Smart City Ecosystem Research Articles

Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities

Digital Twin (DT) technology has become a cornerstone of modern smart city infrastructure, providing real-time insights and operational efficiencies by creating a virtual replica of physical systems such as traffic networks, energy grids, and public services. While these advancements enable optimized urban management and improved decision-making, they also present new cybersecurity challenges that can potentially jeopardize the safety and reliability of critical infrastructures. This study addresses the cybersecurity risks associated with Digital Twin deployments in smart cities, focusing on threats such as unauthorized access, data manipulation, and hijacking of the DT models, which could result in service disruptions and compromise public safety. The research employs a comprehensive risk assessment methodology based on the NIST Cybersecurity Framework, where potential risks are identified, evaluated, and prioritized according to their severity and likelihood of occurrence. To mitigate these risks, a multi-layered security framework was developed, incorporating encryption mechanisms, robust access control, and an Artificial Immune System (AIS)-based anomaly detection model. The framework was tested through a simulated case study on a smart transportation system within a smart city environment, demonstrating its effectiveness in preventing data tampering and detecting unauthorized access. The results indicate that the proposed security model reduced data manipulation incidents by 35%, decreased response times for threat detection by 25%, and improved overall system resilience by 40%. These findings underscore the critical need for proactive cybersecurity strategies in ensuring the secure and resilient deployment of Digital Twin technologies in smart cities. The study concludes by emphasizing the importance of continuous security monitoring and adaptive threat management to safeguard smart city ecosystems from evolving cyber threats

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

Hybrid quantum architecture for smart city security

Currently and in the near future, Smart Cities are vital to enhance urban living, address resource challenges, optimize infrastructure, and harness technology for sustainability, efficiency, and improved quality of life in rapidly urbanizing environments. Owing to the high usage of networks, sensors, and connected devices, Smart Cities generate a massive amount of data. Therefore, Smart City security concerns encompass data privacy, Internet-of-Things (IoT) vulnerabilities, cyber threats, and urban infrastructure risks, requiring robust solutions to safeguard digital assets, citizens, and critical services. Some solutions include robust cybersecurity measures, data encryption, Artificial Intelligence (AI)-driven threat detection, public–private partnerships, standardized security protocols, and community engagement to foster a resilient and secure smart city ecosystem. For example, Security Information and Event Management (SIEM) helps in real-time monitoring, threat detection, and incident response by aggregating and analyzing security data. To this end, no integrated systems are operating in this context. In this paper, we propose a Hybrid Quantum-Classical Architecture for bolstering Smart City security that exploits Quantum Machine Learning (QML) and SIEM to provide security based on Quantum Artificial Intelligence and patterns/rules. The validity of the hybrid quantum-classical architecture was proven by conducting experiments and a comparison of the QML algorithms with state-of-the-art AI algorithms. We also provide a proof of concept dashboard for the proposed architecture.

Pendidikan Kewirausahan Kampus Hijau UIN Raden Intan Lampung dalam Mendukung Ekosistem Kota Pintar Bandar Lampung

Entrepreneurship is about changing the world through solving social problems, creating innovative products and generating new solutions in life. This research examines how entrepreneurship education at universities or green campuses plays a role in supporting the Bandar Lampung smart city ecosystem. This research is library research with a descriptive approach. The results of this research show that Entrepreneurship Education on an environmentally friendly green campus is an effective way to produce intelligent people as an indicator of creating a smart city based on a green city and a liveable city. Therefore, leaders of green universities or campuses must continue to strive to create environmentally friendly entrepreneurship education so that green campus efforts as part of the green city ecosystem towards a livable city can continue to be implemented towards achieving smart city targets.

A copula-based secured intelligent dynamic-static energy community transportation system for smart cities

This paper addresses a secured co-dynamic model for the energy management of Electrical Vehicles (EVs) within the real community transportation system (RCTS). The proposed model aims to facilitate interoperability among mobile energy resources within the smart city, enabling the RCTS to model the co-dynamic-static transportation systems (TSs) simultaneously. The energy management model within the traffic flow system focuses on dynamic assignment, considering the power consumption associated with the density of moving vehicles. EVs play a key role in economically managing energy in both static and dynamic behaviors within charging stations while aligning with the current traffic flow.To enhance data security within the smart city ecosystem, a directed acyclic graph (DAG)-based decentralized cyber security approach is recommended. This approach ensures that data transactions involving mobile energy resources are secured against cyber-attacks through the use of public, private, and transaction blocks. Additionally, an uncertainty-based copula function is presented to create a precise management environment within the smart city. The results indicate that the proposed model for transportation energy resources tends to reduce energy costs by optimally controlling energy consumption within traffic flow, compared to normal conditions.

