Smart Cities, Volume 6, Issue 6 (December 2023) – 18 articles

As digital technology continues to evolve rapidly and get integrated into various aspects of our cities and societies, the alignment of technological advancements with societal values becomes paramount. The evolving socio-technical landscape has prompted an increased focus on responsible innovation and technology (RIT) among technology companies, driven by mounting public scrutiny, regulatory pressure, and concerns about reputation and long-term sustainability. This study contributes to the ongoing discourse on responsible practices by conducting a policy review that delves into insights from the most influential high-tech companies’—so-called tech giants’—RIT guidance. The findings disclose that (a) leading high-tech companies have started to focus on RIT; (b) the main RIT policy focus of the leading high-tech companies is artificial intelligence; (c) trustworthiness and acceptability of technology are the most common policy areas; (d) affordability related to technology outcomes and adoption is almost absent from the policy; and (e) sustainability considerations are rarely part of the RIT policy, but are included in annual corporate reporting. Additionally, this paper proposes a RIT assessment framework that integrates views from the policy community, academia, and the industry and can be used for evaluating how well high-tech companies adhere to RIT practices. The knowledge assembled in this study is instrumental in advancing RIT practices, ultimately contributing to technology-driven cities and societies that prioritise human and social well-being. Full article

The overarching goal of this paper is to explore innovative ways to adapt existing urban infrastructure to achieve a greener and more resilient city, specifically on synergies between the power grid, the wastewater treatment system, and community development in low-lying coastal areas. This study addresses the technical feasibility, benefits, and barriers of using wastewater resource recovery facilities (WRRFs) as community-scale microgrids. These microgrids will act as central resilience and community development hubs, enabling the adoption of renewable energy and the provision of ongoing services under emergency conditions. Load flow modeling and analysis were carried out using real network data for a case study in New York City (NYC). The results validate the hypothesis that distributed energy resources (DERs) at WRRFs can play a role in improving grid operation and resiliency. Full article

This study proposes an integrated approach to developing a Microservice, Cloud Computing, and Software as a Service (SaaS)-based Real-Time Storm Sewer Simulation System (MBSS). The MBSS combined the Storm Water Management Model (SWMM) microservice running on the EC2 Amazon Web Services (AWS) cloud platform and an Internet of Things (IoT) monitoring device to prevent disasters in smart cities. The Python language and Docker container were used to develop the MBSS and Web API of the SWMM microservice. The IoT comprised a pressure water level meter, an Arduino, and a Raspberry Pi. After laboratory channel testing, the simulated and IoT-monitored water levels under different flow rates indicate that the simulated water level in MBSS was such as that monitored by the IoT. These findings suggest that MBSS is feasible and can be further used as a reference for smart urban early warning systems. The MBSS can be applied in on-site stormwater sewers during heavy rain, with the goal of issuing early warnings and reducing disaster damage. The use case can be the process by which the SWMM model parameters will be optimized based on the water level data from IoT monitoring devices in stormwater sewer systems. The predicted rainfall will then be used by the SWMM microservices of MBSS to simulate the water levels at all manholes. The status of the water levels will finally be applied to early warning. Full article

The apple is a delicious fruit with high nutritional value that is widely grown around the world. Apples are traditionally picked by hand, which is very inefficient. The development of advanced fruit-picking robots has great potential to replace manual labor. A major prerequisite for a robot to successfully pick fruits the accurate identification and positioning of the target fruit. The active laser vision systems based on structured algorithms can achieve higher recognition rates by quickly capturing the three-dimensional information of objects. This study proposes to combine the laser active vision system with the YOLOv5 neural network model to recognize and locate apples on trees. The method obtained accurate two-dimensional pixel coordinates, which, when combined with the active laser vision system, can be converted into three-dimensional world coordinates for apple recognition and positioning. On this basis, we built a picking robot platform equipped with this visual recognition system, and carried out a robot picking experiment. The experimental findings showcase the efficacy of the neural network recognition algorithm proposed in this study, which achieves a precision rate of 94%, an average precision mAP% of 92.86%, and a spatial localization accuracy of approximately 4 mm for the visual system. The implementation of this control method in simulated harvesting operations shows the promise of more precise and successful fruit positioning. In summary, the integration of the YOLOv5 neural network model with an active laser vision system presents a novel and effective approach for the accurate identification and positioning of apples. The achieved precision and spatial accuracy indicate the potential for enhanced fruit-harvesting operations, marking a significant step towards the automation of fruit-picking processes. Full article

