Sustainable Development Of Smart Cities Research Articles

Revolutionizing crowd surveillance through voice-driven face recognition empowering rapid identification: towards development of sustainable smart cities

Recent global efforts to create sustainable smart cities have significantly transformed society and improved the lives of people. Nowadays, crowd surveillance (CS) has become essential in sustainable smart cities and society to protect public safety and security. In this regard, the face-based human detection system has received considerable attention because it is recognized as an emerging method in crowd surveillance applications. Thus, in this work, a new method for real-time identification of people for a crowd surveillance system (CSS) that uses facial and speech recognition technology has been introduced. In traditional CS systems, human operators are frequently used by crowd surveillance systems to watch and evaluate video feeds. Human error and operator weariness may result in lost opportunities or slow replies, which reduce the system’s efficacy. Certain procedures, including the initial identification and monitoring of people in video feeds, can be automated using a voice-activated system. To address the issues with the present CSS, a new framework Voice-Activated Face Recognition (VAFR) is proposed in this work. The proposed framework combines the speech and face recognition models for crowd surveillance. Experimental and simulation studies have been performed to analyze the performance of the proposed VAFR framework. The proposed framework uses the Viola-Jones algorithm for face identification and the Conformer architecture for speech analysis, reaching a noteworthy 99.8% accuracy rate in live video feeds. In addition, the ethical and safety aspect of the proposed VAFR system is presented.

Assessing the Adoption of Blockchain Technology in Smart Sustainable Cities: Insights from Construction Practitioners in Malaysia

ABSTRACT In response to rapid urbanization and associated challenges, city planners worldwide are increasingly turning to the development of smart sustainable cities (SSC). Blockchain technology has emerged as a disruptive force with the potential to facilitate this transformation. While existing research has explored the concept, features, and applications of blockchain within SSC, limited attention has been given to the perspectives of construction practitioners regarding its adoption. This study seeks to address this gap by assessing the acceptance level of construction practitioners toward blockchain technology in SSC. Through the distribution of questionnaires to construction practitioners, comprising 153 responses, this research reveals a generally high level of acceptability toward blockchain technology in SSC, with notable exceptions in property or asset transactions and data management. Furthermore, significant disparities in acceptance were observed across different age groups and educational backgrounds, with older generations and those with lower levels of education demonstrating lower levels of acceptance. These findings provide valuable insights for city planners and relevant authorities, enabling them to design informed strategies and roadmaps to ensure the successful integration of blockchain technology in SSC by considering the acceptance levels of construction practitioners.

Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review.

The Internet of Things (IoT) is a critical component of smart cities and a key contributor to the achievement of the United Nations Sustainable Development Goal (UNSDG) 11: Sustainable Cities and Communities. The IoT is an infrastructure that enables devices to communicate with each other over the Internet, providing critical components for smart cities, such as data collection, generation, processing, analysis, and application handling. IoT-based applications can promote sustainable urban development. Many studies demonstrate how the IoT can improve smart cities' sustainable development. This systematic literature review provides valuable insights into the utilization of the IoT in the context of smart cities, with a particular focus on its implications for sustainable urban development. Based on an analysis of 73 publications, we discuss the role of IoT in the sustainable development of smart cities, focusing on smart communities, smart transportation, disaster management, privacy and security, and emerging applications. In each domain, we have detailed the attributes of IoT sensors. In addition, we have examined various communication technologies and protocols suitable for transmitting sensor-generated data. We have also presented the methods for analyzing and integrating these data within the IoT application layer. Finally, we identify research gaps in the literature, highlighting areas that require further investigation.

Designing a smart city sustainability assessment information system based on the adapted SULPiTER methodology

Increasing number of smart cities, the rise of technology and urban population engagement in urban management, and the scarcity of open data for evaluating sustainable urban development determines the necessity of developing new sustainability assessment approaches. This study uses passive crowdsourcing together with the adapted SULPiTER (Sustainable Urban Logistics Planning to Enhance Regional freight transport) methodology to assess the sustainable development of smart cities. The proposed methodology considers economic, environmental, social, transport, communication factors and residents’ satisfaction with the urban environment. The SULPiTER relies on experts in selection of relevant factors and determining their contribution to the value of a sustainability indicator. We propose an alternative approach based on automated data gathering and processing. To implement it, we build an information service around a formal knowledge base that accumulates alternative workflows for estimation of indicators and allows for automatic comparison of alternatives and aggregation of their results. A system architecture was proposed and implemented with the Astana Opinion Mining service as its part that can be adjusted to collect opinions in various impact areas. The findings hold value for early identification of problems, and increasing planning and policies efficiency in sustainable urban development.