Reshaping Smart Cities through NGSI-LD Enrichment.

The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems.

Energy-Efficient Dynamic MAC Protocol for M2M Communication

In the Internet of Things, which is vital for smart city applications, a vast network of battery-operated Machine Type Communication Devices (MTCDs) requires Machine Type Communication. These MTCDs connect directly to a network with a base station, facilitating a wide range of smart city functionalities. The development of energy-efficient communication protocols is essential to support the deployment of energy-constrained battery-operated devices. These protocols aim to minimize energy consumption, thereby enhancing network lifetime. Additionally, they must address scalability, reduce collisions and delays, and ensure a high Quality of Service (QoS) to effectively manage the vast number of devices involved. Clustering is a viable strategy to enhance scalability and energy efficiency in network communications. However, within this framework, particularly in the proposed Power Efficient Dynamic MAC protocol for M2M Communication (PMAC), there's still a need for energy-efficient communication between the Machine Type Communication Devices (MTCDs). This requirement is crucial to optimize the overall performance and sustainability of the network. In this paper, we analyses the Energy Efficient CSMA/CA (E-CSMA/CA) protocol, specifically developed for facilitating communication between Machine Type Communication Devices (MTCDs) in short-range communications. This analysis is set within the context of smart city applications, with a particular focus on smart metering. This approach underscores the importance of efficient, reliable communication protocols in modern urban infrastructure, especially for applications like smart metering that are integral to the smart city ecosystem.

Advancements in Bio-inspired Self-Powered Wireless Sensors: Materials, Mechanisms, and Biomedical Applications.

The rapid maturation of smart city ecosystems is intimately linked to advances in the Internet of Things (IoT) and self-powered sensing technologies. Central to this evolution are battery-less sensors that are critical for applications such as continuous health monitoring through blood metabolites and vital signs, the recognition of human activity for behavioral analysis, and the operational enhancement of humanoid robots. The focus on biosensors that exploit the human body for energy-spanning wearable, attachable, and implantable variants has intensified, driven by their broad applicability in areas from underwater exploration to biomedical assays and earthquake monitoring. The heart of these sensors lies in their diverse energy harvesting mechanisms, including biofuel cells, and piezoelectric, triboelectric, and pyroelectric nanogenerators. Notwithstanding the wealth of research, the literature still lacks a holistic review that integrates the design challenges and implementation intricacies of such sensors. Our review seeks to fill this gap by thoroughly evaluating energy harvesting strategies from both material and structural perspectives and assessing their roles in powering an array of sensors for myriad uses. This exploration offers a comprehensive outlook on the state of self-powered sensing devices, tackling the nuances of their deployment and highlighting their potential to revolutionize data gathering in autonomous systems. The intent of this review is to chart the current landscape and future prospects, providing a pivotal reference point for ongoing research and innovation in self-powered wireless sensing technologies.

Creating Value to Address Urban Challenges in Nascent Ecosystems: A Study of Effectuation in Smart City Context

The multidimensional nature of urban challenges requires firms to develop smart solutions with interdependent actors, often referred to as innovation ecosystems. However, firms might find it challenging to define a viable value proposition for a smart city offering at the outset, as value propositions depend on other actors' complementary technologies, an uncertain yet emerging demand, and the multifaceted nature of urban challenges. This article deploys effectuation theory from entrepreneurship research to enable focal firms to create value by managing uncertainties and available means at multiple levels of emerging ecosystems. We performed a case study within a large, incumbent corporation operating as the focal firm within a smart city context to gain insight into the value creation efforts and the challenges inherent in this process. We iteratively synthesized the case study insights with extant research on effectuation into a multilevel value creation framework in smart city ecosystems. The framework highlights how awareness of the range of alternative futures based on (potential) ecosystem-level means can enable a focal firm to develop actionable value propositions and identify relevant stakeholders within the emerging network of the focal firm. Our findings show that a holistic overview of dynamics in ecosystems can support firms in creating value in nascent ecosystems.

The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

Exploring the Issues and Scenarios among Malaysia’s Real Estate Valuers Community Related to Smart City Concepts

The rise of smart city concept is fundamental because it is more integrated in the fast-growing urbanisation nowadays and the primary objective is to solve the social problems that are associated with the urban cities. Though the outcomes of these programs may vary based on the level of participation of the real estate valuers community and its acceptance of them. In the light of it, the aim for this study would be to show the problems and scenarios personally proven as obstacles for real estate valuers while the acceptance of smart city concept is in Malaysia. This will lead to the community being able to familiarize themselves with the smart city technologies and the process of valuation. This study has been carried out throughout the entire process of development of smart city ecosystem. The purpose of this study is to be able to distinguish the issues and scenarios among real estate valuers community related to smart city concepts. Thus, this study could help us understand on how to stimulate more involvement and participation of real estate valuers community in the urban areas that are under the category of smart city. It can be used in educating political leaders on inclusive policies, involving citizens in designing and operating technologies that people want and let the communities play a role in planning how they will design the city. Conclusively, thi study intended to lay the foundation for the sustainability and equality of smart cities, which are vital aspects of national development.

