Smart City Innovation Research Articles

Investigating the Effects of the Quadruple Helix on Civic Society Engagement in Smart City Innovation

This paper is focused on the Quadruple Helix (QH) impacts on civic engagement in urban innovations. The main research question addressed is whether there is evidence available in the public domain that use of quadruple helix has increased the involvement of members of civil society in decision-making in smart cities initiatives (SCIs). Due to its rising popularity as a practical innovation model, QH – where government, industry, academia and civil society collaborate to collectively address public problems - has received growing attention in smart cities research and policy making. But despite this, the actual effects of involving the fourth helix – members of civil society – remains an under-researched area. It has yet to be demonstrated what, in practice, SCIs operating under the banner of QH have accomplished, especially for improving the engagement of civil society (CS) members in decision-making about smart city (SC) innovations. A broadly based systematic review of the literature on QH in SCIs has been conducted to discover whether those featuring QH report that successful engagement of members of civil society has affected innovation outcomes. Results suggests that despite its heavy promotion, neither the practical significance of involving the fourth helix in decision-making in SCIs, nor the ‘benefits’ delivered from doing so, are currently unpacked in the literature. This is only partly due to the dearth of published evidence about these issues. The lack of reported effects of the quadruple helix on civic engagement in smart city initiatives is compounded by the inadequate research design adopted for such initiatives, especially by the insufficient attention given to reporting on their implementation or to evaluations of what they have achieved in practice.

An intelligent parking allocation framework for digital society 5.0

In recent smart city innovations, parking lot location has garnered a lot of focus. The issue of where to put cars has been the subject of a lot of literature. However, these efforts rely heavily on algorithms built on centralized servers using historical data as their basis. In this study, we propose a smart parking allocation system by fusing k-NN, decision trees, and random forests with the boosting techniques Adaboost and Catboost. Implementing the recommended intelligent parking distribution technique in Smart Society 5.0 offers promise as a practical means of handling parking in contemporary urban settings. Users will be given parking spots in accordance with their preferences and present locations as recorded in a centralized database using the proposed system’s hybrid algorithms. The evaluation of performance considers the effectiveness of both the ML classifier and the boosting technique, and it finds that the combination of Random Forest and Adaboost achieves 98% accuracy. Users and operators alike can benefit from the suggested method’s optimised parking allocation and pricing structure, which in turn provides more convenient and efficient parking options.

A Quantitative Assessment Approach to Implement Pneumatic Waste Collection System Using a New Expert Decision Matrix Related to UN SDGs

An innovative decision matrix has been developed to guide the selection and implementation of Pneumatic Urban Solid Waste Collection Systems (PUSWCS) in smart city projects. This study comprehensively collects and analyzes data on the advantages and disadvantages of pneumatic collection systems from technical, economic, and social perspectives. A decision-making tool was created to address the complexities of evaluating the desirability of incorporating PUSWCS in municipalities or specific areas, using a holistic approach. The tool assesses the technical, economic, and social feasibility of implementing PUSWCS, aligning it with the United Nations’ Sustainable Development Goals (SDGs). Specific variables are measured to assess compliance with the SDGs, distinguishing technical aspects from economic and social aspects. The methodology includes surveys of system users and technicians, expert assessments, and the development of a decision matrix that cross-references study variables and SDGs. The matrix assigns numerical values to the Magnitude (M) and Impact (I) of each variable, enabling quantitative interpretation. This holistic approach accommodates the complexities of waste management and diverse stakeholder perspectives. The results demonstrate the matrix’s effectiveness in accurately assessing the desirability of implementing PUSWCS. This confirms the matrix’s ability to optimally integrate with innovative smart city concepts and align with long-term sustainability goals. The study concludes that the design of the decision matrix allows the collection of information from experts, users, and stakeholders about economic, social, and environmental variables and relates them to the SDGs, to obtain a numerical result that allows to decide whether in a given urban environment it is advisable to implement a PUSWCS.

