Smart Cities, Volume 6, Issue 4 (August 2023) – 23 articles

The concept of smart cities has gained significant attention due to the potential of smart cities to optimize city services and enhance citizens’ quality of life. Cities are investing in digital transformation to become smarter, sustainable, and resilient. Therefore, there is a need to build a comprehensive and holistic model to assess smart city initiatives. This paper aims to develop a model that can capture the maturity of smart city adoption across various functional domains. These domains are divided into focus areas that capture different dimensions of a smart city and grouped into seven groups: ICT, economy, environment, social, resources, services, and governance. Each focus area has a set of maturity levels that describe the capabilities and outcomes of the city at different stages of development. To develop the model, the focus areas were extracted from the literature based on 16 models that have been reviewed. Assessing these models helped in identifying gaps and building the foundation of the model. Using the information extracted from the literature, a focus area model was designed and developed. The model development included seven main phases, which were: scope, design, populate, test, deploy, and maintain. The current paper validates the proposed model using the Delphi method, which involves the participation of a panel of sixty field experts. The experts evaluated the model’s correctness and completeness based on their experience and provided feedback. This feedback was used to revise and finalize the model. The smart city maturity model provides a framework for benchmarking, planning, and improving smart city initiatives. Cities can use the model to measure their performance and evaluate their weaknesses and strengths. The model is also the most comprehensive in terms of the scope of the focus areas included, and the results show that the model has a high level of accuracy and consistency and can effectively assess smart city adoption. Full article

Based on the assumption that citizens can participate in smart city development, this paper aims to capture the diversity of their profiles and their positioning towards smart city dynamics. The article starts with a literature review of some models of citizens to better understand how they could be portrayed in the smart city era. Considering that there is no “general citizen” and that usual typologies remain restrictive, we construct tailor-made personas, i.e., fictitious profiles based on real data. To this end, we present the results of a large-scale survey distributed to highly educated Walloon people in the framework of a general public exhibition. The profiling focuses on three aspects: (1) perception of smart city dimensions, (2) intended behavior regarding smart city solutions, and (3) favorite participatory methods. The collected answers were first analyzed with descriptive and nonparametric statistics, then classified with a k-means cluster analysis. The main results are five personas, which highlight the coexistence of different citizen groups that think and behave in a specific way. This process of profiling citizens’ priorities, behaviors, and participatory preferences can help professional designers and local governments to consider various citizens’ perspectives in the design of future smart solutions and participatory processes. Full article

As cities tackle a variety of recent challenges, such as climate change or resilience against natural hazards, the concept of smart cities has increasingly moved into the spotlight to provide technological solutions as appropriate countermeasures. European policymakers chose the systematic funding of smart city initiatives to incentivize and accelerate innovation and sustainability transitions by disseminating knowledge, data, and information. As this undertaking is complex, there is a pressing need to involve and engage capable stakeholders to successfully implement and operate smart city projects. To ensure the diffusion and effectiveness of these initiatives, activities towards replication and standardization as knowledge management instruments have been applied in some of these research projects. However, there is a knowledge gap on how standardization can be combined with replication efforts. As one possible answer, the lighthouse project Smarter Together has actively integrated standardization in its replication activities, resulting in the development of the CEN Workshop Agreement 17381 for describing and assessing smart city solutions. The analysis of these activities resulted in the development of 11 assumptions, which show the role of standardization as a knowledge carrier for replication activities and as a facilitator for stakeholder engagement. These findings reinforce the chosen and future policy decisions. Full article

The location service is an important part of the smart city. A unified location service for outdoor and indoor/overground and underground activity will assist the construction of smart cities. However, with different coordinate systems and data formats, it is difficult to unify various positioning technologies on the same basis. Global navigation satellite system (GNSS)-based positioning is the only way to provide absolute location under the Earth-centered, Earth-fixed coordinate system (ECEF). Increasing indoor and underground human activity places significant demand on location-based services but no GNSS signals are available there. Fortunately, a type of satellite that is indoors, known as pseudolite, can transmit GNSS-like ranging signals. Users can obtain their position by receiving ranging signals and their resection without adding or switching other sensors when they go from outdoors to indoors. To complete the outreach of the GNSS indoors and underground to support the smart city, how to adapt the pseudolite design and unify coordinate frames for linking to the GNSS remain to be determined. In this regard, we provide an overview of the history of the research and application of pseudolites, the research progress from both the system side and the user side, and the plans for pseudolite-based location services in smart cities. Full article

