Context Of Smart Cities Research Articles

The artistic image processing for visual healing in smart city

This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents’ mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.

Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method.

An Ontological Model to Enhance Traffic Conditions in Smart City Domain

The rising trend of the increasing interconnection of city services refers to the growing integration and interconnectedness of various urban systems and services through the utilization of technology and data. One of the primary objectives of smart city initiatives is to establish more efficient and sustainable urban environments by optimizing resource utilization and infrastructure. In this paper, we introduce a traffic ontology named "TrafCsOnto," which encompasses a comprehensive range of traffic concepts. The primary aim of this ontology is to address traffic congestion issues within the smart city context. The formalization of this ontology has been carried out using the OWL language and the protégé tool. We endeavor to illustrate the practical application of the traffic ontology through the manipulation of real-world instances and inferences.

Towards sustainable smart cities: Integration of home energy management system for efficient energy utilization

Smart Homes (SHs) play a crucial role in the broader context of smart cities, contributing to the overall efficiency, sustainability, and quality of life for residents. This work presents a computationally efficient mathematical model for Home Energy Management Systems (HEMS) to activate the Demand Side Management (DSM) while minimizing the energy consumption costs of SHs. The proposed model is represented as a standard Mixed-Integer Linear Programming (MILP) optimization problem. The proposed HEMS provides the optimal scheduling of home appliances and plugging time for charging Electric Vehicles (EV) at home. The main objective of this optimization problem is to minimize the electricity bills while improving the load profile of the distribution system in the context of smart cities. In this study, load management for a typical household has been studied considering several electricity tariffs in Portugal. The simulation results confirm that in the presence of the proposed HEMS, the electricity bill will be effectively reduced while consumer behavior is changed. The cost savings that can be attained through only shifting loads is 5.7 %, while it can reach 67.65 % by installing a hybrid energy system at the studied smart home. The weekly cost reduction percentage due to charging the EV at home is around 16.24 % compared to charging the EV using public charging stations regardless of the traveling time and waiting at the public charging stations.

Citizens’ Perception of Blockchain-Based E-Voting Systems: Focusing on TAM

Digital transformation and new technologies have made people’s lives easier and led to great results in most areas of business and society. Implementing blockchain technology is one of the best tools for establishing sustainable smart cities and societies. In terms of sustainable governance sophisticated and secure voting systems are necessary to achieve high integrity and transparency and null election fraud, and, in environmental sustainability, e-voting systems eliminate the mass waste of paper and transportation gas emissions; namely, e-voting systems are eco-friendly with high democratic outcomes. Blockchain technology can revolutionize e-voting by increasing the security and transparency of the voting process. Integrating artificial intelligence (AI) and machine learning (ML) into blockchain-based e-voting systems further augments their effectiveness. AI algorithms can analyze voting patterns and detect irregularities, supporting the prevention of fraudulent activities and coercion. ML procedures can enhance voter authentication processes, improve accessibility for diverse demographics, and optimize the productivity of blockchain networks during peak voting periods. This study focuses on understanding citizen perceptions of blockchain-based e-voting in a smart city context using the Technology Acceptance Model (TAM). The study’s results indicate that perceived ease of use and perceived usefulness are important factors in determining citizens’ intentions to use blockchain-based e-voting. Furthermore, trust in the technology and perceived security were found to influence the usefulness of blockchain-based e-voting positively. This study provides important insights for policymakers and technologists seeking to promote the adoption of blockchain-based e-voting systems in smart cities. The findings of the research supported the research model with positive results. In conclusion, our research model encourages the adoption of a blockchain-based e-voting system to enhance the future voting environment.