GC-YOLOv9: Innovative smart city traffic monitoring solution

In urban smart city environments, traffic hazards can lead to catastrophic outcomes, including significant property losses and severe threats to public safety. Conventional traffic monitoring systems are limited in terms of accuracy and speed, presenting significant challenges for real-time traffic surveillance. To tackle these challenges, this paper introduces the GC-YOLOv9 algorithm. Specifically, we have enhanced the YOLOv9 model by incorporating Ghost Convolution, markedly improving the model’s perceptual abilities and detection accuracy. Furthermore, this study designed an integrated smart city framework that includes layers for service applications, the Internet of Things, edge processing, and data centers. By deploying the enhanced YOLOv9 model within this framework, our method achieved mAP@0.5 scores of 77.15 and 74.95 on the BDD100K and Cityscapes datasets, respectively, surpassing existing technologies. Additionally, the potential applications of this method in public area fire safety management, forest fire monitoring, and intelligent security systems further underscore its significant value in improving the safety and efficiency of smart cities.

Securing tomorrow’s urban frontiers: A holistic approach to cybersecurity in smart cities

To address the intricate interplay between digital infrastructure and urban ecosystems, this study will adopt a multidisciplinary approach, combining expertise from cybersecurity, urban planning, and information technology. The research will delve into the vulnerabilities and potential threats that arise with the integration of IoT devices, interconnected systems, and the extensive data networks inherent in Smart Cities. By understanding the technological landscape, our goal is to devise adaptive and resilient cybersecurity measures that safeguard critical infrastructures while preserving the privacy and security of citizens. The methodology involves a qualitative inquiry through an open-ended questionnaire from 50 stakeholders. The anticipated outcomes of this research include the development of practical guidelines, best practices, and policy recommendations to fortify the cybersecurity posture of existing and future Smart Cities. By addressing the intricate relationship between urbanization and technology, this project aspires to contribute to the creation of secure, resilient, and sustainable urban environments that harness the full potential of Smart City innovations while mitigating cybersecurity risks.

An Integrative Approach to Healthcare Enhancement through Internet of Things, Artificial Intelligence and Smart City Innovations

An Integrative Approach to Healthcare Enhancement through Internet of Things, Artificial Intelligence and Smart City Innovations

The Wharf Street Smart Park Story: A Guide to Navigating Multi-Stakeholder Innovation in Smart Cities

The challenges of multistakeholder innovation in smart city projects can be navigated through collaboration and a clear understanding of each organisation’s values, technology, user groups, and potential policy changes. Increasing technology use in urban development projects brings numerous challenges, such as technology redundancy, varying stakeholder expectations, and the required policy changes. This paper aims to discuss the story of the development of Wharf Street smart park in Western Australia and highlights strategies for overcoming the challenges of multistakeholder innovation in smart city projects. It utilises a democratic collaboration tool to map out each organisation’s vision, values, and responsibilities at the initial stage of the project to create an open innovation ecosystem where knowledge can be shared. High collaboration levels using this tool have helped establish common goals, adaptive practices and overcome governance and technical challenges. However, the tool needs further development, as it was found to be insufficient in addressing long-term management issues, risk mapping, and user group identification for big data.

A Review on Key Innovation Challenges for Smart City Initiatives

Smart city initiatives are being promoted across the world to address major urban challenges, and they all share a common belief in the transformative power of digital technologies. However, the pace of innovation in smart cities seems to be much slower than the rapid and profoundly disruptive transformations brought about by digital innovation in many other domains. To develop new insights about the main causes behind this relatively modest success, this study provides a Systematic Literature Review (SLR) on the connection between major smart city challenges and the essential properties of digital innovation. The review involved the qualitative analysis of 44 research papers reporting on smart city innovation practices and outcomes. The results characterize five major challenge categories for smart city innovation: Strategic vision; Organizational Capabilities and Agility; Technology Domestication; Ecosystem Development; and Transboundary Innovation. This study also explores the connections between these challenges and concrete digital innovation practices in smart city initiatives. The main conclusion is that current innovation practices in smart cities are not properly aligned with what the research literature commonly describes as core properties of digital innovation and that this might be a major cause behind the limited progress in smart city initiatives.