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

IMPLEMENTATION OF INTELLIGENT AUTOMATIC CONTROL OF TRAFFIC FLOWS IN URBAN AREAS OF REGULATION BASED ON THE USE OF FUZZY MODELS

The article deals with the implementation of automatic traffic control of automobile flows in urban areas of regulation based on the use of fuzzy models. The relevance of the topic of the article is due to the problem of traffic management in the Smart City ecosystem. Traffic flow control is a complex dynamic task, for the solution of which it is proposed to use artificial intelligence methods for processing fuzzy knowledge. The article proposes a model of a traffic flow control system at an intersection based on the use of fuzzy knowledge. Knowledge processing in the system is carried out by the module “Fuzzy Controller”. The input data for the fuzzy controller is information about the number of cars that have passed and information about the current duration of the traffic light phases. The fuzzy controller has a number of output variables corresponding to the number of phases of the traffic light. The fuzzy controller is implemented by means of the fuzzy sets apparatus. The system solves the following tasks: tracking the increase in traffic in the regulation zone; tracking the approach of the flow density on all streets of the regulation zone to the critical one; collecting information about the filling of the road departing from the intersection; implementing indirect unloading of the road section after the intersection, implementing management in transit sections of the city. To increase the efficiency of the model, an improved traffic management process is proposed within the framework of a single intersection, which takes into account traffic situations after the intersection. This approach has a positive impact on traffic in the regulated area due to the decentralized structure of the system consisting of such controlled intersections. The authors also implement the priorities of the directions of movement within the framework of the proposed model. Priorities are set when setting up the system at each of the traffic lights and allow you to speed up the circulation of traffic within the control zone.

Smart Parking System: Optimized Ensemble Deep Learning Model with Internet of Things for Smart Cities

In the recent era of smart city ecosystems and the Internet of Things (IoT), innovative, intelligent parking systems must make cities more sustainable. Every year, the increasing number of city vehicles requires more time to search for parking slots. In large cities, 10\% of the traffic congestion occurs because of cruising; drivers spend almost 20 minutes searching for free space to park their vehicles. The passing time of waiting for parking in the traffic leads the issues such as energy, pollution, and stress. There needs to be more than the developed solutions. Therefore, the necessary to create a parking slot availability detection system that informs the drivers in advance about the free parking slot based on location. This paper introduces an enhanced ensemble Deep Learning (DL) model designed to forecast parking slot availability through the integration of IoT, cloud technology, and sensor networks. The devised model, known as Ensemble CNN-Boosted Graph LSTM (ECNN-BGLSTM), is optimized using a Genetic Algorithm (GA) framework. The model's performance is rigorously evaluated using a dataset from Europe, and various metrics, including Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE), are employed for assessment. The experimental findings demonstrate the superior performance of the proposed model compared to existing state-of-the-art approaches.

Role of Privacy Metrics in Next Generation Smart Cities

In the evolving landscape of urbanization, the concept of smart cities has gained momentum, integrating technology to enhance urban living. However, as cities become increasingly interconnected and data-driven, the paramount importance of privacy within this context cannot be overstated. This abstract delves into the multifaceted significance of privacy in the realm of smart cities. The seamless integration of data-driven technologies, such as Internet of Things (IoT) devices and advanced surveillance systems, offers unprecedented urban management opportunities. Nonetheless, this integration poses substantial threats to personal privacy. Balancing the potential benefits of smart city solutions with the preservation of individual autonomy and data security is a critical challenge that policymakers, technology developers, and citizens must collectively address. The abstract emphasizes that safeguarding privacy in smart cities is not merely a matter of individual preference, but a fundamental human right. Analyzing various dimensions, from the ethical implications of constant surveillance to the risks of data breaches, it becomes evident that effective privacy protection contributes to fostering trust among citizens, encouraging their active participation in the smart city ecosystem. Through a comprehensive review of current practices and regulatory frameworks, this abstract underscores the necessity of a holistic approach to privacy preservation. Ultimately, this abstract underscores that while smart cities hold immense potential to revolutionize urban living, ensuring privacy protection is not only a legal and ethical obligation but also a prerequisite for their long-term success and societal acceptance.

