Smart Cities, Volume 6, Issue 5 (October 2023) – 36 articles

The smart grid optimises energy transmission efficiency and provides practical solutions for energy saving and life convenience. Along with a decentralised, transparent and fair trading model, the smart grid attracts many users to participate. In recent years, many researchers have contributed to the development of smart grids in terms of network and information security so that the security, reliability and stability of smart grid systems can be guaranteed. However, our investigation reveals various malicious behaviours during smart grid transactions and operations, such as electricity theft, erroneous data injection, and distributed denial of service (DDoS). These malicious behaviours threaten the interests of honest suppliers and consumers. While the existing literature has employed machine learning and other methods to detect and defend against malicious behaviour, these defence mechanisms do not impose any penalties on the attackers. This paper proposes a management scheme that can handle different types of malicious behaviour in the smart grid. The scheme uses a consortium blockchain combined with the best–worst multi-criteria decision method (BWM) to accurately quantify and manage malicious behaviour. Smart contracts are used to implement a penalty mechanism that applies appropriate penalties to different malicious users. Through a detailed description of the proposed algorithm, logic model and data structure, we show the principles and workflow of this scheme for dealing with malicious behaviour. We analysed the system’s security attributes and tested the system’s performance. The results indicate that the system meets the security attributes of confidentiality and integrity. The performance results are similar to the benchmark results, demonstrating the feasibility and stability of the system. Full article

An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7). Full article

The concept of Smart Cities integrates innovative technologies to improve citizens’ quality of life in towns and cities worldwide. Crisis management is a separate section directly managed by the leadership of municipalities, cities and counties in cooperation between police, fire and municipal police to ensure the safety of residents and safety in public spaces. The purpose of this study is to investigate to which extent publicly available information related to the field of crisis management is unavailable to residents in municipalities, towns and cities through online information systems. The primary aim is to provide suggestions for a general information system structure and content that would highlight and satisfy the need to address the crisis management issue, especially in providing immediate information to the population through an innovative online form. The achievement of this goal is methodologically based on qualitative research analysing and comparing the information published for residents through Smart City information systems in selected towns and municipalities. Document analysis or conceptual design was applied, and evaluation criteria for objective assessment of Smart City information systems were appropriately determined. The comparative analysis based on this set of criteria enabled the development of the proposals of information systems’ content that can be used to keep the information systems for Smart Cities in cities, municipalities and regions, actual and beneficial. From the available resources, two main modules that focused either on citizens or cities were synthesised. Moreover, SWOT analysis or the Smart Regions Rapid Response structure was derived. Acquired results outline generic structures and contents that support the development of the concept of Smart Cities and can be suitably implemented for the development of the modification of information systems containing relevant information for residents, cities and municipalities, focusing on citizen safety. Full article

As the integration of renewable energy sources (RESs) and distributed generations (DGs) increases, the need for stable and reliable operation of microgrids (MGs) becomes crucial. However, the inherent low inertia of such systems poses intricate control challenges that necessitate innovative solutions. To tackle these issues, this paper presents the development of robust-adaptive controllers tailored specifically for grid-forming (GFM) converters. The proposed adaptive-robust controllers are designed to accommodate the diverse range of scenarios encountered in low-inertia MGs. The proposed approach applies both the robust control techniques and adaptive control strategies, thereby offering an effective means to ensure stable and seamless converter performance under varying operating conditions. The efficacy of the introduced adaptive-robust controllers for GFM converters is validated within a low-inertia MG, which is characterized by substantial penetration of converter-interfaced resources. The validation also encompasses diverse MG operational scenarios and conditions. Full article

This study shows the concept of an innovative road and rail vehicle as a new form of public transport. Our literature review shows that the idea of a “smart city” contains not only new tools but also vehicles or infrastructure. The new vehicle is proposed based on the observed development of urban public transport means and other novel solutions. A slight innovation proposed here could allow the use of typical and operated tram routes for modified buses. A new type of vehicle could use both the existing tram routes and newly constructed sections with no tracks. It is assumed that new vehicles would drive with trams on the same, shared tracks. All of the conditions should reduce the costs of developing public transport networks in many cities where tram networks already exist. This paper contains a description of the idea and a potential case study location. The implementation conditions are outlined in the Discussion section. The title’s question is also considered there: “Should smart cities introduce a new form of public transport vehicles?” In addition, the potential benefits as well as threats are presented. Conclusions define the next steps for the research. So, this paper is an introduction to the wider research. It will popularize the idea of a new vehicle and could motivate the industry to construct a prototype. At this stage, no models or detailed calculations were conducted. Full article