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

IntelliHome: The Automated Household Infrastructure

Abstract: The rapid advancement of technology in recent years has ushered in an era of unprecedented connectivity and automation in our daily lives. One of the most promising developments in this domain is the concept of IntelliHome – an integrated and intelligent household infrastructure that seamlessly combines smart devices, artificial intelligence, and data analytics to enhance convenience, efficiency, and sustainability within the home environment. This research paper delves into the multifaceted realm of IntelliHome, exploring its technological underpinnings, potential benefits, challenges, and societal implications. The paper begins by elucidating the fundamental components of an IntelliHome, encompassing a spectrum of smart devices such as thermostats, lighting systems, security systems, and appliances, all interconnected through the Internet of Things (IoT). It discusses the pivotal role of artificial intelligence in orchestrating these devices, enabling autonomous decision-making, predictive analytics, and adaptive customization to cater to the unique needs and preferences of each household. Furthermore, the research paper delves into the manifold benefits of an IntelliHome, including energy conservation, increased security, enhanced convenience, and improved quality of life. It examines how IntelliHome systems can reduce carbon footprints, optimize resource utilization, and contribute to the development of sustainable smart cities. Finally, the research paper contemplates the broader societal implications of IntelliHome technology. It explores how these systems may redefine the boundaries of work, leisure, and domesticity, and how they may influence family dynamics, social interactions, and community engagement. It also investigates the economic aspects and economic impacts.

An artificial intelligence‐based non‐intrusive load monitoring of energy consumption in an electrical energy system using a modified K‐Nearest Neighbour algorithm

AbstractEnergy profligacy and appliance degradation are the apex reasons accounting for the continuous rise in power wastage and high energy bills. The decline in energy conservation and management in residences has been largely attributed to the financial implications of using intrusive methods. This work aimed to resolve the challenges of intrusive load monitoring by introducing artificial intelligence and machine learning to optimise load monitoring. To solve this challenge, a non‐intrusive approach was proposed where modalities for load prediction and classification were achieved with a Bagging regressor and a modified multiclass K‐Nearest Neighbour algorithms. This developed supervised learning models produced a 0.9624 R2 score and 78.24% accuracy for prediction and classification, respectively, when trained and tested on a Dutch Residential Energy Dataset. This work seeks to provide a cost‐effective approach to the optimisation of energy using steady state active power features. Essentially, the adoption of this non‐intrusive technique for load monitoring would effectively aid customers on the distribution network save cost on energy bills, facilitate the detection of faulty appliances, provide recommendations for smart homes and buildings with the required information for efficient decision making and planning of energy needs. In the long term, easing the pressure on power generation to meet demand would translate to reduction in carbon emissions based on a wide‐scale implementation of this proposed system. Hence, these are important parameters in realising the development of smart sustainable cities and sustainable energy systems in this current industrial revolution.

Environmental Assessment of Green Areas in the City of Campinas, SP: A Study of Sustainability and Biodiversity

Objective: To present a diagnosis of the environmental quality of three (3) urban green areas in Campinas, SP, so that the impact caused by urban pressure arising from the growth of cities can be evaluated. Theoretical Benchmark: Research has been conducted on sustainable development goals, smart cities and urban green areas, and can understand the importance of urban green areas in the sustainable development of smart cities. Method: Application of the LAP (Landscape Assessment Protocol) methodology, where, through Landscape Assessment using 15 metrics, the LAP CI index is obtained. Results and conclusion: The areas evaluated have LAP CI indices ranging from moderate with index 61 for the Taquaral Lagoon, 81 being good for the German Forest and 95, excellent for the Santa Geneva Forest. The methodology used can be applied to different types of areas, reflecting their environmental condition through the urban pressure suffered and having an inverse relationship between the number of visitors to the parks and the index found. Implications of the research: The research demonstrated that the methodology can be applied by different evaluators bringing different views about the environmental quality of urban green areas, precisely for the subjetive character of the evaluator in scoring the metrics. This environmental classification can help decision-makers create urban green area recovery programs. Originality / value: The research showed that the evaluation of urban green areas is an important ally in the search for sustainability in cities in order to provide better quality of life to the population.