The concept of a smart city is based on the extensive multidimensional use of information and communication technologies to create the most favorable living conditions for residents and visitors. It is also important to create favorable conditions for economic activity while respecting the environment. One of the most important dimensions of this concept is security in the broadest sense, particularly that which concerns urban residents. This article addresses this subject by analyzing crime and determining the state of safety in 16 Polish provincial cities between 2013–2022. The measure of this state was chosen to be a set of indicators characterizing a number of registered criminal and economic offenses in the studied cities. On this basis, values of the indices of the dynamics of change for these offenses in individual cities in the analyzed period were determined. In the next stage, the number of offenses was compared to the number of residents of the cities under study and the indices of concentration for total offenses (LQ_T) and for individual types of offenses (LQ_n) were determined. Based on these results, the studied cities were divided into four concentration levels. Afterward, these results were used for a multi-criteria analysis of the safety of studied cities, which was carried out using the TOPSIS method. The calculated values of the safety index (Pi) formed the basis for creating a ranking and specifying security levels of studied cities. The results indicate a wide variation among the cities in terms of safety levels. Gdańsk, Bydgoszcz, Olsztyn and Zielona Góra were found to be the safest cities, while Szczecin was found to be the least safe. The methodology developed and the results obtained show the validity of conducting comparative research in areas relevant to the implementation of the smart cities concept. The knowledge gained can be used to build strategies and conduct policies with regard to improving safety in cities, especially those aspiring to be smart cities. Full article

The smart cities paradigm has gained significant attention as a tool to address the multifaceted challenges posed by contemporary urban mobility systems. While cities are eager to integrate cutting-edge technologies to evolve into digital and intelligent hubs, they often deal with infrastructure and governance bottlenecks that prevent the rapid adoption of industry-driven innovations. This study introduces a three-step methodological approach to forecast a city’s innovation readiness in urban mobility, thus facilitating city-led innovation and identifying key areas within urban mobility systems that require attention. Initially, a comprehensive literature review was undertaken to ascertain the most impactful innovation indicators influencing a city’s ability to embrace new technologies. Subsequently, Principal Component Analysis (PCA) was applied to identify these indicators, highlighting the primary markers of innovation for each city. The final step involved the application of both random and fixed-effects regression models to quantify the influence of distinct unobserved variables—such as economic, cultural, and political factors—on the innovation readiness of various cities. The methodology’s effectiveness was tested using data from cities across diverse regions. The findings underscore that merely 7 out of 21 innovation indicators are critical for assessing a city’s innovation readiness. Moreover, the random-effects model was identified as the most suitable for capturing the nuances of unobserved variables in the studied cities. The innovation readiness scores at the city level revealed a diverse range, with cities like Madrid, Gothenburg, and Mechelen demonstrating high readiness, while others like Kalisz and Datong showed lower scores. This research contributes to the strategic planning for smart cities, offering a robust framework for policymakers to enhance innovation readiness and foster sustainable urban development, with a newfound emphasis on city-specific analysis. Full article

This study aims to compare the technological solutions that can contribute to more sustainable energy use in the residential sector. Specifically, the goal of the study is to evaluate the environmental impact of different energy (heat and electricity) supply technologies applicable for an average size single-family building in Latvia, a country known for climatic condition characterized by cold winters with frequent snowfall. The study applies the lifecycle assessment methodology of ISO 14040 and the impact assessment method known as ReCiPe 2016 v1.1, which has not been used before for the scope addressed in the study in the context of single-family building energy supply technologies for climatic conditions in Latvia. Thus, the results of the study will provide new information for more sustainable energy solutions in this area of study. The technologies included in the defined scenarios are conventional boiler, electricity from the grid, Stirling engine, and solar photovoltaics (PV). The results of the lifecycle impact assessment for damage categories revealed that all scenarios have a high impact on human health due to fine particulate matter formation followed by global warming. Regarding the damage to the ecosystem, the terrestrial ecotoxicity category has highest impact, followed by global warming. Sensitivity analyses affirmed the model’s validity and also showed that the impacts of conventional systems were most sensitive to changes in electricity consumption, and therefore, the scenarios with electricity supply from a Stirling engine or PV can be considered a more robust solution under changing electricity demands from an environmental perspective. Full article

The socioeconomic contribution of microbusinesses towards emerging economies is undeniable. However, numerous factors have broadened the gap between microbusinesses and their smartification. This conceptual study proposes the Technology Adoption Model Canvas (TAMC) based on theories such as the Unified Theory of Acceptance and Use of Technology (UTAUT2), Diffusion of Innovation (DOI), and the Business Model Canvas (BMC) alongside four new/emerging variables, making it possible to understand technology adoption through both individual/cognitive and organizational/physical perspectives. The framework is developed for food service (FS) microbusinesses to facilitate their adaptability in current and future market conditions. Subsequently, we explain the development of the TAMC, including its significance, limitations, and avenues for future research. The proposed framework can provide a solution for FS microbusinesses towards a ‘smarter’ and more sustainable future. It further guides the evaluation of both microbusinesses’ readiness and the factors driving/impeding them towards/from adopting smart technology. Full article