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual. Full article

Sustainable Smart Cities have a significant potential to ensure equal access to public services, achieve sustainability and governance transparency, improve livability, and anticipate and mitigate increasingly changing threats. This study aims at prioritizing a core set of Sustainable Smart City (SSC) indicators using a combined methodology: (a) Content Analysis and (b) Analytic Hierarchy Process. The study’s contribution is that it successfully developed a more robust ranking of the above-mentioned set of indicators by combining AHP and co-occurrence analyses. The final combined ranking is intended to serve as a Decision Support Tool to streamline the decision-making process and help decision-makers prioritize dimensions to measure, achieve, or monitor actions when they cannot be undertaken simultaneously in contexts of economic recessions, financial constraints, and resource mobilization challenges. The findings draw attention to the need for considering the concept of SSCs through the prism of interconnecting the various current technology-driven “smart silos” under an inclusive umbrella that focuses on the combinations and connectedness to achieve a systemic approach to sustainability and smartness that none of those single areas can achieve in isolation. The results also revealed an interesting paradox, which relegated the Technology and ICT dimension to the bottom of the ranking, contrary to the widespread consensus and opinion, opening an opportunity for discussion among peers. Full article

Global urbanization and increasing water demand make efficient water resource management crucial. This study employs Multi-Criteria Decision Making (MCDM) to evaluate smart city water management strategies. We use representative criteria, employ objective judgment, assign weights through the Analytic Hierarchy Process (AHP), and score strategies based on meeting these criteria. We find that the “Effectiveness and Risk Management” criterion carries the highest weight (15.28%), underscoring its pivotal role in strategy evaluation and robustness. Medium-weight criteria include “Resource Efficiency, Equity, and Social Considerations” (10.44%), “Integration with Existing Systems, Technological Feasibility, and Ease of Implementation” (10.10%), and “Environmental Impact” (9.84%) for ecological mitigation. “Community Engagement and Public Acceptance” (9.79%) recognizes involvement, while “Scalability and Adaptability” (9.35%) addresses changing conditions. “Return on Investment” (9.07%) and “Regulatory and Policy Alignment” (8.8%) balance financial and governance concerns. Two low-weight criteria, “Data Reliability” (8.78%) and “Long-Term Sustainability” (8.55%), stress data accuracy and sustainability. Highly weighted strategies like “Smart Metering and Monitoring, Demand Management, Behavior Change” and “Smart Irrigation Systems” are particularly effective in improving water management in smart cities. However, medium-weighted (e.g., “Educational Campaigns and Public Awareness”, “Policy and Regulation”, “Rainwater Harvesting”, “Offshore Floating Photovoltaic Systems”, “Collaboration and Partnerships”, “Graywater Recycling and Reuse”, and “Distributed Water Infrastructure”) and low-weighted (e.g., “Water Desalination”) strategies also contribute and can be combined with higher-ranked ones to create customized water management approaches for each smart city’s unique context. This research is significant because it addresses urban water resource management complexity, offers a multi-criteria approach to enhance traditional single-focused methods, evaluates water strategies in smart cities comprehensively, and provides a criteria-weight-based resource allocation framework for sustainable decisions, boosting smart city resilience. Note that results may vary based on specific smart city needs and constraints. Future studies could explore factors like climate change on water management in smart cities and consider alternative MCDM methods like TOPSIS or ELECTRE for strategy evaluation. Full article