Gaia-X & Fiware: Implementation of a Federated Data Platform in Smart Cities

Using and connecting different technologies to improve the quality of urban life is a significant challenge for smart cities. The interconnection of transport, energy, water, public lighting, and other urban infrastructure systems is critical to providing quality services to citizens and reducing environmental impact. However, data interoperability from these systems is a significant challenge, as data is mainly stored in different formats and locations from various sources. This paper examines the challenges of integrating existing datasets in intelligent cities and proposes solutions to create secure and interoperable data-sharing environments. To this end, an approach called data federation is discussed by implementing a federated platform for integrating such data. However, it is essential to note that there may be challenges in terms of cybersecurity and interoperability. This is where context brokers play a crucial role, regulating access to data by enforcing rules and providing security standards. In this way, these challenges can be managed appropriately, ensuring data protection and the system’s proper functioning. Finally, it is essential to mention that this work is linked to a smart cities project, which aims to promote innovation in smart cities by implementing several innovative solutions. The study presented in this research contributes to understanding the challenges in smart cities. It proposes solutions for creating secure and interoperable data-sharing environments, which are essential both for the project’s success and for the development of smart cities worldwide.

Wireless innovation in smart cities: a Dubai perspective

Wireless innovation in smart cities: a Dubai perspective

Digital Economic Transformation: In-depth Analysis of Smart Economy in the Implementation of Smart Village in Ketapang Village

Weak technology and lack of human resources caused global e-government to show that Indonesia is below the world average. It is challenging for Indonesia to have an E-Government initiative program towards Good Governance, one of which is Smart City innovation. The community economy is crucial in determining community welfare in moving towards a Smart City. With a Smart Village in Ketapang Village, Banyuwangi Regency, it is hoped that it can create community welfare through a Smart Economy. This research aims to determine the success of Smart Economy in implementing Smart Village in Ketapang Village, Banyuwangi Regency. This research uses a qualitative method with a descriptive approach. Data collection techniques were carried out using observation, interviews and documentation. If the previous research only discussed Smart Cities in general, then in this research, the author examines one of the success factors of smart cities, namely, the Smart Economy. This update discusses the Smart Economy in depth. The study was carried out based on seven Smart Economy indicators using the Giffinger approach. The research results obtained show that the Smart Economy in Ketapang Village has not been maximized due to several obstacles, such as the uneven distribution of facilities provided by the regional government, the lack of ability of the Ketapang Village community to utilize technology, and the lack of government support for technology weak responsibility towards society. However, in general, its implementation is exemplary, manifested in developing technological innovations for the community and government facilities that continue to be built.

Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems

The emergence of smart cities has presented the prospect of transforming urban transportation systems into more sustainable and environmentally friendly entities. A pivotal facet of achieving this transformation lies in the efficient management of traffic flow. This paper explores the utilization of machine learning techniques for predicting traffic flow and its application in supporting sustainable transportation management strategies in smart cities based on data from the TRAFFIC CENSUS of the Hong Kong Transport Department. By analyzing anticipated traffic conditions, the government can implement proactive measures to alleviate congestion, reduce fuel consumption, minimize emissions, and ultimately improve quality of life for urban residents. This study proposes a way to develop traffic flow prediction methods with different methodologies in machine learning with a comparison with other results. This research aims to highlight the importance of leveraging machine learning technology in traffic flow prediction and its potential impact on sustainable transportation systems for the green innovation paradigm. The findings of this research have practical implications for transportation planners, policymakers, and urban designers. The predictive models demonstrated can support decision-making processes, enabling proactive measures to optimize traffic flow, reduce emissions, and improve the overall sustainability of transportation systems.

Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication

Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things, which enables gadgets to connect with one another mostly without human involvement, is made possible by AI. In line with this, The Ad Hoc Routing Function (ARF) AI computation is used for multi-rule simplification, the use of Adaptive Cloud Computing Virtual Machine Asset Allotment Technique (ACC-VMRA) is advised. To confirm its viability, the applied developments of IoT and CC in smart cities is examined and duplicated. The experiment results show that the recommended enhancement calculation is more productive than other currently used methods.

