Future Smart Cities Research Articles

Exploring best practices in smart public library services: a survey

Purpose This paper aims to the integration of smart cities and technologies has had a profound impact on public library services globally. However, debates continue about how public libraries are incorporating smart technologies and tools, and what this means for their role in smart city development. This paper explores how public libraries are evolving to meet community needs by providing both physical and digital services. Design/methodology/approach The purpose of this study is to assess the role of public libraries in the smart cities and how they are aligning toward development of smart concepts, technologies, services such as contributions to smart city objectives, community impact, the digital divide, unique projects and readiness for future smart city advancements. So, the authors explored 11 public library systems of cities across the world implementing smart city projects. Findings The authors found that all the library systems offer extensive print and digital collections, sustainable buildings and a well-connected network of branches. They provide self-checkout services, makerspaces equipped with 3D printing and coding workshops and advanced recording and video editing technologies. Furthermore, these libraries function as community centers, serving diverse groups including children, women, the elderly and marginalized communities. They function as third spaces that foster lifelong learning and digital literacy. Originality/value Although smart cities face substantial debate regarding their features, tools and feasibility, public libraries have adapted and redefined their roles to become influential public institutions. They now play a crucial part in supporting vibrant and dynamic communities and neighborhoods within smart cities.

Graphene reinforced cement-based triboelectric nanogenerator for efficient energy harvesting in civil infrastructure

This paper investigated a graphene reinforced cement-based triboelectric nanogenerator (TENG) aimed at harvesting mechanical energies in infrastructure, such as pedestrians, vehicles, human-induced vibrations, and natural stimulus like wind and earthquakes. The triboelectric layers of the cement-based TENG consisted of a fully cured graphene modified cement-based plate and a polytetrafluoroethylene (PTFE) film, which were tested under a contact-separation mode. Microstructural analysis indicated that the graphene was well-dispersed in the cementitious matrix, and the graphene-cement composites achieved excellent compressive and flexural strengths of 53.0 and 3.5 MPa, respectively. The electrical characteristics of the graphene-cement composites, specifically their resistivity and impedance, showed that they did not reach the percolation threshold, making them ideal dielectric materials with a dielectric constant of 100 at 1 kHz. The performance of the cement-based triboelectric nanogenerator (TENG) varied depending on the amplitude and frequency of the contact-separation cycle. At a frequency of 10 Hz and under a force of 100 N, the short-circuit current and open-circuit voltage peaked at 3.62 µA and 279.4 V, respectively, achieving a maximum power density of 95 mW/m2 with a 100 MΩ resistor. In practical applications, this TENG charged a 10 μF capacitor to 3.1 V within one minute and to 57.2 V in one hour. Additionally, manual operation of the TENG enabled the lighting of 29 LEDs with one minute of hand pressure. By utilizing triboelectric effects, the results provide the feasibility of self-powering concrete structures and pavements for future smart cities.

Artificial Neural Networks and Ensemble Learning for Enhanced Liquefaction Prediction in Smart Cities

This paper examines how smart cities can address land subsidence and liquefaction in the context of rapid urbanization in Japan. Since the 1960s, liquefaction has been an important topic in geotechnical engineering, and extensive efforts have been made to evaluate soil resistance to liquefaction. Currently, there is a lack of machine learning applications in smart cities that specifically target geological hazards. This study aims to develop a high-performance prediction model for estimating the depth of the bearing layer, thereby improving the accuracy of geotechnical investigations. The model was developed using actual survey data from 433 points in Setagaya-ku, Tokyo, by applying two machine learning techniques: artificial neural networks (ANNs) and bagging. The results indicate that machine learning offers significant advantages in predicting the depth of the bearing layer. Furthermore, the prediction performance of ensemble learning improved by about 20% compared to ANNs. Both interdisciplinary approaches contribute to risk prediction and mitigation, thereby promoting sustainable urban development and underscoring the potential of future smart cities.