This article investigates the feasibility of using regenerative energy from braking trains to charge electric buses in the context of New York City’s (NYC) subway and electric bus networks. A case study centered around NYC’s system has been performed to evaluate the benefits and challenges pertaining to the use of the preexisting subway network as a power supply for its new all-electric buses. The analysis shows that charging electric buses via the subway system during subway off-peak periods does not hinder regular train operation. In addition, having the charging electric buses connected to the third rail allows for more regenerative braking energy (RBE) to be recuperated, decreasing the energy wasted throughout the system. It was also found that including a wayside energy storage system (WESS) reduces the overall substation peak power consumption. Full article

Ensuring safe and clean drinking water for communities is crucial, and necessitates effective tools to monitor and predict water quality due to challenges from population growth, industrial activities, and environmental pollution. This paper evaluates the performance of multiple linear regression (MLR) and nineteen machine learning (ML) models, including algorithms based on regression, decision tree, and boosting. Models include linear regression (LR), least angle regression (LAR), Bayesian ridge chain (BR), ridge regression (Ridge), k-nearest neighbor regression (K-NN), extra tree regression (ET), and extreme gradient boosting (XGBoost). The research’s objective is to estimate the surface water quality of Al-Seine Lake in Lattakia governorate using the MLR and ML models. We used water quality data from the drinking water lake of Lattakia City, Syria, during years 2021–2022 to determine the water quality index (WQI). The predictive performance of both the MLR and ML models was evaluated using statistical methods such as the coefficient of determination (R²) and the root mean square error (RMSE) to estimate their efficiency. The results indicated that the MLR model and three of the ML models, namely linear regression (LR), least angle regression (LAR), and Bayesian ridge chain (BR), performed well in predicting the WQI. The MLR model had an R² of 0.999 and an RMSE of 0.149, while the three ML models had an R² of 1.0 and an RMSE of approximately 0.0. These results support using both MLR and ML models for predicting the WQI with very high accuracy, which will contribute to improving water quality management. Full article

Research into novel methods for reducing greenhouse gas emissions is being carried out with the use of energy-harvesting systems. On road pavements, energy-harvesting technology has been successful in finding solutions and applications. This study discusses a solution for airport pavements that aims to produce electric energy from aircraft traffic. The new system is simulated in Simulink/MATLAB with all the components for producing technical data being provided by the manufacturers. The system is internally subdivided by simulating the aircraft in 3DOF and the energy harvesting in 1DOF. The energy-harvesting simulations achieved an energy density of up to 6.80 Wh/(m.vehicle) and a 24% conversion rate. This paper contributes to the exploration of solutions to enable energy-harvesting systems to be placed in airport pavements. These solutions are traffic dependent and require an innovative system to control the operation due to the specifications of airport pavements. Full article

In the quest to meet the escalating demands of citizens, future smart cities emerge as crucial entities. Their role becomes even more vital given the current challenges posed by rapid urbanization and the need for sustainable and inclusive living spaces. At the heart of these future smart cities are advancements in information and communication technologies, with Industry 5.0 playing an increasingly significant role. This paper endeavors to conduct an exhaustive survey to analyze future technologies, including the potential of Industry 5.0 and their implications for smart cities. The crux of the paper is an exploration of technological advancements across various domains that are set to shape the future of urban environments. The discussion spans diverse areas including but not limited to cyber–physical systems, fog computing, unmanned aerial vehicles, renewable energy, machine learning, deep learning, cybersecurity, and digital forensics. Additionally, the paper sheds light on the specific role of Industry 5.0 in the smart city context, illuminating its impact on enabling advanced cybersecurity measures, fostering human–machine collaboration, driving intelligent automation in urban services, and refining data management and decision making. The paper also offers an in-depth review of the existing frameworks that are shaping smart city applications, evaluating how Industry 5.0 technologies could augment these frameworks. In particular, the paper delves into the various technological challenges that smart cities face, bringing potential Industry 5.0-enabled solutions to the fore. Full article

