the priority-based and the heuristic-based solutions in both power consumption and adaptability in all configurations. Full article

Urbanization growth poses various challenges, such as congestion, pollution, and resource consumption, prompting city planners and governments to adopt smart systems to manage these issues more efficiently. Despite widespread adoption, there is no consensus on the defining attributes of smart cities, particularly regarding their role in urban sustainability and contemporary urbanism. This paper provides a literature review to understand the implications of smart city initiatives for sustainable urban planning, focusing on practices in Singapore, Helsinki, Barcelona, and Medellin. Based on 71 publications surveyed from Scopus and Web of Science, this paper evaluates smart, sustainable initiatives undertaken in these four cities across six smart domains: mobility, governance, environment, people, living, and economy. This review shows that most studies focus on Barcelona and Singapore, particularly in the domains of smart environment and governance. Despite differing urban contexts, the notion of “smart” is closely tied to using information and communication technologies to drive urban operations. This analysis identifies a lack of assessment studies on the benefits of smart cities in terms of urban sustainability and a lack of holistic approaches to address the complex challenges cities face in achieving sustainable development. Full article

Buildings and their supporting infrastructure are vulnerable to both natural and human-made disasters, which pose significant risks to the safety of the occupants. Evacuation models are essential tools for assessing these risks and for ensuring the safety of individuals during emergencies. The primary objective of an evacuation model is to realistically simulate the process by which a large group of people can reach available exits efficiently. This paper introduces an agent-based evacuation model that represents the environment as a rectangular grid, where individuals, obstacles, and exits interact dynamically. The model employs only five rules to simulate evacuation dynamics while also accounting for complex factors such as movement and stagnation. Different from many evacuation models, this approach includes rules that account for common behaviors exhibited in stressful evacuation situations such as accidents, hysteria, and disorientation. By incorporating these behavioral conditions, the model more accurately reflects the real-life reactions of individuals during evacuation, leading to more realistic and applicable results. To validate the effectiveness of the proposed model, comprehensive experiments and case studies are conducted in diverse urban settings. The results of these experiments demonstrate that the model offers valuable insights into the evacuation process and provides a more precise assessment of its behavior in emergency scenarios. Full article

The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem. Full article

Digital-twin platforms are increasingly adopted in energy infrastructure management for smart grids. Novel opportunities arise from emerging artificial intelligence technologies to increase user trust by enhancing predictive and prescriptive analytics capabilities and by improving user interaction paradigms. This paper presents a novel data-driven and knowledge-based energy digital-twin framework and architecture. Data integration and mining based on machine learning are integrated into a knowledge graph annotating asset status data, prediction outcomes, and background domain knowledge in order to support a retrieval-augmented generation approach, which enhances a conversational virtual assistant based on a large language model to provide user decision support in asset management and maintenance. Components of the proposed architecture have been mapped to commercial-off-the-shelf tools to implement a prototype framework, exploited in a case study on the management of a section of the high-voltage energy infrastructure in central Italy. Full article

Employing vehicle telematics data, this study investigates the transport environment across urban and major road networks during a two-week period encompassing the Easter holidays, considered as a case study. The analysis spans four distinct years: 2016, 2018, 2021, and 2022. Geospatial and Temporal Mapping captured the dependencies of vehicle speed, acceleration, vehicle-specific power (VSP), and emission factors (EFs) for air pollutants (CO₂ and NO_x) on the studied calendar period. The results showed that during the Easter holiday, the median vehicle speeds exceeded annual averages by roughly 5%, indicating a clear deviation from regular traffic patterns. This deviation was particularly stark during the 2021 lockdown, with a significant drop in vehicle presence, leading to less congestion and thus higher speeds and vehicle acceleration. The emissions analyses revealed that individual cars emit higher levels of CO₂ and NO_x during Easter. Specifically, the median values of CO₂ EF and NO_x EF were 9% and 11% higher than the annual norm. When combined with road occupancy data, the results demonstrate that the Easter holidays in 2022 had a variable impact on NO_x and CO₂ emissions, with significant reductions on major roads during weekday rush hours (15–25%) but slight increases on urban roads during weekend periods. Full article

