Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

Deep learning solutions for smart city challenges in urban development

In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized. This research embarks on a multifaceted exploration that combines the power of deep learning with Bayesian regularization techniques to enhance the performance and reliability of neural networks tailored for urban planning applications. Deep learning, characterized by its ability to extract complex patterns from vast urban datasets, has the potential to offer unprecedented insights into urban dynamics, transportation networks, and environmental sustainability. However, the complexity of these models often leads to challenges such as overfitting and limited interpretability. To address these issues, Bayesian regularization methods are employed to imbue neural networks with a principled framework that enhances generalization while quantifying predictive uncertainty. This research unfolds with the practical implementation of Bayesian regularization within neural networks, focusing on applications ranging from traffic prediction, urban infrastructure, data privacy, safety and security. By integrating Bayesian regularization, the aim is to, not only improve model performance in terms of accuracy and reliability but also to provide planners and decision-makers with probabilistic insights into the outcomes of various urban interventions. In tandem with quantitative assessments, graphical analysis is wielded as a crucial tool to visualize the inner workings of deep learning models in the context of urban planning. Through graphical representations, network visualizations, and decision boundary analysis, we uncover how Bayesian regularization influences neural network architecture and enhances interpretability.

Research and innovation paving the way for climate neutrality in urban transport: Analysis of 362 cities on their journey to zero emissions

The EU Mission on Climate Neutral and Smart Cities is an ambitious initiative aiming to involve a wide range of stakeholders and deliver 100 climate-neutral and smart cities by 2030. We analysed the information submitted in the expressions of interest by 362 candidate cities. The majority of the cities' strategies for climate neutrality include urban transport as a main sector and combine the introduction of new technologies with the promotion of public transport and active mobility. We combined the information from the EU Mission candidate cities with data from the CORDIS and TRIMIS databases, and applied a clustering algorithm to measure proximity to foci of H2020 funding. Our results suggest that preparedness for the EU Mission is correlated with research and innovation activities on transport and mobility. Horizon 2020 activities specific to transport and mobility significantly increased the likelihood of a city to be a candidate. Among the various transport technology research pathways, smart mobility appears to have a major role in the development of solutions for climate neutrality.

Efficient content caching for 5G assisted vehicular networks

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale-Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

A hybrid LBP-CNN with YOLO-v5-based fire and smoke detection model in various environmental conditions for environmental sustainability in smart city

Smart, secure, and environmentally friendly smart cities are all the rage in urban planning. Several technologies, including the Internet of Things (IoT) and edge computing, are used to develop smart cities. Early and accurate fire detection in a Smart city is always desirable and motivates the research community to create a more efficient model. Deep learning models are widely used for fire detection in existing research, but they encounter several issues in typical climate environments, such as foggy and normal. The proposed model lends itself to IoT applications for authentic fire surveillance because of its minimal configuration load. A hybrid Local Binary Pattern Convolutional Neural Network (LBP-CNN) and YOLO-V5 model-based fire detection model for smart cities in the foggy scenario is presented in this research. Additionally, we recommend a two-part technique for extracting features to be applied to YOLO throughout this article. Using a transfer learning technique, the first portion of the proposed approach for extracting features retrieves standard features. The section part is for retrieval of additional valuable information related to the current activity using the LBP (Local Binary Pattern) protective layer and classifications layers. This research utilizes an online Kaggle fire and smoke dataset with 13950 normal and foggy images. The proposed hybrid model is premised on a two-cascaded YOLO model. In the initial cascade, smoke and fire are detected in the normal surrounding region, and the second cascade fire is detected with density in a foggy environment. In experimental analysis, the proposed model achieved a fire and smoke detection precision rate of 96.25% for a normal setting, 93.2% for a foggy environment, and a combined detection average precision rate of 94.59%. The proposed hybrid system outperformed existing models in terms of better precision and density detection for fire and smoke.

Better, not more, lighting: Policies in urban areas towards environmentally-sound illumination of historical stone buildings that also halts biological colonization

Anthropogenic or Artificial light at night (ALAN) pollution, or more simply light pollution, is an issue of increasing concern to the general public, as well as to scientists and politicians. However, although advances have been made in terms of scientific knowledge, these advances have not been fully transferred to or considered by politicians. In addition, illumination of stone monuments in urban areas is an emerging contribution to ALAN pollution that has scarcely been considered to date. This paper presents a literature review of the topic of light pollution and related policies, including a bibliometric analysis of studies published between 2020 and 2022. The prevailing legislation in Europe regarding the regulation of outdoor lighting, which emphasises the complexity of controlling light pollution, is summarised and the regulation of monumental lighting in Spain is discussed. Findings concerning the impact of ALAN on biodiversity in urban areas, and the promising biostatic effect of ornamental lighting (halting biological colonization on stone monuments, mainly caused by algae and cyanobacteria) are described. Finally, trends in monument illumination and policymaking towards environmentally sustainable management are considered.