The Smart City concept is perceived as a method of dynamic development of cities and an opportunity to improve the quality of life of the urban community. Nevertheless, it is not without its disadvantages, among which the possibility of exclusion (economic, social or digital) is most often mentioned. However, the literature on the subject lacks empirical research verifying this allegation. For this reason, the purpose of this article is to conduct a comparative analysis of economic and social conditions in 17 Polish cities, 3 of which are recognized as Smart Cities in international rankings. By analyzing the economic and demographic conditions in the long term, an attempt is made to answer the question of whether Smart Cities offer better living conditions, and if so, how big is the imbalance and the risk of excluding other cities? In the course of the research, the following are taken into account: tax revenue per capita, unemployment rate, population density and level, as well as the share of working and post-working age population. These parameters are analyzed using descriptive statistics and systematized using multi-criteria analysis. The collective comparison of all the surveyed provincial cities shows that the best economic and demographic conditions apply to cities recognized as smart. The average annual rate of changes in tax revenues in the surveyed cities ranges from 5% to almost 8% and is the highest in Warsaw, Kraków and Wrocław. These cities are also characterized by the lowest unemployment rate, ranging from 3% to 4% (in other cities, from 4% to almost 7%). The mentioned cities and Gdańsk are the only ones with a positive rate of population change (from 0.62% to 1.08%). Other studied cities are systematically depopulating (annual rate of change from −0.37% to −7.09%). In Warsaw, Wrocław and Kraków, the share of the working-age population is also decreasing the slowest (the annual rate of change below −1.0%). The cities recognized as smart (Warsaw, Kraków and Wrocław) are matched by Gdańsk and Poznań, which can be considered strong contenders for being smart. Unfortunately, the remaining cities are far from the leaders of the ranking, which may expose them to economic and social exclusion, all the more so that the parameters examined in them are characterized by negative tendencies. It can, therefore, be concluded that striving to be smart can be a cause of increasing the economic and demographic distance. Therefore, it may increase unbalance and generate exclusion in the analyzed areas. Full article

This paper explores the challenges in providing digital services of general interest in rural areas and proposes co-created ride-sharing software solutions to address the specific needs of these regions. This applied research is part of the Smarte.Land.Regionen project, which aims to improve digital public services at the district level. Focusing on rural mobility, the paper introduces ride-sharing benches enhanced with software as a possible low-threshold solution. Via workshops, surveys, and market research, the study identifies barriers to the adoption of ride-sharing benches and investigates factors contributing to their success. The software will be developed in an agile process together with partner counties and applied in a real-world case study. The proposed software solution emphasizes user-centered development, the geographical location of benches, and the prioritization of ride requests over ride offers. The findings highlight safety concerns, a lack of reliability, and the importance of obtaining people who are theoretically interested in solutions to actively participate in them. The paper emphasizes the importance of collaborative development with county stakeholders while also acknowledging the inherent limitations as the overall process becomes more complex and organizational obstacles arise. In addition, the findings suggest that the current state of rural mobility cannot be fundamentally changed by the implementation of ride-sharing software alone. Future research should focus on sustaining and scaling digital solutions, measuring their impact on rural mobility, and ensuring their transferability to other regions. The goal is to contribute to inclusive and sustainable rural development by improving access to digital public services and promoting the adoption of tailored mobility solutions. Full article

Networking protocols have undergone significant developments and adaptations to cater for unique communication needs within the IoT paradigm. However, meeting these requirements in the context of vehicle-to-infrastructure (V2I) communications becomes a multidimensional problem due to factors like high mobility, intermittent connectivity, rapidly changing topologies, and an increased number of nodes. Thus, examining these protocols based on their characteristics and comparative analyses from the literature has shown that there is still room for improvement, particularly in ensuring efficiency in V2I interactions. This study aims to investigate the most viable network protocols for V2I communications, focusing on ensuring data quality (DQ) across the first three layers of the IoT protocol stack. This presents an improved understanding of the performance of network protocols in V2I communication. The findings of this paper showed that although each protocol offers unique strengths when evaluated against the identified dimensions of DQ, a cross-layer protocol fusion may be necessary to meet specific DQ dimensions. With the complexities and specific demands of V2I communications, it’s clear that no single protocol from our tri-layered perspective can solely fulfil all IP-based communication requirements given that the V2I communication landscape is teeming with heterogeneity, where a mixture of protocols is required to address unique communication demands. Full article