Deep Learning-Enhanced anomaly detection for IoT security in smart cities

The swift expansion of Internet of Things (IoT) devices within smart cities necessitates robust security measures to safeguard critical infrastructure and ensure citizen safety. In response, this research presents an advanced deep learning- based anomaly detection system designed to bolster IoT security within the context of smart cities. Leveraging the IoT-23 dataset, our system demonstrates impressive results. One of the system's notable strengths is its adaptability; it generalizes well to diverse datasets and maintains its efficacy in the presence of adversarial attacks. An intuitive user interface facilitates system management and response to detected anomalies, providing a holistic approach to IoT security in smart cities. Positive user feedback affirms the system's usability and satisfaction, emphasizing its practical utility. This research contributes to the broader field of IoT security. It furnishes well-documented code and resources, laying the groundwork for further advancements in this critical domain. As smart cities continue to evolve, the findings and innovations presented in this research serve as a vital step toward ensuring the integrity, privacy, and reliability of IoT networks within urban environments. Lastly, the findings of the experiments show that this technique has an excellent detection performance, with an accuracy rate which is more than 98.7%.

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber-Physical Systems Environment.

The arising of the Cyber-Physical Systems' vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber-Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context.

The Industry 5.0 (I50) paradigm, blockchain-based applications and the smart city

PurposeAgainst the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.Design/methodology/approachA mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.FindingsThe key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.Originality/valueAt the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.

An Integrated Smart Metering System Using LORA Technology

Precise meter readings are essential for effectively regulating residential energy systems in India's smart city context. However, due to the high number of users and the absence of daily consumption monitoring, standard meter reading methods are expensive and time-consuming, which causes consumer unhappiness. The suggested approach to address this issue is to put in place an integrated wireless smart energy system that makes use of smart metering technologies. The way utilities manage energy could be completely changed by this method. The integrated system is intended to take the place of the traditional digital energy meters, which are primarily used to record meter readings properly for payment purposes. This allows for automatic meter readings without the need for frequent travels to the location where the meters are located. Low Power Wide Area Networks (LPWAN) technology, which is renowned for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration, are used in this technology to enable the transfer of accurate and secure data on energy usage in real-time. The experiments that resulted from the trials allowed the energy to successfully communicate and documented reliable readings. By providing users with real-time usage data and guarding against overloaded scenarios, the system also benefits consumers. Additionally, the device reports electricity theft to the utility company. The shortcomings of the conventional systems and design difficulties are addressed by the suggested system.

Interoperability for intelligent traffic management systems in smart cities

Intelligent traffic management systems (ITMS) are essential for safe and livable smart cities. However, achieving seamless interoperability between diverse devices and services is challenging due to the lack of universal open standards. This study examines different types of interoperability (syntactic, semantic, network, middleware, and security) and their relationships with ITMS in the smart city context. By discussing requirements, challenges, and potential standards, this research provides a comprehensive understanding of interoperability issues in ITMS. It highlights the importance of standardization and collaboration among stakeholders, including policymakers, urban planners, and technology providers, to achieve interoperability. Addressing these challenges can optimize ITMS performance and contribute to smarter, sustainable cities. The study categorically examines challenges and potential standards, offering a framework for future research and practice. By advancing our understanding of ITMS interoperability, this research facilitates improved traffic management and smarter city development, enhancing urban residents’ quality of life. It makes a significant contribution to the field by emphasizing the critical role of interoperability in effective traffic management systems and the advancement of smart cities. By addressing interoperability challenges, we can create safer, more efficient, and sustainable transportation networks, fostering the development of livable cities.

MPC-based framework incorporating pre-disaster and post-disaster actions and transportation network constraints for weather-resilient power distribution networks