Exploring the nexus between the barriers and drivers for sustainable smart cities in developing countries: The case of Nigeria

AbstractAmidst the rapid urbanisation and increasing calls for sustainable development, this study examines the key drivers and barriers influencing sustainable smart city initiatives in Nigeria. By employing a quantitative approach, the research amalgamates insights from diverse professionals, integrating descriptive statistics, exploratory factor analysis, and Spearman rank correlation analysis to illuminate the intricate landscape of sustainable smart city development within the Nigerian context. The findings underscore the interconnected nature of various factors, underscoring the imperative of an all‐encompassing approach that synergistically incorporates infrastructure integration, environmental sustainability, efficient governance, social inclusivity, and economic innovation. Furthermore, identifying specific barriers, including challenges related to integrated urban transformation, socioeconomic equity, and governance and infrastructure, highlights the critical need for precise interventions to surmount these obstacles. The implications and recommendations derived from this study emphasise the pivotal role of collaborative endeavours among diverse stakeholders, accentuating the significance of inclusive decision‐making processes, sustainable environmental practices, and equitable economic expansion. The insights gleaned from this research serve as an invaluable resource for policymakers and urban planners, providing a robust groundwork for informed policy formulation and strategic planning to guide sustainable smart city development in Nigeria and other developing nations.

Using Multi-Criteria Decision-Making for Smart City Evaluation and Ranking

As urban areas continue to grow and evolve, the development of smart cities has become a critical topic in urban planning and sustainability. Cities have been forced to become as smart as possible to deal with huge amounts of civic functions and boost economic growth, while constantly increasing citizens’ quality of life. They frequently invest in data-driven smart technologies for managing the vast amount of data and improving performance and efficiency. However, a city earns its smartness status when it satisfies criteria from various dimensions. Therefore, governments and local authorities need to direct their limited resources to the right domains. One way to determine it is to position the city among its rivals. The paper proposes a methodology for evaluating and ranking the smartness of 48 cities worldwide using two techniques: TOPSIS and the entropy weight method. The proposed framework examines the smartness of the cities considering seven dimensions. The results reveal a comprehensive ranking that not only accounts for the cities' overall performance in smart city development but also highlights specific strengths and weak areas for improvement. The proposed approach can be utilized as a decision support tool for city planners, policymakers, and stakeholders to identify best practices for the development of sustainable smart cities, evaluate progress, and direct investments to the right areas.

Research on the Impact of Smart City Development Taking Amsterdam as an Example

The Smart City is the highest stage of urban information development and an emerging model of sustainable urban development. Currently, the predominant focus of smart city research lies in the domain of construction, while there exists a dearth of discourse and theoretical underpinning pertaining to the sustainable development of smart cities with regards to conceptual standards and practical necessities. This insufficiency in comprehensive discussion and theoretical support may hinder the progress of Smart City research and impede the long-term effectiveness of implementation strategies. In order to address these concerns, this study chooses the most typical Amsterdam Smart City and investigates viable options for sustainable urban growth. A comprehensive definition of a smart city is formulated through a thematic study, this study adopting a prototype theory as the basis. The study also presents reference strategies for establishing a sustainable development model for smart cities in China, utilizing case studies. The findings indicate that the continued development of the Smart City model can contribute to the progress of urban intelligence, information systems, and modernization in the long run. Besides, this paper provides a case study of smart cities for in-depth investigation, with development factors influencing the selection of subsequent urban development plans.