Recent global trends related to the increasing use of e-commerce are becoming a challenge for courier transport, especially in the last-mile process of delivering products to the final retail recipient. One delivery method is the personal collection of the parcel in an automated post box, available 24/7 for the customer. Our research method was based on a preliminary selection of the most important features of parcel lockers’ service quality, which were extracted based on the analysis of the scientific literature and previous research. This analysis was carried out by conducting a survey of Polish parcel locker users that provided data coded according to the dimensions of the Kano model. Based on the total satisfaction index, the results allowed us to conclude that a dedicated application (−0.96), proper placement of the parcel in the box (−0.82), adjusting the size of the parcel to the size of the box (−0.79), the location of parcel stations (−0.74), and ensuring improvements for the disabled (−0.62) are the most important features in the process of the automatic delivery of parcels to recipients in urban areas. This paper enriches the literature on the customer service quality of self-service technologies for last-mile delivery with the use of automated parcel lockers. Full article

Smart parking system plays a critical role in the overall development of the cities. The capability to precisely detect an open parking space nearby is necessary for autonomous vehicle parking for smart cities. Finding parking spaces is a big issue in big cities. Many of the existing parking guidance systems use fixed IoT sensors or cameras that are unable to offer information from the perspective of the driver. Accurately locating parking spaces can be difficult since they come in a range of sizes and colors that are blocked by objects that seem different depending on the environmental lighting. There are numerous auto industry players engaged in the advanced testing of driverless cars. A vacant parking space must be found, and the car must be directed to park there in order for the operation to succeed. The machine learning-based algorithms created to locate parking spaces and techniques and methods utilizing dashcams and fish-eye cameras are reviewed in this study. In response to the increase in dashcams, neural network-based techniques are created for identifying open parking spaces in dashcam videos. The paper proposed the review of the existing parking slot types and their detection techniques. The review will highlight the importance and scope of a smart parking system for smart cities. Full article

Thailand’s policies are in accord with the global drive to electrify transportation vehicle fleets due to climate concerns. This dedication is evident through its adoption of the 30@30 initiative and the planned ban on new internal combustion (IC) engine vehicles by 2035, showcasing a strong commitment. The objective of this study was to utilize the Low Emission Analysis Platform (LEAP) software to model the transition possibilities for electric vehicle (EV). Emphasis was placed on the future of the light-duty vehicle (LDV) sector, encompassing the energy sources, electric power demands, and greenhouse gas (GHG) emissions. Two scenarios were evaluated: one involving rapid economic growth and the other characterized by a more-gradual expansion. The former projection foresees 382 vehicles per thousand people by 2040, while the latter estimate envisions 338 vehicles. In the scenario of high growth, the vehicle stock could surge by 70% (27-million), whereas in the case of low growth, it might experience a 47% rise (23.3-million) compared to the base year (15.8 million). The increased adoption of EVs will lead to a decrease in energy demand owing to improved fuel efficiency. Nonetheless, even in the most-extreme EV scenarios, the proportion of electricity in the energy mix will remain below one-third. While GHG emissions will decrease, there is potential for even greater emission control through the enforcement of stricter emission standards. Significant EV adoption could potentially stress power grids, and the demand for charging might give rise to related challenges. The deployment of public fast charging infrastructure could provide a solution by evenly distributing the load across the day. In the most-rapid EV penetration scenario, a public charging program could cap the demand at 9300 MW, contrasting with the 21,000 MW demand for home charging. Therefore, a recommended approach involves devising an optimal strategy that considers EV adoption, a tariff structure with incentives, and the preparedness of the infrastructure. Full article

Solar photovoltaic (PV) power, a highly promising renewable energy source, encounters challenges when integrated into smart grids. These challenges encompass voltage fluctuations, issues with voltage balance, and concerns related to power quality. This study aims to comprehensively analyze the implications of solar PV penetration in Malaysian power distribution networks predominantly found in urban and rural areas. To achieve this, we employed the OpenDSS 2022 and MATLAB 2022b software tools to conduct static power flow analyses, enabling us to assess the effects of solar PV integration over a wide area under two worst-case scenarios: peak-load and no-load periods. Our investigation considered voltage violations, power losses, and fault analysis relative to the power demand of each scenario, facilitating a comprehensive evaluation of the impacts. The findings of our study revealed crucial insights. We determined that the maximum allowable power for both urban and rural networks during no-load and peak-load situations is approximately 0.5 MW and 0.125 MW, respectively. Moreover, as the percentage of PV penetration increases, notable reductions in power losses are observed, indicating the potential benefits of higher smart grid PV integration. Full article