The transport sector is undergoing disruption due to trends such as tightening environmental targets, digitalization, and servitization, contributing to low-carbon mobility and offering citizen-oriented services. As a response, various initiatives, such as electric mobility (eMobility), have emerged that promote sustainable road transport and active mobility in the last few years. However, irrespective of the potential of eMobility, there are still few studies that examine individuals’ intention and adoption of eMobility-sharing services in smart communities. Accordingly, this study aims to develop a model grounded on the Diffusion of Innovation (DoI) theory to investigate the factors that impact individuals’ adoption of eMobility-sharing service and how to improve the adoption of eMobility-sharing service. A mixed-mode methodology was employed; quantitative data from survey questionnaires were used to gather data, and Statistical Package for Social Science (SPSS) was used to analyze the data. Additionally, qualitative data via interview was collected to demonstrate in ArchiMate modeling language how eMobility-sharing services are practically implemented as a use case study within smart communities. Findings from this study offer a model that focuses on eMobility-sharing adoption from the perspective of smart communities. Additionally, the findings offer a better understanding of how such integrated, multimodal systems fit with the sustainable mobility needs of citizens. More importantly, general recommendations to policymakers and practitioners to increase the uptake of shared eMobility are provided. Full article

Micromobility responds to urban transport challenges by reducing emissions, mitigating traffic, and improving accessibility. Nevertheless, the safety of micromobility users, particularly cyclists, remains a concern in urban environments. This study aims to construct a safety map and a risk-averse routing system for micromobility users in diverse urban environments, as exemplified by a case study in Lisbon. A data-driven methodology uses object detection algorithms and image segmentation techniques to identify potential risk factors on cycling routes from Google Street View images. The ‘Bikeable’ Multilayer Perceptron neural network measures these risks, assigning safety scores to each image. The method analyzed 5321 points across 24 parishes in Lisbon, with an average safety score of 4.5, indicating a generally safe environment for cyclists. Carnide emerged as the safest area, while Alcântara exhibited a higher level of potential risks. Additionally, an equation is proposed to compute route efficiency, enabling comparisons between different routes for identical origin-destination pairs. Preliminary findings suggest that the presented routing solution exhibits higher efficiency than the commercial routing benchmark. Risk-averse routes did not result in a substantial rise in travel distance or time, with increments of 7% on average. The study also contributed to increasing the existing amount of cycle path data in Lisbon by 12%, correcting inaccuracies, and updating the network in OpenStreetMap, providing access to more precise information and, consequently, more routes. The key contributions of this study, such as the safety map and risk-averse router, underscore the potential of data-driven tools for boosting urban micromobility. The solutions proposed demonstrate modularity and adaptability, making them fit for a range of urban scenarios and highlighting their value for cities prioritizing safe, sustainable urban mobility. Full article

In an era in which technological advancements have a profound impact on our cities and societies, it is crucial to ensure that digital technology is not only driven by technological progress with economic goals but that it can also fulfill moral and social responsibilities. Hence, it is needed to advocate for ‘Responsible Innovation and Technology’ (RIT) to ensure cities and societies can harness the potential of technological progress and prosperity while safeguarding the well-being of individuals and communities. This study conducts a PRISMA review to explore and understand RIT concepts and its characteristics. In this study, we emphasize that RIT should deliver acceptable, accessible, trustworthy, and well governed technological outcomes, while ensuring these outcomes are aligned with societal desirability and human values, and should also be responsibly integrated into our cities and societies. The main contribution of this study is to identify and clarify the key characteristics of RIT, which has not been performed in such detail so far. The study, reported in this paper, also broadens the understanding of responsible research and innovation in the technosphere, particularly from a bottom-up perspective. Furthermore, the paper develops an RIT conceptual framework outlining its possible design procedures, which could be used by governments, companies, practitioners, researchers, and other stakeholders as a tool to address the grand challenges that accompany technological and scientific progress. The framework also informs science, technology, and innovation policy. Full article