Smart environment practices in Kashiwa-No-Ha (Japan) smart city

Abstract The smart city is a modernization effort that enables cities to use their resources more effectively and to provide better services to their residents. In this study, the smart practices of Kashiwanoha City, Japan, one of the best smart city examples in the world, were examined. The study showed that smart environment applications are designed to improve the quality of life of the citizens and ensure the sustainability of the city. The smart environment applications in Kashiwa-no-ha Smart City include smart mobility, smart technology, smart governance, smart economy, smart living, and smart environment based on transport, energy, and environment. The integration of blockchain technology is a potential solution to address the challenges of urbanisation in smart sustainable city development. The development of Kashiwa-no-ha Smart City is an example of how cities are linking their technological policies and development plans to achieve development while linking the objectives and dimensions of sustainable development to provide the best possible benefit from the available technologies. The most important factor behind Kashiwa-no-ha‘s success is the cooperation of the public and private sectors and universities. For successful smart city practices, it has been proposed to establish an independent organisation similar to the Urban Design Centre Kashiwanoha (UDCK) with a separate budget and decision-making power.

Evaluation of the Smart City and Analysis of Its Spatial–Temporal Characteristics in China: A Case Study of 26 Cities in the Yangtze River Delta Urban Agglomeration

The smart city is recognized as a potent instrument for creating efficient urban environments and improving the quality of life of urban residents. However, there is an absence of research establishing a comprehensive evaluation model for the smart cities (SCs) and focusing on their spatiotemporal analysis. Thus, a comprehensive evaluation framework was developed and applied to 26 cities in the Yangtze River Delta Urban Agglomeration (YRDUA) in China from 2017 to 2020 to assess the smart city level (SCL) in China and analyze these cities’ spatial–temporal characteristics. The results indicated the following: (1) The overall SCL in the YRDUA has exhibited sustainable improvement, and the gap between cities is gradually narrowing. (2) The SCL of the YRDUA exhibits a higher SCL in the east and a lower SCL in the west of the YRDUA. The global spatial correlation of the SCL was random. Nantong, Hefei, Jiaxing, Zhoushan, Chizhou, Tongling, and Wuhu showed significant local spatial correlation. (3) The comprehensive evaluation framework is applicable for analyzing the SCs in China, and this framework can also be extended to other countries. Pertinent recommendations are put forth to enhance the SCL through the formulation of policies and fostering intercity collaboration. This research not only enriches the theoretical research on smart city evaluation but also clarifies the spatial–temporal characteristics of the SCs in China, thereby providing valuable insights that can foster sustainable smart city development.

Blockchain technology as an enabler for cross‐sectoral systems integration for developing smart sustainable cities

AbstractSmart cities driven by modern technologies are the need of the day to alleviate the urbanisation challenges and improve the overall experience of the citizens. As the role of data‐sharing to facilitate systems integration across city sectors for developing smart cities has grown ever so profoundly, there is a need for decentralisation, transparency, and openness in terms of integration of city sectors to have efficient data diffusion among them. This is extremely important as the requirements of smart sustainable cities are open data sharing to allow service providers to better serve the citizens. Blockchain technology offers these characteristics with the extremely important added advantage of maintaining data security via an immutable record. However, the notion of the use of Blockchain for smart sustainable cities is still in the early years and requires extensive efforts to research and test it. In this research, a state‐of‐the‐art review is conducted to explore the usefulness of Blockchain technology in smart sustainable city development with a specific focus on cross‐sectoral systems integration, highlighting the gaps in the existing body of knowledge. This leads to the proposal of a novel framework for the use of Blockchain for smart sustainable cities, linking together service providers and citizens.

Information extraction system for urban planning and governance using LiDAR based 3D repository

ABSTRACT In rapidly expanding cities, an accurate land-cover information system can assist in making decisions for sustainable urban planning and governance. Point cloud procured using Airborne LiDAR Scanning (ALS) can be harnessed by creating an adaptable urban semantic information layout with querying capability. We propose a novel methodology that isolates spatial objects and integrates them with attribute information to enable object-based spatio-semantic queries. Experiments were carried out to demonstrate proposed methodology based on queries of different complexities. Results indicate that proposed approach is scalable, flexible, and hence opens a wide application perspective in urban planning, and sustainable smart city development.

Artificial intelligence for sustainable development of smart cities and urban land-use management

ABSTRACT The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions’ space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.