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards. Full article

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities. Full article

Critical infrastructure (CI) networks face diverse natural hazards, such as flooding. CI network modeling methods are used to evaluate these hazards, enabling the analysis of cascading effects, flood risk, and potential flood risk-reducing measures. However, there is a lack of linkage between analytical methods and potential multisectoral, structural, and nonstructural measures. This deficiency impedes the development of CI network (CIN) models as robust tools for active flood risk management. CI operators have significant expertise in managing and implementing flooding-related measures within their sectors. The objective of this study is to bridge the gap between the application of CIN modeling and the consideration of flood measures in three steps. The first step is conducting a literature review and CI stakeholder interviews in Central Europe on flood measures. The second step is the culmination of the findings in a comprehensive catalog detailing flood measures tailored to five CI sectors, with a generalized category spanning each phase of the disaster risk management cycle. The third step is the validation of the catalog’s utility in a proof-of-concept study along the Vicht River in Western Germany with a model-based flood risk analysis of five flood measures. The application of the flood measure catalog improves the options available for active and residual flood risk management. Additionally, the CI flood risk modeling approach presented here allows for consideration of disruption duration and recovery capability, thus linking the concept of risk and resilience. Full article

Today, historic villages represent a widespread and relevant reality of the Italian administrative structure. To preserve their value for future generations, smart city applications can contribute to implement effective monitoring and decision-making processes devoted to safeguarding their fragile ecosystem. Starting from a situational awareness model, this study proposes a method for designing human-in-the-loop cyber-physical systems that allow the design of monitoring and decision-making applications for historic villages. Both the model and the design method can be used as a reference for the realization of human-in-the-loop cyber-physical systems that consist of human beings, smart objects, edge devices, and cloud components in edge-cloud architectures. The output of the research, consisting of the graphical models for the definition of monitoring architectures and the method for the design of human-in-the-loop cyber-physical systems, was validated in the context of the village of Sant’Agata dei Goti through the implementation of a human-in-the-loop cyber-physical system for monitoring sites aiming at their management, conservation, protection, and fruition. Full article

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives. Full article

This study analyses five months of continuous monitoring of different lighting warning systems at a pedestrian crosswalk through video surveillance cameras during nighttime. Three different light signalling systems were installed near a pedestrian crossing to improve the visibility and safety of vulnerable road users: in-curb LED strips, orange flashing beacons, and asymmetric enhanced LED lighting. Seven different lighting configurations of the three systems were studied and compared with standard street lighting. The speed of vehicles for each pedestrian–driver interaction was also evaluated. This was then compared to the speed that vehicles should maintain in order to stop in time and allow pedestrians to cross the road safely. In all of the conditions studied, speeds were lower than those maintained in the five-month presence of standard street lighting (42.96 km/h). The results show that in conditions with dedicated flashing LED lighting, in-curb LED strips, and orange flashing beacons, most drivers (72%) drove at a speed that allowed the vehicle to stop safely compared to standard street lighting (10%). In addition, with this lighting configuration, the majority of vehicles (85%) stopped at pedestrian crossings, while in standard street lighting conditions only 26% of the users stopped to give way to pedestrians. Full article

This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities. Full article

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development. Full article

Public transportation has been identified as a viable solution to mitigate traffic congestion. Transit signal priority (TSP) control, which is widely used at signalized intersections, has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. However, traditional TSP control may fall short of efficiency and is facing several challenges of negative externalities for non-transit users and the need to handle conflicting priority requests. Recent studies have proposed the use of reinforcement learning (RL) methods to identify efficient traffic signal control (TSC). Some of these studies on RL-based TSC have incorporated the concept of max-pressure (MP), which is a maximal weight-matching algorithm to minimize queue sizes. Nevertheless, the existing RL-based TSC methods focus on private vehicles and cannot adequately distinguish between buses and private vehicles. In prior research, RL-based control has been implemented within the context of bus rapid transit (BRT) systems. This study proposes a novel RL-based TSC strategy that leverages the MP concept and extends it to incorporate TSP control. This is the first implementation of RL-based TSP control within the mixed-traffic road network. A significant innovation of this research is the introduction of the priority factor (PF), which is designed to prioritize bus movements at signalized intersections. The proposed RL-based TSP with PF control seeks to balance the competing objectives of enhancing bus operations while mitigating adverse impacts on non-transit users. To evaluate the performance of the proposed TSP method with the PF mechanism, simulations were conducted on an arterial and a grid network under dynamic traffic conditions. The simulation results demonstrated that the proposed TSP with PF not only reduces bus travel times and resolves conflicts between priority requests but also does not make a significant negative impact on passenger car operations. Furthermore, the PF can be dynamically assigned according to the number of passengers on each bus, suggesting the potential for the proposed approach to be applied in various traffic management scenarios. Full article

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. Full article