The COVID-19 pandemic has significantly disrupted human socioeconomic activities, leaving an everlasting impact on urban systems. As a result, there is a growing scholarly focus on exploring how urban planning strategies and tools can help create resilient cities. In Saudi Arabia, the pilgrimage city of Makkah, which has always faced the challenge of managing crowds during the annual pilgrimage, was left deserted due to lockdowns and social distancing measures. To quickly revive socioeconomic and pilgrimage activities in the city, a set of digital tools and communication technologies were deployed to manage crowds and enforce social distancing to minimize the spread of the COVID-19 virus. This study examines the role of digitalization and smartification in reviving the city and the importance of context in building urban resilience. This study used desktop research and case study analysis to highlight the transformation to the new normal and the development of future smart technologies for the city. Smart solutions provided valuable support in reducing the impacts of the pandemic and restarting Makkah’s economy. Although most activities have been restored, some facilities and services are still operating below capacity. Digitalization and smartification of urban services could play a major role in improving service delivery and urban resilience. Full article

Information and communication technology is changing the manner in which urban policies are designed. Saudi Arabia bases its smart initiative on the use of information and communication technologies in six dimensions, including economy, people, environment, living, mobility, and governance to improve quality of life and sustainable environment. This study draws on four Saudi Arabian cities including Riyadh, Makkah, Jeddah, and Medina, and aims to analyze their progress in the transformation into smart cities. The six identified areas were assessed using 57 indicators based on national and international information and literature. The results show that the four cities are progressing successfully into smart cities, with the highest progress evident for smart economy and the lowest progress for smart mobility in all investigated cities. Study findings show that Riyadh has made the most progress in the six smart city dimensions, concluding that Riyadh has been efficiently executing the smart city initiative with an aim to be a unique model in the world. Full article

With the increasing popularity of wireless networks and the development of smart cities, the Mobile Crowdsourcing System (MCS) has emerged as a framework for automatically assigning spatiotemporal tasks to workers. The study of mobile crowdsourcing makes a valuable research contribution to community service and urban route planning. However, previous algorithms have faced challenges in effectively addressing task allocation issues with massive spatial data. In this paper, we propose a novel solution to the spatiotemporal task allocation problem using a knowledge graph. Firstly, we construct a robust spatiotemporal knowledge graph (STKG) and employ a knowledge graph embedding algorithm to learn the representations of nodes and edges. Next, we utilize these representations to build a task transition graph, which is a weighted and learning-based graph that highlights important neighbors for each task. We then apply a simplified Graph Convolutional Network (GCN) and an RNN-based model to enhance task representations and capture sequential transition patterns on the task transition graph. Furthermore, we design a similarity function to facilitate personalized task allocation. Through experimental results, we demonstrate that our solution achieves higher accuracy compared to existing approaches when tested on three real datasets. These research findings are significant as they contribute to an 18.01% improvement in spatiotemporal task allocation accuracy. Full article

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on a strategy for the renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)^®, and structural optimization, together with lifecycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and sustainability. It was further observed that optimizing the building design with the goal of reducing embodied energy and carbon in compliance with modern energy standards was crucial to improving overall energy performance. This work demonstrated that the BIM-based framework developed to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects. Full article

Industry 5.0 (I5.0) represents a shift towards a human-centered industry and emphasizes the integration of human and machine capabilities. A highly compatible concept for enabling the I5.0 implementation is intelligent spaces (ISs), i.e., physical spaces equipped with a network of sensors, which obtains information about the place it observes, and a network of actuators, which enables changes in the environment through computing services. These spaces can sense, interpret, recognize user behavior, adapt to preferences, and provide natural interactions between humans and intelligent systems, using the IoT, AI, computer vision, data analytics, etc., to create dynamic and adaptive environments in real time. The integration of ISs and I5.0 has paved the way for the development of cognitive factories, which transform industrial environments into ISs. In this context, this article explores the convergence of IS and I5.0 concepts and aims to provide insights into the technical implementation challenges of cognitive factories. It discusses the development and implementation of a laboratory replica of a cognitive cell as an example of a segment of a cognitive factory. By analyzing the key points and challenges associated with cognitive cell implementation, this article contributes to the knowledge base surrounding the advanced manufacturing paradigm of I5.0. Full article