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems. Full article

Increasing renewable energy share in total energy production is a direction that leads toward the European Union’s aims of carbon neutrality by 2050, as well as increasing energy self-sufficiency and independence. Some of the main challenges to increasing renewable energy share while providing an efficient and secure energy supply are related to the optimization and profitability of de-centralized energy production systems. Integration of energy storage systems in addition to decentralized renewable energy production, for example, by solar panels, leads to more effective electricity supply and smart energy solutions. The modeling of such a complex dynamic system can be performed using the system dynamics method. The main aim of this research is to build and validate the basic structure of the system dynamics model for PV and battery diffusion in the household sector. A system dynamics model predicting the implementation of battery storage in private households was created for the case study of Latvia. Modeling results reveal that under the right conditions for electricity price and investment costs and with the right policy interventions, battery storage technologies combined with PV panels have a high potential for utilization in the household sector. Model results show that in a baseline scenario with no additional policies, up to 21,422 households or 10.8% of Latvian households could have combined PV and battery systems installed in 2050. Moderate subsidy policy can help to increase this number up to 25,118. Full article

Smart cities are urban areas that utilize digital solutions to enhance the efficiency of conventional networks and services for sustainable growth, optimized resource management, and the well-being of its residents. Today, with the increase in urban populations worldwide, their importance is greater than ever before and, as a result, they are being rapidly developed to meet the varying needs of their inhabitants. The Internet of Things (IoT) lies at the heart of such efforts, as it allows for large amounts of data to be collected and subsequently used in intelligent ways that contribute to smart city goals. Time-series forecasting using deep learning has been a major research focus due to its significance in many real-world applications in key sectors, such as medicine, climate, retail, finance, and more. This review focuses on describing the most prominent deep learning time-series forecasting methods and their application to six smart city domains, and more specifically, on problems of a multivariate nature, where more than one IoT time series is involved. Full article

The conventional approach to urban planning has predominantly focused on horizontal dimensions, disregarding the potential risks originating from outer space. This paper aims to initiate a discourse on the vertical dimension of cities, which is influenced by outer space, as an essential element of strategic urban planning. Through an examination of a highly disruptive incident in outer space involving a collision between the Iridium 33 and Cosmos 2251 satellites, this article elucidates the intricate interdependencies between urban areas and outer space infrastructure and services. Leveraging the principles of critical infrastructure protection, which bridge the urban and outer space domains, and employing simulation methods and software, this study articulates the intricate governance complexities of urban security and presents viable solutions for its enhancement. Consequently, the study contributes to the ongoing deliberations regarding the spatial integration of security practices by providing scholarly discourse on urban governance with potential strategies for cultivating sustainable smart cities. In essence, the intrinsic resilience of urban areas heavily relies on the interconnections between cities and outer space, necessitating urban strategists to acknowledge and comprehend these intricate interdependencies. To ensure sustainable urban development, it is imperative to fortify smart cities’ resilience against space debris through the implementation of more stringent regulations. Full article

This paper strives to enhance the inclusivity of urban public spaces, which play a crucial role in providing essential services for all citizens, including community building, physical and mental well-being, social interaction, civic engagement, citizen participation, and economic vitality. Despite the importance of these spaces, as recognized by the UN’s 2030 sustainability goals, the 2023 UN sustainable development report and scholars have drawn attention to their low availability, particularly for low-income individuals, women, children, and people with disabilities. To improve the inclusivity of public spaces, this paper offers the following contributions. (i) The establishment of a comprehensive framework for assessing public space inclusivity. This framework incorporates eight indicators: spatial distribution, typology, facilities and services, green and humid areas, governance and management, safety, user categories, and user satisfaction. (ii) The utilization of the framework to assess the inclusivity of public spaces in Nablus, a major Palestinian city. This assessment confirms the observations made by the UN and scholars regarding the low inclusivity of public spaces; in particular, a lack of public space, poor spatial distribution, and user dissatisfaction with safety conditions and services. (iii) The introduction of the concept of smart public space, which involves citizens in the governance of this space and leverages smart technology for monitoring, providing real-time information and services to citizens, improving facility efficiency, and creating an eco-friendly environment that preserves resources and biodiversity. By addressing these aspects, this paper enhances inclusivity. It promotes the development of an urban public space that caters to the diverse needs of the community, fostering a sense of belonging and well-being for all. Full article