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Contextual visual and motion salient fusion framework for action recognition in dark environments

Infrared (IR) human action recognition (AR) exhibits resilience against shifting illumination conditions, changes in appearance, and shadows. It has valuable applications in numerous areas of future sustainable and smart cities including robotics, intelligent systems, security, and transportation. However, current IR-based recognition approaches predominantly concentrate on spatial or local temporal information and often overlook the potential value of global temporal patterns. This oversight can lead to incomplete representations of body part movements and prevent accurate optimization of a network. Therefore, a contextual-motion coalescence network (CMCNet) is proposed that operates in a streamlined and end-to-end manner for robust action representation in darkness in a near-infrared (NIR) setting. Initially, data are preprocessed to separate foreground, normalized, and resized. The framework employs two parallel modules: the contextual visual features learning module (CVFLM) for local feature extraction, and the temporal optical flow learning module (TOFLM) for acquiring motion dynamics. These modules focus on action-relevant regions used shift window-based operations to ensure accurate interpretation of motion information. The coalescence block harmoniously integrates the contextual and motion features within a unified framework. Finally, the temporal decoder module discriminatively identifies the boundaries of the action sequence. This sequence of steps ensures the synergistic optimization of both CVFLM and TOFLM and thorough competent feature extraction for precise AR. Evaluations of CMCNet are carried out on publicly available datasets, InfAR and NTURGB-D, where superior performance is achieved. Our model yields the highest average precision of 89% and 85% on these datasets, respectively, representing an improvement of 2.25% (on InfAR) compared to conventional methods operating at spatial and optical flow levels which underscores its efficacy.

Enhancing Agricultural Practices through ICT: The Diffusion and Impact of the Among Tani Application in Batu City, Indonesia

This study examines the implementation and impact of the Among Tani application, a digital platform designed to support farmers in Batu City, Indonesia, as part of a broader smart city initiative. Utilizing a qualitative approach, the research investigates the diffusion process of the application, its role as a virtual communication medium, and its effects on organizational structures within farmer groups. The study reveals a staged adoption process influenced by factors such as farmer age and crop vulnerability. While the application has enhanced access to agricultural information and improved pest management, challenges including the digital divide and integration with traditional practices persist. The findings highlight the complex interplay between digital innovation and existing agricultural systems, emphasizing the need for context-sensitive approaches in implementing agricultural technologies. This research contributes to the understanding of digital agriculture's potential in supporting sustainable rural development and provides insights for future smart city initiatives in agricultural contexts.

Seamless rate adaptation for wide SNR range in FSO systems based on rate compatible modulation.

We propose and experimentally demonstrate a large-range and seamless rate-adaptive free-space optical (FSO) scheme based on rate compatible modulation (RCM). It utilizes a mapping method through weighted summation, enabling an even increase of bit energy. This allows the system to seamlessly adjust the throughput. In addition, the handover mechanism based on rate check and acknowledgment signals ensures a wide coverage of signal-to-noise ratio (SNR) fluctuation. It is worth noting that this scheme does not require any changes to the mapping matrix and decoding algorithm, making it easier to implement in real-world systems. Simulation proves the superiority of the proposed approach in terms of coding rate coverage by applying a fixed mapping matrix compared to a traditional adaptive modulation and coding scheme. Experimental demonstration over a 50 m FSO link verifies that the SNR dynamic range of this scheme is >15 dB with a seamless rate adjustment between 6.7 Gb/s and 53.6 Gb/s and the operability of RCM is not limited by the time scale of turbulence, revealing huge potential for the massive connections in future smart cities.

The Future of Smart Cities: Global Trends and Emerging Challenges in Urban Development

This article explores the evolving concept of "smart cities" and the global trends shaping their development. It examines the potential benefits of smart city initiatives, including improved resource efficiency, quality of life, and environmental sustainability. However, the article also acknowledges the challenges associated with smart city development, such as digital inequality, privacy concerns, and potential security risks.

Congestion Transition on Random Walks on Graphs.