Applications of adaptive and intelligent lighting technologies such as presence sensitive lighting, potentially offer solutions for reducing the energy consumption of road lighting while maintaining user comfort and safety. However, little is known about road users’ experiences of such lighting. To address this gap, we conducted a real-world case study of a presence sensitive roadway lighting on a collector road in a housing area in southern Finland. New, controllable LED lighting with PIR (passive infrared) presence sensors was implemented along the road, and test scenarios were designed, programmed, and tested. The lighting was adapted both to motor vehicles using the road and to the measured traffic density along it. Drivers’ experiences and attitudes toward the lighting were collected in a three-phase evaluation with questionnaires from the community of about 1000 households using the road as part of their daily mobility. The results indicate that as an experience, presence sensitive lighting in a road environment was at least as positive as traditional, uncontrolled lighting. User experiences of presence sensitive lighting did not differ from the experiences of uncontrolled lighting regarding pleasantness, uniformity, glare, and road visibility. Most of the drivers (86%) did not notice any dynamic change in the lighting. When informed about the tested lighting strategies, most of the participants (72%) would prefer either one of the intelligent lighting modes to be the permanent lighting solution. The results of this exploratory, real-world study point towards the potential feasibility of this technology from a user experience perspective, as the experienced stability of the lighting was unaltered in the tested scenarios; importantly, it also highlights the need to study adaptive roadway lighting further, especially through confirmatory studies in controlled settings. Full article

The use of artificial intelligence in solutions used in smart cities is becoming more and more popular. An example of the use of machine learning is the improvement of the management of shared mobility systems in terms of assessing the accuracy of user journeys. Due to the fact that vehicle-sharing systems are appearing in increasing numbers in city centers and outskirts, and the way vehicles are used is not controlled by operators in real mode, there is a need to fill this research gap. The article presents a built machine learning model, which is a supplement to existing research and is updated with new data from the existing system. The developed model is used to determine and assess the accuracy of trips made by users of shared mobility systems. In addition, an application was also created showing an example of using the model in practice. The aim of the article is therefore to indicate the possibility of correct identification of journeys with vehicles from shared mobility systems. Studies have shown that the prediction efficiency of the data generated by the model reached the level of 95% agreement. In addition, the research results indicate that it is possible to automate the process of evaluating journeys made in shared mobility systems. The application of the model in practice will facilitate management and, above all, it is open to further updates. The use of many machine learning models will allow solving many problems that will occur in an increasing number of smart cities. Full article

This research presents key factors influencing smart building integration into smart cities considering the city as a technological system. This paper begins with an overview of the concept of smart buildings, defining their features and discussing the technological advancements driving their development. The frameworks for smart buildings are presented, emphasizing energy efficiency, sustainability, automation, and data analytics. Then, the concept of a smart city and the role of digitalization in its development is explored. The conceptual framework of smart building into a smart city is presented, contributing to understanding the complex process of integrating smart buildings into smart cities. Further research delves into the factors influencing the integration of smart buildings into smart cities, focusing on energy, mobility, water, security systems, and waste management infrastructure domains. Each thematic area is examined, highlighting the importance of integration and the associated challenges and opportunities, based on research in the literature and the analysis of case studies. This enables the identification of 26 factors influencing integration and the synthesis of findings. The findings indicate that the successful integration of smart buildings into smart cities requires attention to multiple factors related to smart energy, smart mobility, smart water, smart security, and smart waste management infrastructures. The results obtained from this research provide valuable insights into the factors influencing smart building integration into a smart city from a technological perspective, enabling stakeholders to make informed decisions and develop strategies paving the way for sustainable, resilient, and efficient urban environments. Full article