Nurturing Sustainable Urban Space: Integrating Smart City Innovations and Earthship Design Principles for Eco-Friendly Futures

Nurturing Sustainable Urban Space: Integrating Smart City Innovations and Earthship Design Principles for Eco-Friendly Futures

An Urban Acoustic Rainfall Estimation Technique Using a CNN Inversion Approach for Potential Smart City Applications

The need for robust rainfall estimation has increased with more frequent and intense floods due to human-induced land use and climate change, especially in urban areas. Besides the existing rainfall measurement systems, citizen science can offer unconventional methods to provide complementary rainfall data for enhancing spatial and temporal data coverage. This demand for accurate rainfall data is particularly crucial in the context of smart city innovations, where real-time weather information is essential for effective urban planning, flood management, and environmental sustainability. Therefore, this study provides proof-of-concept for a novel method of estimating rainfall intensity using its recorded audio in an urban area, which can be incorporated into a smart city as part of its real-time weather forecasting system. This study proposes a convolutional neural network (CNN) inversion model for acoustic rainfall intensity estimation. The developed CNN rainfall sensing model showed a significant improvement in performance over the traditional approach, which relies on the loudness feature as an input, especially for simulating rainfall intensities above 60 mm/h. Also, a CNN-based denoising framework was developed to attenuate unwanted noises in rainfall recordings, which achieved up to 98% accuracy on the validation and testing datasets. This study and its promising results are a step towards developing an acoustic rainfall sensing tool for citizen-science applications in smart cities. However, further investigation is necessary to upgrade this proof-of-concept for practical applications.

CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data

Carbon Dioxide (CO_{2}) is a significant contributor to greenhouse gas emissions and one of the main drivers behind global warming and climate change. In spite of the global economic slowdown due to the COVID-19 pandemic, the global average atmospheric CO_{2} concentration reached a new record high in 2020 with its year-on-year increase being the fifth highest annual increase in 63 years, according to the National Oceanic and Atmospheric Administration. Furthermore, the years 2020 and 2019 were respectively the second and third warmest, while the decade 2010–2019 was the warmest decade ever recorded. In an attempt to curb this climate emergency, many countries and organizations globally have adopted ambitious goals and announced plans to help dramatically reduce CO_{2} emissions. As part of these plans, various innovative smart city projects are being developed, focusing on implementing Internet of Things (IoT) technologies. By collecting sensor-based data, such technologies aim towards automating data-driven decision-making around carbon emission management and reduction. In this work, a hybrid machine learning system, aimed at forecasting CO_2 concentration levels in a smart city environment was developed using a multivariate time series dataset containing IoT sensor measurements of CO_{2}, as well as various environmental factors, taken at every second. The proposed system demonstrated superior performance to similar methods, while also maintaining a high degree of interpretability. More specifically, the approach was empirically compared against other similar approaches in several scenarios and use cases, thus also offering more insight into the predictive capabilities of such state-of-the-art systems. For this comparison, both traditional time series and deep learning approaches were employed, including the current state-of-the-art architectures, such as attention-based, transformer networks. Results demonstrated that, when measured across various settings and metrics, including three different forecasting horizons, the hybrid solution achieved the best overall results, and in some cases, the difference in performance was statistically significant. At the same time, insights from the system’s inner workings were extracted, shedding light on the reasoning behind the model’s predictions and the factors that contribute to them, thus showcasing its transparency. Lastly, throughout the experiments, deep learning approaches illustrated their ability to better handle the multivariate nature of the dataset and in general tended to outperform the traditional time series methods, especially for longer forecasting horizons.

Smart Cities in UAE: Governance, Innovation and Quality

This article is a trail to investigate and evaluate the quality and innovation in the governance of the smart cities of UAE. It will highlight 3 major issues: the current governance of the smart cities of UAE, the need and the role of the quality and innovation in smart cities, the need of a framework to achieve the vision of the smart cities through innovative governance. Quantitative technique is used to collect the primary data from the target audience. The target audience are high level business/management positions and technology professionals working under the governance system of smart city of UAE at different positions. The article concludes that current governance approaches of the smart cities of UAE are not much effective to cater to the current challenges of the smart city such as maximum use of the resources and attain sustainability. Some governance approaches of the smart cities of UAE need to be modified or upgraded to be able to cater to the current problems of the smart cities. There is need to upgrade or innovate the approaches of the governance to achieve the best use of resources and provide quality of life through sustainable development. The study recommended that governance of the smart cities should provide solution at a centralized single platform, the flow of information to maximize the use of resource and minimize the waste, establish digital leadership, digital learning platform and collaboration between the stakeholders.