The formation of congestion on an urban road network is a key issue for the development of sustainable mobility in future smart cities. In this work, we propose a reductionist approach by studying the stationary states of a simple transport model using a random process on a graph, where each node represents a location and the link weights give the transition rates to move from one node to another, representing the mobility demand. Each node has a maximum flow rate and a maximum load capacity, and we assume that the average incoming flow equals the outgoing flow. In the approximation of the single-step process, we are able to analytically characterize the traffic load distribution on the single nodes using a local maximum entropy principle. Our results explain how congested nodes emerge as the total traffic load increases, analogous to a percolation transition where the appearance of a congested node is an independent random event. However, using numerical simulations, we show that in the more realistic case of synchronous dynamics for the nodes, entropic forces introduce correlations among the node states and favor the clustering of empty and congested nodes. Our aim is to highlight the universal properties of congestion formation and, in particular, to understand the role of traffic load fluctuations as a possible precursor of congestion in a transport network.

Planning and Development of Smart Cities Based on the Concept of Sustainable Development

Based on the idea of sustainable development, this paper explores the planning and development of smart cities. Cities face many issues as global urbanization picks up speed, including environmental pollution, resource depletion, traffic congestion, and social inequality. To address these challenges, the notion of sustainable development has become a guiding principle for many city policymakers, aiming to meet contemporary needs while safeguarding future development. Smart cities utilize advanced information and communication technology to enhance urban operational efficiency, improve resource management, and enhance residents' quality of life, thereby providing a more sustainable development model for cities. This paper focuses on the key areas of smart city planning, including energy utilization and environmental protection, smart transportation and urban mobility, smart city planning and land use, social equity, and community engagement. Using Shenzhen as a case study, the practical application of the notion of sustainable development in smart city construction is elucidated, providing valuable insights and practical references for future smart cities.

Enhancing the city-level thermal environment through the strategic utilization of urban green spaces employing geospatial techniques.

Smart urban planning needs to have a multicriteria-based approach to prevent the deteriorating local thermal climate. Maximizing the cooling potential using the available grey infrastructure would be the utmost priority of future smart cities. Remote sensing and GIS can be the appropriate tools to develop a climate-resilient urban planning framework. Studies are needed to include different features of vertical and horizontal landscaping to mitigate heat stress and enhance liveability at the city level. With this goal, the current work outlined a holistic approach to efficiently using green spaces with minimal reconstruction. The problem of regional climate threat was evaluated with urban heat island characterization. Moran's I clustering identified nearly 12% of the study area to be under considerable heat stress during summer days. Multiple techniques, such as mapping local climate zones, segment mean shift-based roof extraction, vegetation index computation, solar azimuth-based green wall site selection, etc., were applied to formulate solutions and provide an integrated method for city-level environment enhancement. A considerable area was identified as most suitable for green roof cover, and it was also computed that the transition towards green roof at only these locations may bring down the maximum heat island intensity by 0.74°C. Additionally, solar zenith, illumination effect, and building height information were combined to create a distinct method where vertical plantation would flourish exceptionally. A rigorous assessment of more than 130 urban green spaces further quantified the relation between landscape geometry and cooling effect to provide optimum green space designs for future urban planning.

A Spatio-Temporal Examination of Land Use and Land Cover Changes in Smart Cities of the Delhi–Mumbai Industrial Corridor

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department’s sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC.

Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery to their communities, ranging from simple task automation to more complex engineering endeavours. As more local governments adopt AI, it is imperative to understand the functions, implications, and consequences of these advanced technologies. Despite the growing importance of this domain, a significant gap persists within the scholarly discourse. This study aims to bridge this void by exploring the applications of AI technologies within the context of local government service provision. Through this inquiry, it seeks to generate best practice lessons for local government and smart city initiatives. By conducting a comprehensive review of grey literature, we analysed 262 real-world AI implementations across 170 local governments worldwide. The findings underscore several key points: (a) there has been a consistent upward trajectory in the adoption of AI by local governments over the last decade; (b) local governments from China, the US, and the UK are at the forefront of AI adoption; (c) among local government AI technologies, natural language processing and robotic process automation emerge as the most prevalent ones; (d) local governments primarily deploy AI across 28 distinct services; and (e) information management, back-office work, and transportation and traffic management are leading domains in terms of AI adoption. This study enriches the existing body of knowledge by providing an overview of current AI applications within the sphere of local governance. It offers valuable insights for local government and smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision. Additionally, it highlights the importance of using these insights to guide the successful integration and optimisation of AI in future local government and smart city projects, ensuring they meet the evolving needs of communities.