The automatic identification of various design elements in a floor-plan image has gained increasing attention in recent research. Emergency-evacuation applications can benefit greatly from automated floor-plan solutions, as they allow for the development of horizontal solutions instead of vertical solutions targeting a specific audience. In addition to that, current evacuation plans rely on static signs without taking into account the dynamic characteristics of each emergency case. This work aims to extract information from a floor-plan image and transform it into a graph that is used for pathfinding in an emergency evacuation. First, the basic elements of the floor-plan image, i.e., walls, rooms and doors, are identified. This is achieved using Panoptic-DeepLab, which is a state-of-the-art deep neural network for the panoptic segmentation of images, and it is available from DeepLab2, an image segmentation library. The neural network was trained using CubiCasa5K, a large-scale floor-plan image dataset containing 5000 samples, annotated into over 80 floor-plan object categories. Then, using the prediction of each pixel, a graph that shows how rooms and doors are connected is created. An application that presents this information in a user-friendly manner and provides graph editing capabilities was developed. Finally, the exits are set, and the optimal path for evacuation is calculated from each node using Dijkstra’s algorithm. Full article

Traffic incidents have adverse effects on traffic operations, safety, and the economy. Efficient Automatic Incident Detection (AID) systems are crucial for timely and accurate incident detection. This paper develops a realistic AID model using the Random Forest (RF), which is a machine learning technique. The model is trained and tested on simulated data from VISSIM traffic simulation software. The model considers the variations in four critical factors: congestion levels, incident severity, incident location, and detector distance. Comparative evaluation with existing AID models, in the literature, demonstrates the superiority of the developed model, exhibiting higher Detection Rate (DR), lower Mean Time to Detect (MTTD), and lower False Alarm Rate (FAR). During training, the RF model achieved a DR of 96.97%, MTTD of 1.05 min, and FAR of 0.62%. During testing, it achieved a DR of 100%, MTTD of 1.17 min, and FAR of 0.862%. Findings indicate that detecting minor incidents during low traffic volumes is challenging. FAR decreases with the increase in Demand to Capacity ratio (D/C), while MTTD increases with D/C. Higher incident severity leads to lower MTTD values, while greater distance between an incident and upstream detector has the opposite effect. The FAR is inversely proportional to the incident’s location from the upstream detector, while being directly proportional to the distance between detectors. Larger detector spacings result in longer detection times. Full article

With the ever-growing frequency of natural catastrophe occurrences such as hurricanes, floods, earthquakes, etc., the idea of resilient microgrids (MGs) has attracted more attention than before. Providing the opportunity for a multi-carrier energy supply after a natural catastrophe can lessen power losses and improve power resiliency and reliability. Critical loads within the MG can be prioritized and restored in the shortest possible time based on the condition of the network after the damaging occurrence by considering the energy hub (EH) systems and the optimum design and allocation of these multi-carrier systems. To this end, this paper aims to address the resilience framework in MGs considering sets of water and EHs (WEHs) consisting of CHP (combined heat and power), a boiler, energy storage, and a desalination unit. This study focused on considering an effective resilient scheme to restore critical loads in a short period after a natural catastrophe when the MG experiences an unpredictable event. By applying the idea of WEHs, there would be a chance of restoring the system by using two sets of WEH systems in the appropriate islanded points to restore the system and critical loads of electricity, heat, and water. For this purpose, different scenarios were considered for assessing the resiliency of the system against a natural catastrophic event that causes serious damage to the network by analyzing the energy-not-supplied (ENS) factor. Moreover, the allocated WEHs can adequately supply the electrical, water, and thermal demand loads throughout the day after the natural catastrophe. To mitigate the unforeseen variations in the renewable sources, a battery is located in the WEH, which can attend to the optimal scheduling effectively. A scenario-based method is also introduced to improve the resiliency of MGs in an uncertain environment such as electrical, heat, and water stochastic demands. The appropriate efficiency of the offered model was considered on a modified IEEE test system. Full article