Challenges and strategies for gender mainstreaming policy in smart city development in Indonesia

A smart city is an urban policy innovation that can improve the community's quality of life by promoting environmental, social, and economic sustainability issues. The involvement and participation of civil society in developing smart cities is a major issue in implementing inclusive smart cities. However, gender issues are often forgotten in developing sustainable smart cities. This article examines two issues. First, the importance of gender issues integration in the development of sustainable smart cities in Indonesia. Second, the importance of gender mainstreaming policy in the development of smart cities in Indonesia. This is a desk study or literature study. Data were obtained from research results published in national and international journals. Data was obtained through accredited journal search engines, namely google scholar, SagePub and Researchgate. The research results show that development of smart city in Indonesia is not yet gender responsive, causing gender inequality gender and excluding women from development. To respond to this, thus there is a need for gender mainstreaming policies in developing smart cities through an overview of the challenges of gender mainstreaming in developing smart cities. Thus, this research recommends a gender mainstreaming strategy as a policy in developing smart cities in Indonesia. Gender mainstreaming policy in smart city development need to be to create a smart city life that is just, inclusive and involves women in development.

Provably Secure Dynamic Anonymous Authentication Protocol for Wireless Sensor Networks in Internet of Things

Wireless sensor networks are a promising application of the Internet of Things in the sustainable development of smart cities, and have been afforded significant attention since first being proposed. Authentication protocols aim to protect the security and confidentiality of legitimate users when accessing and transmitting data. However, existing protocols may suffer from one or more security flaws. Recently, Butt et al. proposed an energy-efficient three-factor authentication protocol for wireless sensor networks. However, their protocol is vulnerable to several attacks, and lacks certain security properties. In this paper, the causes of these design flaws are analyzed. Furthermore, we propose a novel three-factor authentication protocol (password, smart card, and biometric information) for wireless sensor networks in Internet of Things contexts. A dynamic anonymous strategy is designed to prevent privacy disclosure and to resist sensor node capture attacks, tracking attacks, and desynchronization attacks. The Find–Guess model and random oracle model are combined to prove the security of the proposed protocol. A comparative analysis with related schemes shows that the proposed protocol has higher security and is able to maintain a low computational overhead.

The Role of Community Engagement in Urban Innovation Towards the Co-Creation of Smart Sustainable Cities

AbstractOne of the most recent topics in smart cities is community engagement which has been generally deliberated in both industrial and academic literature around the approaches and tools employed in urban environment. Accordingly, the purpose of this study is to advocate for community engagement as a key driver that supports the acquisition of knowledge and requirements needed for innovation and creativity towards achieving an equitable community for social sustainability. A semi-systematic review method is adopted to analyze 71 sources from Web of Science and Scopus databases. Secondary data from the literature is extracted and synthesized to provide narrative and descriptive analysis. Findings from this study presents a developed model that can support community engagement for urban innovation by specifying factors that influences community engagement for smart sustainable city development. The model enables citizens, policy makers, government, urban planners, academics, and enterprises in urban environment to connect, interact, engage, and co-create innovative services. More importantly findings from this research provides theoretical evidence on administrative and non-administrative stakeholder’s involvement towards co-creation of urban services towards smart sustainable cities. Furthermore, this study provides recommendation on how community engagement perspective involving different stakeholders can help to achieve resilient technological driven city by supporting sustainable development and ultimately actualizing a socially inclusive urban space.

Smart cities from low cost to expensive solutions under an optimal analysis

This scientific approach mainly aims to develop a smart city/smart community concept to objectively evaluate the progress of these organizational forms in relation to other classical/traditional forms of city organizations. The elaborated model allowed the construction of the dashboard of access actions in the smart city/smart community category on two levels of financial effort correlated with the effect on the sustainable development of smart cities. The validity of the proposed model and our approach was supported by the complex statistical analysis performed in this study. The research concluded that low-cost solutions are the most effective in supporting smart urban development. They should be followed by the other category of solutions, which implies more significant financial and managerial efforts as well as a higher rate of welfare growth for urban citizens. The main outcomes of this research include modelling solutions related to smart city development at a low-cost level and identifying the sensitivity elements that maximize the growth function. The implications of this research are to provide viable alternatives based on smart city development opportunities with medium and long-term effects on urban communities, economic sustainability, and translation into urban development rates. This study’s results are useful for all administrations ready for change that want the rapid implementation of the measures with beneficial effects on the community or which, through a strategic vision, aim to connect to the European objectives of sustainable growth and social welfare for citizens. Practically, this study is a tool for defining and implementing smart public policies at the urban level.