INVESTING IN INTELLIGENT SMART CITY TECHNOLOGIES

Modern information and communication technologies are becoming the basis for a new quality of cities, the organisation of a comfortable living space in cities, improving the quality of life of the population, ensuring sustainable development and improving technology as a tool for implementing these tasks. The implementation of all these measures requires significant investments, which makes it necessary to find various ways of diversifying financial sources and mechanisms. The purpose of the article is to identify modern investment processes in the development of smart technologies of the world's smart cities in the conditions of large-scale digitalisation. Methodology. Graphic visualization methods are used to characterize trends in the penetration of the latest ICT technologies into the life of cities, the growth of the ICT market in smart cities, including 5G technologies. The methods of system analysis and generalisation allowed to determine the main directions of investment in smart city technologies and to identify the processes of formation of the newest forms of investment. Practical implications. The generalisation of the smart city practice made it possible to identify the following basic technologies necessary for the formation of a smart city ecosystem: Internet of Things (IoT), artificial intelligence (AI), Big Data, cloud computing, etc. These technologies are used to improve the quality of life, increase the efficiency of the economy and infrastructure, ensure community safety and promote the sustainable development of the smart city. These key areas require significant investment: communications infrastructure (5G network); traffic management systems; energy efficiency; security and surveillance systems; electronic service management. Global investment in smart city technology is predicted to increase many times over in the coming years. Value/originality. The need to attract significant investment in the development of the city's digital infrastructure has led to the emergence of the latest financial technologies that have widened the range of opportunities: direct investment, venture capital, corporate investment, corporate partnerships, state and local funds, crowdfunding, bonds, fintech, blockchain, etc.

Citizen-centric Design of Consumable Services for Smart Cities

The ongoing growth of city populations has brought new challenges and increased complexity to everyday city life. In response, numerous cities are making efforts to adopt new information technologies to become more efficient and transform themselves into smart cities. A large body of research has underlined that the success of these smart city initiatives depends on putting citizens at the center of the design process. In contrast to the techno-centric view applied in the past, citizen-centric approaches focus on prioritizing the needs of citizens over technology and aim at involving them directly in the development of smart city services. In this article, we describe this involvement as citizen development by introducing a model-based architecture that enables citizens to develop smart city services in a low-code fashion. Consequently, we focus on citizen developers, a group of citizens engaged with the technological development of services. Furthermore, the conceptualization of a citizen development architecture is discussed, as well as the ways citizens can interact with it. An important pillar in this approach are citizen workshops, which aim at stimulating innovative ideas and collaboration among stakeholders by using design thinking methodology. Our approach empowers citizens to become active producers within the smart city ecosystem, leading to more inclusive and citizen-centric smart cities.

GEOSPATIAL BIG DATA ANALYTICS FOR SUSTAINABLE SMART CITIES

Abstract. Growing urbanization cause environmental problems such as vast amount of carbon emissions and pollution all over the world.Smart Infrastructure and Smart Environment are two significant components of the smart city paradigm that can create opportunities for ensuring energy conservation, preventing ecological degradation, and using renewable energy sources. Since a great portion of the data contains location information, geospatial intelligence is a key technology for sustainable smart cities. We need a holistic framework for the smart governance of cities by utilizing key technological drivers such as big data, Geographic Information Systems (GIS), cloud computing, Internet of Things (IoT). Geospatial Big Data applications offer predictive data science tools such as grid computing and parallel computing for efficient and fast processing to build a sustainable smart city ecosystem. Effective management of big data in storage, visualization, analytics, and analysis stages can foster green building, green energy, and net zero targets of countries. Parallel computing systems have the ability to scale up analysis on geospatial big data platforms which is key for ocean, atmosphere, land, and climate applications. In this study, it is aimed to create the necessary technical infrastructure for smart city applications with a holistic big data management approach. Thus, a smart city model framework is developed for Smart Environment and Smart Governance components and performance comparison of Dask-GeoPandas and Apache Sedona parallel processing systems are carried out. Apache Sedona performed better on the performance test during read, write, join and clustering operations.

Artificial intelligence-assisted air quality monitoring for smart city management.

The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.

Spectrum regulation and frequency allocation in the context of a smart city – using the regulatory approach in Finland as an example

ABSTRACT Smart cities rely on data, wireless technology, and connectivity; therefore, the radio spectrum is essential for their future development. This article provides an overview of the regulatory framework and relevant actors related to spectrum use, thereby promoting foreseeability and thus investment in the smart city ecosystem. The focus is the EU-level, but due to the importance of national and local levels, Finland functions as an example. The article highlights the important role of network slicing, infrastructure and spectrum sharing, other forms of cooperation for smart city development, and various operational models and alternatives. The article concludes that smart city operations are both possible and also supported by current regulation and allocation. Despite international and EU-level frameworks, national approaches remain relatively critical. Future real-life experiences will indicate the direction of spectrum regulation, but clearly smart cities require dynamic, local and flexible (use of) networks.