There are limited research articles that focus on smart city assessment (SCA) applications as it is a relatively new field of research and practice. However, numerous studies have been conducted and published to date, particularly in developing countries, with the broad objective of building theoretical frameworks that are centered on smart city assessments. This study aimed to systematically examine the available literature on SCA, particularly in the context of developing economies, and provide valuable insights for the various stakeholders involved in smart city projects. The specific objectives of the study were to synthesize the existing literature on smart city assessment in developing economies, analyze the frameworks employed for smart city assessment, and identify critical gaps in these frameworks while providing recommendations for future research. The methodology employed involved a scoping review procedure, and the data that were collected and analyzed were specific to developing economies. The findings revealed that SCA often incorporates other research methods, such as mixed and quantitative analyses, and embraces a multidisciplinary approach that encompasses various subject areas. While social science emerged as a prominent subject area, sustainability, renewable energy, and industrial development also play crucial roles in smart city assessments. This study highlighted that ISO 37122:2019 is the most widely adopted framework due to its structured methodology, ability to measure progress over time, and potential for benchmarking against other cities. However, it is important to consider that each framework has its own strengths and weaknesses, and cities may opt to utilize multiple frameworks or tailor them to their specific needs. Our paper concludes by emphasizing the significance of this research in providing comprehensive insights into smart city assessment in developing economies and the need for further studies to address the identified gaps and enhance future assessments. Full article

Smart cities are rapidly evolving concept-transforming urban developments in the 21st century. Smart cities use advanced technologies and data analytics to improve the quality of life for their citizens, increase the efficiency of infrastructure and services, and promote sustainable economic growth. Smart cities integrate multiple domains, including transportation, energy, health, education, and governance, to create an interconnected and intelligent urban environment. Our research study methodology was a structured literature review using Web of Science and Google Scholar and ten smart city research questions. The research questions included smart city definitions, advantages, disadvantages, implementation challenges, funding, types of applications, quantitative techniques for analysis, and prioritization metrics. In addition, our study analyzes the implementation of smart city solutions in international contexts and proposes strategies to overcome implementation challenges. The integration of technology and data-driven solutions in smart cities has the potential to revolutionize urban living by providing citizens with personalized and accessible services. However, the implementation also presents challenges, including data privacy concerns, unequal access to technology, and the need for collaboration across private, public, and government sectors. This study provides insights into the current state and future prospects of smart cities and presents an analysis of the challenges and opportunities they present. In addition, we propose a concise definition for smart cities: “Smart cities use digital technologies, communication technologies, and data analytics to create an efficient and effective service environment that improves urban quality of life and promotes sustainability”. Smart cities represent a promising avenue for urban development. As cities continue to grow and face increasingly complex challenges, the integration of advanced technologies and data-driven solutions can help to create more sustainable communities. Full article

Recent years have seen a surge in interest in DC microgrids as DC loads and DC sources like solar photovoltaic systems, fuel cells, batteries, and other options have become more mainstream. As more distributed energy resources (DERs) are integrated into an existing smart grid, DC networks have come to the forefront of the industry. DC systems completely sidestep the need for synchronization, reactive power control, and frequency control. DC systems are more dependable and productive than ever before because AC systems are prone to all of these issues. There is a lot of unrealized potential in DC power, but it also faces some significant challenges. Protecting a DC system is difficult because there is no discrete location of where the current disappears. DC microgrid stability that is dependent on inertia must also be considered during the planning stage. The problems that DC microgrids have include insufficient power quality and poor communication. The power quality, inertia, communication, and economic operations of these value streams, as well as their underlying architectures and protection schemes, are all extensively discussed in this paper. This review paper examines the pros and cons of both grid-connected and isolated DC microgrids. In addition, the paper compares the different kinds of microgrids in terms of power distribution and energy management agency, such as the prerequisites for a DC microgrid’s planning, operation, and control that must be met before state-of-the-art systems can be implemented. Full article

A smart city is a complex concept that can be analyzed from different aspects and points of view. E-governance plays a key role in facilitating the integration of all elements of a smart city. The purpose of the article is to investigate key enablers of e-governance in terms of economic, social, political, information and technological indicators. The research base includes 68 smart cities selected on the basis of different regional affiliations and different economic, social and political developments. The authors apply the methods of cluster analysis (to divide smart cities into clusters according to e-governance indicators); construction of an integral indicator using the linear mathematical model and the Fishburn formula; VAR/VEC modeling (to stud the key factors influencing the development of e-government in smart cities). It was found that the Human Development Index has the greatest impact on e-governance, while the GNI per capita indicator demonstrated the absence of influence for all clusters. The factor of information technologies was defined as the main factor of direct influence on the Smart City Governance Index for smart cities of the first cluster with the highest indicators of e-governance. Full article

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO₂ emissions or communications load. Full article