Purpose- In recent years, there has been a notable surge in the utilization of emerging technologies, notably the Internet of Things (IoT), within the realm of business operations. However, empirical evidence has underscored a disconcerting trend whereby a substantial majority, surpassing 70%, of IoT adoption initiatives falter when confronted with the rigors of real-world implementation. Given the profound implications of IoT in augmenting product quality, this study endeavors to scrutinize the extant body of knowledge concerning IoT integration within the domain of agricultural logistics operations. Furthermore, it aims to discern the pivotal determinants that exert influence over the successful assimilation of IoT within business operations, with particular emphasis on logistics. Design/Methodology/Approach- The research utilizes a thorough systematic review methodology coupled with a meta-synthesis approach. In order to identify and clarify the key factors that influence IoT implementation in logistics operations, the study is grounded in the Resource-Based View theory. It employs rigorous grounded theory coding procedures, supported by the analytical capabilities of MAXQDA software. Findings- The culmination of the meta-synthesis endeavor culminates in the conceptual representation of IoT adoption within the agricultural logistics domain. This representation is underpinned by the identification of three overarching macro categories/constructs, namely: (1) IoT Technology Adoption, encompassing facets such as IoT implementation requisites, ancillary technologies essential for IoT integration, impediments encountered in IoT implementation, and the multifaceted factors that influence IoT adoption; (2) IoT-Driven Logistics Management, encompassing IoT-based warehousing practices, governance-related considerations, and the environmental parameters entailed in IoT-enabled logistics; and (3) the Prospective Gains Encompassing IoT Deployment, incorporating the financial, economic, operational, and sociocultural ramifications ensuing from IoT integration. The findings underscore the imperative of comprehensively addressing these factors for the successful assimilation of IoT within agricultural logistics processes. Originality- The originality of this research study lies in its pioneering effort to proffer a conceptual framework that furnishes a comprehensive panorama of the determinants that underpin IoT adoption, thereby ensuring its efficacious implementation within the ambit of agricultural logistics operations. Practical Implications- The developed framework, by bestowing upon stakeholders an incisive comprehension of the multifaceted factors that steer IoT adoption, holds the potential to streamline the IoT integration process. Moreover, it affords an avenue for harnessing the full spectrum of IoT-derived benefits within the intricate milieu of agricultural logistics operations. Full article

Civic engagement plays a critical role in smart city innovation and urban development by encouraging active participation in civic activities such as volunteering, voting, community organizing, or advocacy, all of which contribute to the development of local communities. This study highlights the need to assess civic engagement in smart cities in order to improve the interactions between technology and society. The study assessed the reliability and validity of the Civic Engagement Scale (CES) in the Czech context. The results presented are based on a representative sample of 1366 respondents from the general population aged 15–74. The study included univariate statistics, tests of internal consistency, and principal component analysis. In addition, the study presents the results of confirmatory factor analysis (CFA) that was conducted to examine the fit of the proposed model to empirical data. The results indicate that the CES has excellent psychometric properties, including high internal consistency and favorable absolute and incremental indices. The Czech version of the CES can be considered a valid and reliable instrument. The findings suggest using CES to research and evaluate policy interventions aimed at developing digital platforms that enable citizens to easily participate in urban planning and smart city projects, community-driven smart city projects that ensure local needs and preferences are addressed, or implementing incentive programs for citizens. Full article

The worldwide Sustainable Development Goals (SDGs) for smart cities and communities focus significant attention on air quality and climate change. Technology and management can reduce fossil fuel dependence in smart cities’ energy supply chains (SC). A sustainable smart city and reduced carbon emissions require coordinated technology and management with appropriate infrastructure. A systematic review of smart city SC management literature that reduces the carbon footprint (C.F) inspired this study. The study shows how each attribute reduces greenhouse gas (GHG) emissions. The Introduction highlights the subject matter and principal goal, which is to investigate how SC management strategies could assist smart cities in lowering their C.F. The Methods and Materials section provides a succinct description of the refining process in Systematic Reviews and Meta-Analyses in Scoping Reviews (PRISMA-ScR) relevant to C.F mitigation in smart city (SC) management. Significant works are described in the Results and Findings section, which exposes how smart cities and SC measurements reduce C.F. The Discussion section examines and scientifically debates the research findings. The Conclusion provides a scientific analysis based on the presented insights and features to enhance how policies must be coordinated to achieve the goal of this research study in a comprehensive way. Furthermore, it provides suggestions for practitioners and governments, and proposals for future research. The main contribution of this paper is conducting and proposing a framework for a better understanding of how the novel digital SCs, their components, and their management practices can help smart cities reduce their C.F. Full article