Monitoring urban construction and quarry blasts with low-cost seismic sensors and deep learning tools in the city of Oslo, Norway

The aim of this study is to collect information about events in the city of Oslo, Norway, that produce a seismic signature. In particular, we focus on blasts from the ongoing construction of tunnels and under-ground water storage facilities under populated areas in Oslo. We use seismic data recorded simultaneously on up to 11 Raspberry Shake sensors deployed between 2021 and 2023 to quickly detect, locate, and classify urban seismic events. We present a deep learning approach to first identify rare events and then to build an automatic classifier from those templates. For the first step, we employ an outlier detection method using auto-encoders trained on continuous background noise. We detect events using an STA/LTA trigger and apply the auto-encoder to those. Badly reconstructed signals are identified as outliers and subsequently located using their surface wave (Rg) signatures on the seismic network. In a second step, we train a supervised classifier using a Convolutional Neural Network to detect events similar to the identified blast signals. Our results show that up to 87% of about 1,900 confirmed blasts are detected and locatable in challenging background noise conditions. We demonstrate that a city can be monitored automatically and continuously for explosion events, which allows implementing an alert system for future smart city solutions.

The impact of radio on the construction of smart cities

In the process of smart cities, radio technology has played an increasingly important role. With the continuous acceleration of the global urbanization process, cities are facing many challenges. Driven by the governments vigorous support and business promotion, wireless communication technology has developed rapidly, and new wireless communication technology has continued to emerge, providing more choices and possibilities for smart city construction. This article reviews and analyzes the actual case analysis, studies the impact of radio technology on the construction of smart cities, while explaining the importance of radio security management. The results showed that the application of radio technology enabled smart cities to achieve functions such as intelligent traffic management, intelligent communication, and intelligent energy management, but there were also problems such as leakage privacy, signal interference, and imbalance in regional development. In the process of future smart city construction, it is necessary to further optimize the safety and popularity of radio, promote the use of radio, narrow the gap between the use of different regions and cities, and enhance the radio management, so that it can better serve the construction of smart cities.

Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision

Active building energy management can facilitate the development of low-carbon buildings and support flexible operations of future smart cities, thanks to advancements in digitalization To fully leverage these benefits, it is essential to integrate diverse objectives and engage multiple stakeholders. However, a gap remains in comprehensive field insights into emission reduction, flexibility provision, and user impacts. This study examined how a real occupied building, with all its energy assets, could function as an emission-aware prosumer with flexible energy consumption. An existing building energy management system was enhanced by integrating a model predictive control strategy. The setup reduced equivalent carbon emissions from electricity imports and provided flexibility to the energy system. The experimental results indicate an emission reduction of 12.5% compared to a rule-based controller that maximized PV self-consumption. In addition, a minimal flexibility provision experiment was demonstrated with a locally emulated distribution system operator. The results suggest that flexibility was provided without the risk of rebound effects, as flexibility was quantified and communicated to the system operator in advance. This study demonstrates the feasibility of low-carbon buildings and their support for flexible energy systems, while also identifying and discussing practical scalability challenges.

5G-enabled, battery-less smart skins for self-monitoring megastructures and digital twin applications.

With the current development of the 5G infrastructure, there presents a unique opportunity for the deployment of battery-less mmWave reflect-array-based sensors. These fully-passive devices benefit from having a larger detectability than alternative battery-less solutions to create self-monitoring megastructures. The presented 'smart' skin sensor uses a Van-Atta array design enabling ubiquitous local strain monitoring for the structural health monitoring of composite materials featuring wide interrogation angles. Proof-of-concept prototypes of these 'smart' skin millimeter-wave identification tags, that can be mounted on or embedded within common materials used in wind turbine blades, present a highly-detectable radar cross-section of - 33.75 dBsm and - 35.00 dBsm for mounted and embedded sensors respectively. Both sensors display a minimum resolution of 202 -strain even at 40 off-axis enabling interrogation of the fully-passive sensor at oblique angles of incidence. When interrogated from a proof-of-concept reader, the fully-passive, sticker-like mmID enables local strain monitoring of both carbon fiber and glass fiber composite materials. The sensors display a repeatable and recoverable response over 0-3000 -strain and a sensitivity of 7.55 kHz/ -strain and 7.92 kHz/ -strain for mounted and embedded sensors, respectively. Thus, the presented 5G-enabled battery-less sensor presents massive potential for the development of ubiquitous Digital Twinning of composite materials in future smart cities architectures.