A specialized version of collaborative driving is convoy driving. It is referred to as the practice of driving more than one vehicle consecutively in the same lane with a small inter-vehicle distance, maintaining the same speed. Extensive research has been conducted on convoys of heavy-duty trucks on the highway; however, limited research has studied convoy driving in an urban environment. The complex dynamics of an urban environment require short-lived collaboration with varying numbers of vehicles rather than collaborating over hours. The motivation of this research is to investigate how convoy driving can be realized to address the challenges of an urban environment and achieve the benefits of autonomous driving such as reduced fuel consumption, travel time, improved safety, and ride comfort. In this work, the best-fitted coalitional game framework is utilized to formulate the convoy driving problem as a coalition formation game in an urban environment. A hypothesis is formulated that traveling in a coalition is more beneficial for a vehicle than traveling alone. In connection with this, a coalitional game and an all-comprehensive utility function are designed, modeled, and implemented to facilitate the formation of autonomous vehicle coalitions for convoy driving. Multiple solution concepts, such as the Shapley allocation, the Nucleolus, and the Core, are implemented to solve and analyze the proposed convoy driving game. Furthermore, several coalition formation strategies such as traveling mode selection, selecting optimal coalitions, and making decisions about coalition merging are developed to analyze the behavior of the vehicles. In addition to this, extensive numerical experiments with different settings are conducted to evaluate and validate the performance of the proposed study. The experimental results proved the hypothesis that traveling in a convoy is significantly more beneficial than traveling alone. We conclude that traveling in a convoy is beneficial for coalition sizes of two to four vehicles with an inter-vehicle spacing of less than 4 m considering the limitations of an urban environment. Traveling in a coalition allows vehicles to save on fuel, minimize travel time and enhance safety and comfort. Furthermore, the findings of this research state that achieving the enormous benefits of traveling in a coalition requires finding the right balance between inter-vehicle distance and coalition size. In the future, we plan to extend this work by studying the evolving dynamics of the coalitions and the environment. Full article

Perovskite solar cells (PSCs) are emerging photovoltaics (PVs) with promising optoelectronic characteristics. PSCs can be semitransparent (ST), which is beneficial in many innovative applications, including building-integrated photovoltaics (BIPVs). While PSCs exhibit excellent performance potential, enhancements in their stability and scalable manufacturing are required before they can be widely deployed. This work evaluates the real-world effectiveness of using PSCs in BIPVs to accelerate the development progress toward practical implementation. Given the present constraints on PSC module size and efficiency, bus stop shelters are selected for investigation in this work, as they provide a suitably scaled application representing a realistic near-term test case for early-stage research and engineering. An energy-harvesting system for a bus stop shelter in Astana, Kazakhstan, demonstrates the potential performance evaluation platform that can be used for perovskite solar cell modules (PSCMs) in BIPVs. The system includes maximum power point tracking (MPPT) and charge controllers, which can supply PSCM energy to the electronic load. Based on our design, the bus stop shelter has non-transparent and ST PSCMs on the roof and sides, respectively. May (best-case) and December (worst-case) scenarios are considered. According to the results, the PSCMs-equipped bus stop shelter can generate sufficient daily energy for load even in a worst-case scenario. Full article