Enhancing power system resilience to extreme weather requires an effective coordination of pre-disaster preventive response actions and post-disaster repair and restoration services. In this context, this article presents a two-stage framework to enhance resilience of power distribution networks to severe weather events. It considers operating and resource constraints in interdependent power and transportation networks based on official guidance from transport appraisal studies. It also incorporates sources of information available in a smart city context, such as energy measurements, traffic flows, and geographical information systems. In the first stage, a pre-disaster operational planning strategy is defined to prepare the grid for a high-risk outage scenario with the objective of minimizing the value of lost loads, formulated as a mixed-integer quadratic programming model. In the second stage, a post-disaster corrective strategy is implemented over a receding time horizon to compensate for deviations between the actions computed in the first stage and the actions required to minimize power outages, embedded into a model predictive control scheme. The effectiveness of the proposed framework is demonstrated on a real-world large-scale distribution network in the United Kingdom over a range of outage scenarios. Simulation results show that the proposed framework is capable of effectively minimizing first-stage operational costs and second-stage deviations between the projected and actual load demand supply. Numerical results obtained with the proposed framework indicate that the load energy unserved is at least twice smaller than with typical practices adopted by distribution network operators and computational times take two minutes or less. Therefore, it can be effectively used by distribution network operators to ensure an appropriate level of preparedness to power outages caused by extreme weather along with prompt restoration and repair services.

Exploring motivations for social games in virtual reality for smart cities

The Metaverse is experiencing widespread adoption by organizations aiming to elevate brand engagement, particularly in computer engineering’s burgeoning field focused on virtual world gamification. This trend spans various sectors, including network science, smart cities, and online games. This study aims to reveal the motivations driving the use of virtual media, providing valuable insights into diverse user preferences. Integrating this information into smart city development holds promise for enhancing responsiveness to user needs, making our dataset relevant for both governmental and private entities seeking to create virtual media platforms or smart cities tailored to user demands. This research contributes to the ongoing evolution of smart city design, aligning with user motivations and adapting to evolving trends in user behavior. The findings offer a foundation for informed decision-making, promoting a user-centric and responsive approach in developing virtual experiences within the smart city context. Our research paper focuses on investigating the motivation and necessity of virtual reality (VR) games for potential integration into smart cities. Targeting a specific demographic of 40 individuals aged 18 to 24 in the capital of Thailand, we utilized correlational research methods to explore motivations and needs for VR games. Through the development of a socially virtual game-free asset using Unity, our experiments shed light on the significant motivations for Generation Z in Thailand, emphasizing convenience, product appearance, and game effects. The findings highlight the pivotal roles of social engagement and the motivation for self-disclosure in building relationships. Additionally, our study suggests that social connections and shared experiences strongly motivate individuals to participate in gaming activities. Notably, female players are significantly influenced by social connections, while branding has a minimal impact on their motivation and desire.

Transportation-Enabled Services: Concept, Framework, and Research Opportunities

As a result of rapid advancements in urban infrastructure and technology, transportation is transforming a variety of innovative and emerging services. This paper introduces the concept of transportation-enabled services (TRENS), which is a service model that uses transportation systems to enable and enhance the delivery, accessibility, and effectiveness of nontransportation services. We establish a general framework in which the transportation-enabled services involve four key stakeholders: customers, suppliers, TRENS providers, and transportation carriers. Within this general framework, we present five specific service models: a one-sided market, three variants of two-sided markets, and a three-sided market, each characterized by the interactions among suppliers, TRENS providers, and transportation carriers. Taking different models of transportation-enabled services into consideration, we highlight crucial research opportunities to enhance understanding of the planning, operations, evaluation, and regulation of transportation-enabled services. These research opportunities include demand and supply management, transportation system management and operations, coordination among stakeholders, and the evaluation and regulation of transportation-enabled services. We believe that the emergence, widespread adoption, and popularity of transportation-enabled services will significantly enhance and reshape many services and thus contribute to the overall improvement of future mobility in the broader context of smart cities. History: This paper has been accepted for the Service Science Special Issue on Innovation in Transportation-Enabled Urban Services. Funding: The work of Hai Wang was supported by the Lee Kong Chian Fellowship awarded by Singapore Management University. The work of Hao Sun was supported by the National Natural Science Foundation of China [Grants 72301179].