Exploring the Roles, Future Impacts, and Strategic Integration of Artificial Intelligence in the Optimization of Smart City—From Systematic Literature Review to Conceptual Model

Artificial Intelligence (AI) is one of the science fields with huge potential to create a cognitive and tech-leaping type of future smart city design/development. However, extant studies lag behind recent applications, potential growth areas, and the challenges associated with AI implementation. This study examines AI’s current role, trend, and future potential impacts in enhancing smart city drivers. The methodology entails conducting a Systematic Literature Review (SLR) of publications from 2022 onwards. The approach involves qualitative deductive coding methods, descriptive statistical analysis, and thematic analysis. The findings revealed the impacts of AI in (i) public services and connectivity, (ii) improving accessibility and efficiency, (iii) quality healthcare, (iv) education, and (v) public safety. Likewise, strategies, such as collaborative ecosystems, digital infrastructure, capacity building, and clear guidelines and ethical framework, were proposed for fostering the integration of AI in potential future smart cities. This research fills a notable gap in the current understanding of AI’s specific contributions to smart cities, offering insights for stakeholders in urban planning, computer science, sociology, economics, environmental science, and smart city initiatives. It serves as a strategic guideline and scholarly research output for enhancing smart city design. It also underscores the potential of AI in creating dynamic, sustainable, and efficient urban environments.

Towards A Smart City Concept - Machine Learning Smarts for the Estimation of Future Temperature Rise in Tabuk City

In the Middle Eastern peninsula especially in Saudi Arabia, there is a varsity temperature variation among the individual regions. As far as the city of Tabuk is concerned, no study has been conducted, regarding climate change (the temperature rise) in the Tabuk region and its implications on society and for the flagship “Future Smart Cities” concept. In this paper, machine learning algorithms are used to predict the future temperature values in the Tabuk region. The machine learning algorithms were trained on the data collected from the real-time weather radar stations of the region. Different features from the dataset are used for machine learning models to predict the future temperature. These unique features, for example, humidity and pressure, impact the accurate predictability of the temperature. Temperature prediction is modelled as a regression problem due to the nature of the data, therefore, different machine learning regression models were developed, i.e. Artificial Neural Network ANN-based techniques (Multi-layer Perceptron (MLP)), Decision Trees (DT), K-Nearest Neighbours (KNN), and Support Vector Regression (SVR). Encouragingly, the preliminary model evaluations utilizing Mean Absolute Error (MAE) yielded a high accuracy of 90% on the testing dataset. The findings are envisaged to inform decision-makers in the Climate and Weather Ministry of Tabuk City, potentially contributing to the advancement of the city's "Future Smart Cities" concept.

Modern requirements for energy-efficient renovation of historical buildings

Мodern urban development trends includes the importance of taking historical buildings into account when implementing sustainable development strategies. Energetic modernization of historical buildings has both economic and social benefits. Buildings of historical importance often do not correspond to the requirements of modern directives, but the use of certain technical means and architectural practices allows to achieve a significant increase in energy potential. Energy-efficient renovation of historic buildings is an important component in the formation of future smart cities, since historic buildings themselves connect society with the heritage of the past and represent local and historical features of the city and its inhabitants. At the same time, the relevance of the problem is related to the insufficient systematization of data and information resources along with the constant development of tools for diagnosis and assessment of potential changes when working with historical buildings according to their characteristics. The main modern requirements for energy-efficient renovation of historical buildings, as well as the stages of analysis of the characteristics of historical buildings, and the further formation of the renovation strategy and the selection of appropriate methods and measures are considered. Considered limitations and features of the renovation of historic buildings (general engineering limitations due to the historical context, specific characteristics of a specific building, design limitations).