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis by integrating blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. Combining blockchain with intelligent power management systems makes the efficient utilization of hydrogen resources feasible. Using smart contracts and distributed ledger technology facilitates secure data analysis (SDA), real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus advancing clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, specifically on secure hydrogen data analysis and intelligent power management systems. Full article

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners. Full article

In the last decades, tourism in urban areas has been constantly increasing. The need for short-term accommodations has been coupled with the emergence of internet-based services, which makes it easier to match demand (i.e., tourists) and supply (i.e., housing). As a new mass tourist destination, Bologna, Italy, has been experiencing tensions between tourists and long-, mid-, or short-term renters. The possibility of easy profits for lessees has led to an increase in such housing, which can be rented out either for touristic reasons or not. This paper aims to unveil the contribution of short-term rental accommodations in distorting the real estate market and conditioning social and economic inequalities. To do this, multiple linear regression analyses (MLR) were performed between accommodation density, real estate market information, and indicators about social, economic, and demographic vulnerability and fragility. Analyses were based on official open data and datasets from a major web-based hospitality exchange platform, i.e., Airbnb, able to provide information on registered accommodations, e.g., type, characteristics (e.g., number of bedrooms and average rating), and location. Outputs of the analyses reveal the role of Airbnb in both rental market and social, economic, and demographic vulnerability and fragility and, hence, can be a solid tool for public policies, both housing- and tourism-related. Full article

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. Full article

The adoption and results achieved by “smart city” projects heavily rely on citizens’ acceptance and behavioral intention to embrace smart city living. Understanding the factors influencing citizens’ behavioral intention towards smart city living is crucial for the effective development and rollout of smart city initiatives. This research paper aims to assess the factors influencing citizens’ behavioral intention towards smart city living using quantitative research methods. Through a comprehensive literature review, an ideation structure was developed, integrating theoretical perspectives from the Technology Acceptance Model (TAM). The structure encompasses key variables such as perceived utility, convenience of use, engagement, trialability, observability, interoperability, willingness, and propensity to embrace smart city lifestyles. A quantitative methodological stance was employed to gather information from a statistically significant subset of citizens residing in urban areas in developed countries. A structured questionnaire, based on the theoretical framework, was formulated and distributed to the participants. Statistical analysis techniques, including structural equation modeling, were used for investigating connections between identified factors and citizens’ behavioral intention towards smart city living. Preliminary findings indicate that behavioral intention towards smart city living strongly depends on attitude and perceived usefulness. By addressing these factors, smart cities can foster greater citizen engagement, participation, and ultimately, the successful realization of smart city living. Full article

The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical regulations, etc. Many classical and metaheuristic-based approaches provide good solutions, but they are not intuitive, and they do not scale well for large cities and complex constraints. Many classical solution techniques often require prohibitive amounts of memory and their solutions are not easily explainable. We analyzed the layouts of the 50 most populous cities of the world and observed that any city can be represented as a composition of five basic primitive shapes (stretched to different extents). Based on this insight, we use results from classical topology to design a new charging station placement algorithm. The first step is a topological clustering algorithm to partition a large city into small clusters and then use precomputed solutions for each basic shape to arrive at a solution for each cluster. These cluster-level solutions are very intuitive and explainable. Then, the next step is to combine the small solutions to arrive at a full solution to the problem. Here, we use a surrogate function and repair-based technique to fix any resultant constraint violations (after all the solutions are combined). The third step is optional, where we show that the second step can be extended to incorporate complex constraints and secondary objective functions. Along with creating a full software suite, we perform an extensive evaluation of the top 50 cities and demonstrate that our method is not only 30 times faster but its solution quality is also 36.62% better than the gold standard in this area—an integer linear programming (ILP) approach with a practical timeout limit. Full article

In the quest to understand urban ecosystems, traditional evaluation techniques often fall short due to incompatible data sources and the absence of comprehensive, real-time data. However, with the recent surge in the availability of crowdsourced data, a dynamic view of urban systems has emerged. Recognizing the value of these data, this study illustrates how these data can bridge gaps in understanding urban interactions. Furthermore, the role of urban planners is crucial in harnessing these data effectively, ensuring that derived insights align with the practical needs of urban development. Employing the Design Science Methodology, the research study presents an assessment model grounded in the principles of the city ecosystem, drawing from the General System Theory for Smart Cities. The model is structured across three dimensions and incorporates twelve indicators. By leveraging crowdsourced data, the study offers invaluable insights for urban planners, researchers, and other professionals. This comprehensive approach holds the potential to revolutionize city sustainability assessments, deepening the grasp of intricate urban ecosystems and paving the way for more resilient future cities. Full article