Leveraging Fog Computing, Blockchain, and IoT for Enhanced Healthcare Monitoring Services

Abstract: With the rapid evolution of technology, smart cities have emerged as geographic areas powered by advanced information and communication technologies. These advancements have been significantly influenced by integrating smart technologies, such as the Internet of Things (IoT), blockchain, and fog computing. This paper aims to explore the impact and potential of these technologies on healthcare services within the context of smart cities. Fog computing, as a solution to the challenges faced by integrated Cloud Computing (CC) in IoT applications, offers a promising approach by bringing computation closer to the edge devices. Unlike traditional cloud-based approaches, fog computing enables local data processing and storage, resulting in improved performance, security, and reduced latency. Furthermore, fog computing acts as a middle layer between IoT sensors/devices and cloud data centers, facilitating efficient resource provisioning and service administration. In smart cities, IoT, blockchain, and fog computing are pivotal in enhancing healthcare services. While IoT is widely integrated into healthcare systems, it faces challenges such as cost efficiency, data privacy, and data interoperability. In contrast, blockchain technology offers solutions for safeguarding private data, establishing decentralized databases, and enhancing data interoperability.

HoneyTwin: Securing smart cities with machine learning-enabled SDN edge and cloud-based honeypots

With the promise of higher throughput, and better response times, 6G networks provide a significant enabler for smart cities to evolve. The rapidly-growing reliance on connected devices within the smart city context encourages malicious actors to target these devices to achieve various malicious goals. In this paper, we present a novel defense technique that creates a cloud-based virtualized honeypot/twin that is designed to receive malicious traffic through edge-based machine learning-enabled detection system. The proposed system performs early identification of malicious traffic in a software defined network-enabled edge routing point to divert that traffic away from the 6G-enabled smart city endpoints. Testing of the proposed system showed an accuracy exceeding 99.8%, with an F1 score of 0.9984.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Employee Acceptance of Digital Transformation: A Study in a Smart City Context

The notion of the smart city offers significant benefits to citizens and enterprises alike. To realize these benefits, however, the city’s organizations and working processes need to undergo a digital transformation (DT) which reshapes work practices and demands a new mindset from city employees. The readiness of these employees to accept such a DT has an effect on the extent to which the city’s residents and businesses benefit from being in a smart city context. This raises the question of which factors have the most influence on employees’ acceptance of DT—a question that has thus far received limited attention from researchers. This study introduces a model that examines various influencing factors, using data collected from a survey of 1180 employees in 10 smart cities in Saudi Arabia. The factors examined take a variety of forms, including external elements such as management provision for employees and process complexity, which can lead to resistance to change, as well as internal elements, such as perceived advantages and inertia. The findings of the study show that management support for employees and the perceived advantages of DT have a positive relationship with acceptance by employees, while process complexity and inertia have a negative relationship with employees’ acceptance. These findings contribute to the growing body of literature concerning DT acceptance in a smart city context and will provide organizations with insights on which to base strategies for encouraging employee adaptation to evolving digital landscapes.

Open innovation in a smart city context: the case of Sejong smart city initiative

Open innovation in a smart city context: the case of Sejong smart city initiative

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.

Community Engagement in Smart Cities: A Social Network Analysis and Community Engagement Test

This study, carried out in the ever-changing context of Smart Cities, reveals the complex interactions of digital social networks, digital community involvement, and the Community involvement Test (CET) framework. The examination of data obtained from certain Smart Cities indicates a thriving milieu of community engagement, characterized by a markedly elevated rate of active participation. The identification of urgent issues, particularly those related to environmental sustainability, safety, traffic management, and educational quality, is consistent with the larger urban difficulties that these creative urban centers are facing. Additionally, the CET framework's integration of SNA data and survey results produces very high ratings, indicating the engagement programs' outstanding performance in promoting resident satisfaction and active involvement. These results highlight how digital technologies can help Smart Cities foster vibrant, engaged communities. They also highlight the continued need for smart urban policies to address urgent issues and maintain residents' quality of life while adhering to the Smart City paradigm.