Unveiling the Role of Zoos in Smart Cities: A Quantitative Analysis of the Degree of Smartness in Kyoto City Zoo

The rapid pace of urbanization and the emergence of social challenges, including an aging population and increased labor costs resulting from the COVID-19 pandemic, have underscored the urgency to explore smart city solutions. Within these technologically advanced urban environments, zoos have assumed a pivotal role that extends beyond their recreational functions. They face labor cost challenges and ecological considerations while actively contributing to wildlife conservation, environmental education, and scientific research. Zoos foster a connection with nature, promote biodiversity awareness, and offer a valuable space for citizens, thereby directly supporting the pillars of sustainability, public engagement, and technological innovation in smart cities. This study employs a quantitative analysis to assess the alignment between smart projects and the distinctive characteristics of Kyoto Zoo. Through questionnaires, we collected feedback on performance and importance, and subsequently employed the analytic hierarchy process and the fuzzy integrated evaluation method to obtain quantitative results. The findings reveal the high level of intelligence exhibited by Kyoto Zoo, and the analysis provides insightful guidance that can be applied to other urban facilities. At the same time, we compared Kyoto Zoo with Ueno Zoo to see the difference in intellectualization achievements in different contexts in terms of data and systems.

The impact of e-Musrenbang implementation on development planning process in Banda Aceh City, Indonesia

Sustainable development requires a well established development planning process. In Indonesia, the community participates in the development planning process through the Development Planning Forum (Musrenbang). The conventional Musrenbang faces many obstacles. Hence, smart city innovation by using the E-Musrenbang application was applied. Banda Aceh City is one of the cities using E-Musrenbang. However, the effectiveness E-Musrenbang in the development planning process in Banda Aceh City has never been measured. This study aims to study the conditions and processes of development planning before and after using E-Musrenbang and assess its effectiveness in the development planning forum process in Banda Aceh City. Data were collected through questionnaires and supplemented with interviews. The analysis was conducted in a descriptive qualitative supported by quantitative methods. The results showed that using the E-Musrenbang application increased the development planning process's effectiveness in Banda Aceh City. Increased effectiveness was indicated by the better effectiveness in time and result elements. Meanwhile, the quality of work elements was also improved. In addition, public satisfaction with Musrenbang has also risen because E-Musrenbang increased transparency, accountability and community participation in the Musrenbang process. The implementation of E-Musrenbang also has a positive impact on planning effectiveness because development planning becomes more effective.

Human Centricity as Leading Design Principle for Smart City Innovations: Implications for the Governance, Control, Reporting and Performance Evaluation of Well-being of Civil Society Actors

Governments like municipalities and cities may be regarded as the ultimate stakeholder society organizations. Their key challenge is to balance the welfare of many interest groups as natural stakeholders. Stakeholders need reliable information to assess the effectiveness of implemented policies of organizations to obtain specific objectives. These objectives relate to one or more capitals measuring economic, social, and environmental sustainability affecting societal well-being. This makes reporting sustainability information addressed to a large variety of stakeholders, coined as savers, i.e., investors, and users coined as civil society actor a challenging task to fulfill due to the multidimensional construct of well-being in perspective of CSDR-2022 and similar reporting frameworks. The European Commission asserted that there is significant evidence that many undertakings like businesses do not disclose material information on all major sustainability topics, including climate related information such as GHG emissions and factors that affect bio diversity. In this research, we propose a method build upon the logic of double-entry bookkeeping in a rigorous way, extending the value cycle concept buttressing any value chain to design accounting information systems fulfilling the need of complete and reliable sustainable data for decision-making and evaluation purposes.