The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extended Reality (XR), Artificial Intelligence of Things (AIoT), and platform-mediated everyday life experiences in smart cities. However, the research community faces a pressing challenge in addressing the limitations posed by the resource constraints associated with XR-enabled IoT applications within the Internet of City Things (IoCT). Additionally, there is a limited understanding of the synergies between XR and AIoT technologies in the Metaverse and their implications for IoT applications within this framework. Therefore, this study provides a detailed overview of the literature on the potential applications, opportunities, and challenges pertaining to the deployment of XR technologies in IoT applications within the broader framework of IoCT. The primary focus is on navigating the challenges pertaining to the IoT applications powered by VR and AR as key components of MR in the Metaverse. This study also explores the emerging computing paradigm of AIoT and its synergistic interplay with XR technologies in the Metaverse and in relation to future IoT applications in the realm of IoCT. This study’s contributions encompass a comprehensive literature overview of XR technologies in IoT and IoCT, providing a valuable resource for researchers and practitioners. It identifies challenges and resource constraints, identifying areas that require further investigation. It fosters interdisciplinary insights into XR, IoT, AIoT, smart cities, and IoCT, bridging the gap between them. Lastly, it offers innovation pathways for effective XR deployment in future IoT/AIoT applications within IoCT. These contributions collectively advance our understanding of synergistic opportunities and complementary strengths of cutting-edge technologies for advancing the emerging paradigms of urban development. Full article

Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications. We studied the Jin-an Shopping Mall in Harbin and established the middle point height (h), middle point horizontal location (d), roof angle (α), and exposure to floor ratio (k) as the morphological parameters of the atrium. Using computational fluid dynamics (CFD), the mean radiant temperature (MRT), and the universal thermal climate index calculations (UTCI), this program was set to switch off air conditioning when the resilient ventilation met the thermal comfort requirement to achieve energy savings. The energy-saving efficiency (U) was calculated based on the energy consumption of the original model, and U could reach 7.34–9.64% according to the simulation and prediction. This study provides methods and a theoretical basis for renovating other commercial complexes to improve comfort and control energy consumption. Full article

The transport of freight involves numerous intermediate steps, such as freight consolidation, truck allocation, and routing, all of which exhibit high day-to-day variability. On the delivery side, drivers usually cover specific geographic regions, also known as clusters, to optimise operational efficiency. A crucial aspect of this process is the effective allocation of resources to match business requirements. The discrete-event simulation (DES) technique excels in replicating intricate real-world operations and can integrate a multitude of stochastic variables, thereby enhancing its utility for decision making. The objective of this study is to formulate a routing architecture that integrates with a DES model to capture the variability in freight operations. This integration is intended to provide robust support for informed decision-making processes. A two-tier hub-and-spoke (H&S) architecture was proposed to simulate stochastic routing for the truck fleet, which provided insights into travel distance and time for cluster-based delivery. Real industry data were employed in geographic information systems (GISs) to apply the density-based spatial clustering of applications with noise (DBSCAN) clustering method to identify customer clusters and establish a truck plan based on freight demand and truck capacity. This clustering analysis and simulation approach can serve as a planning tool for freight logistics companies and distributors to optimise their resource utilisation and operational efficiency, and the findings may be applied to develop plans for new regions with customer locations and freight demands. The original contribution of this study is the integration of variable last-mile routing and an operations model for freight decision making. Full article

Smart technologies have become increasingly prevalent in various industries due to their potential for energy cost reduction, productivity gains, and sustainability. Smart campuses, which are educational institutions that implement smart technologies, have emerged as a specific application of these technologies. However, implementing available smart technologies is often not feasible due to various limitations, such as funding and cultural restrictions. In response, this study develops a mathematical decision-making tool based on the evidential reasoning (ER) approach and implemented in Python. The tool aims to assist universities in prioritizing smart campus solutions tailored to their specific needs. The research combines a comprehensive literature review with insights from stakeholder surveys to identify six principal objectives and four foundational technologies underpinning smart campus solutions. Additionally, six critical success factors and nine functional clusters of smart campus solutions are pinpointed, and evaluated through the ER approach. The developed decision-support tool underwent validation through various statistical tests and was found to be highly reliable, making it a generalized tool for worldwide use with different alternatives and attributes. The proposed tool provides universities with rankings and utilities to determine necessary smart applications based on inputs such as implementation cost, operation cost, maintenance cost, implementation duration, resource availability, and stakeholders’ perceived benefit. Full article