A machine-learning approach to a mobility policy proposal

The objective of the URBANITE project is to design an open-data, open-source, smart-city framework to enhance the decision-making processes in European cities. The framework's basis is a robust and user-friendly simulation tool that is supplemented with several innovative service modules. One of the modules, a multi-output, machine-learning unit, is deployed on the simulation results, enabling city officials to more effectively analyse vast quantities of data, discern patterns and trends, and so facilitate advanced policy decisions. The city's decision makers define potential city scenarios, key performance indicators, and a utility function, while the module assists in identifying the policy that is best aligned with the stipulated constraints and preferences. One of the main improvements is a speeding up of the policy testing for the decision makers, reducing the time needed for one policy verification from 3 hours to around 10 seconds. The system was evaluated for Bilbao's Moyua area, where it suggested strategies that could result in a decrease in emissions of more than 5% C O 2 , NOx, PM in the selected area and a broader part of the city with a machine-learning accuracy of 91%. The system was therefore able to provide valuable insights into effective policies for restricting private traffic in specific districts and identifying the most advantageous times for these restrictions.

Comparison of different waste bin monitoring approaches: An exploratory study

Waste bin monitoring solutions are an essential step towards smart cities. This study presents an exploratory analysis of two waste bin monitoring approaches: (1) ultrasonic sensors installed in the bins and (2) visual observations (VO) of the waste collection truck drivers. Bin fill level data was collected from a Portuguese waste management company. A comparative statistical analysis of the two datasets (VO and sensor observations) was performed and a predictive model based on Gaussian processes was applied to enable a trade-off analysis of the number of collections versus the number of overflows for each monitoring approach. The results demonstrate that the VO are valuable and reveal that significant improvements can be achieved for either of the monitoring approaches in relation to the current situation. A monitoring approach based on VO combined with a predictive model is shown to be viable and leads to a considerable reduction in the number of collections and overflows. This approach can enable waste collection companies to improve their collection operations with minimal investment costs during their transition to fully sensorized bins.

Hybrid computational and real data-based positioning of small cells in 5G networks

One of the key technologies in smart cities is the use of next generation networks such as 5G networks. Mainly because this new mobile technology offers massive connections in densely populated areas in smart cities, thus playing a crucial role for numerous subscribers anytime and anywhere. Indeed, all the most important infrastructure to promote a connected world is being related to next generation networks. Specifically, the small cells transmitters is one of the 5G technologies more relevant to provide more connections and to attend the high demand in smart cities. In this article, a smart small cell positioning is proposed in the context of a smart city. The work proposal aims to do this through the development of a hybrid clustering algorithm with meta-heuristic optimizations to serve users, with real data, of a region satisfying coverage criteria. Furthermore, the problem to be solved will be the best location of the small cells, with the minimization of attenuation between the base stations and its users. The possibilities of using multi-objective optimization algorithms based on bioinspired computing, such as Flower Pollination and Cuckoo Search, will be verified. It will also be analyzed by simulation which power values would allow the continuity of the service with emphasis on three 5G spectrums used around the world: 700 MHz, 2.3 GHz and 3.5 GHz.

Smart solutions for urban health risk assessment: A PM2.5 monitoring system incorporating spatiotemporal long-short term graph convolutional network

Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM_2.5 monitoring network and simultaneously forecasts PM_2.5 concentrations at all locations, allowing the monitoring of the health risk associated with exposure to these levels. First, this study uses a graph convolutional layer to incorporate the spatial relationship of the neighboring stations at their current state with real-time measurements. Then, it is coupled to a deep learning temporal model to form the long- and short-term time-series graph convolutional network (LSTGraphNet) model, anticipating high pollutant concentration events. This work tested the proposed model with a case study of an existing ambient air quality monitoring network in South Korea. LSTGraphNet model showed prediction performances of PM_2.5 at multiple monitoring stations with a mean absolute error (MAE) of 1.82 μg/m³, 4.46 μg/m³, and 4.87 μg/m³ for forecasting horizons of one, three, and 6 h ahead, respectively. Compared to conventional sequential models, this architecture was superior among the state-of-the-art baselines, where the MAE decreased to 41%, respectively. The results of the study showed that the proposed architecture was superior to conventional sequential models and could be used as a tool for decision-making in smart cities by revealing hotspots of higher and lower PM_2.5 concentrations in the long term.

Artificial intelligence-assisted air quality monitoring for smart city management

Background

The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities.

Methods

This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions.

Results

In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.

The nexus between digitalization and sustainability: A scientometrics analysis

Digitalization and sustainability are among the most critical mega-trends in 21st century. The nexus between digitalization and sustainability unfolds exciting opportunities in addressing global challenges, creating a just and sustainable society and laying the groundwork for achieving the Sustainable Development Goals. Several studies have reviewed the link between these two paradigms and how they mutually impact one another. However, most of these reviews are qualitative and manual literature reviews that are prone to subjectivity and so lacking the required rigor. Given the above, this study aims to provide a comprehensive and objective review of the knowledge base on how digitalization and sustainability actually and potentially contribute to each other and highlight the key research that links these two megatrends. A comprehensive bibliometric analysis of academic literature is conducted to objectively visualize the research status quo across time, disciplines, and countries. The Web of Science (WOS) database was searched for relevant publications published between January 1, 1900, and October 31, 2021. The search returned 8629 publications, of which 3405 were identified as primary documents pertaining to the study presented below. The Scientometrics analysis identified prominent authors, nations, organizations, prevalent research issues and examined how they have evolved chronologically. The critical review of results reveals four main domains in research on the nexus of sustainability and digitalization including Governance, Energy, Innovation, and Systems. The concept of Governance is developed within the Planning and Policy-making themes. Energy relates to the themes of emission, consumption, and production. Innovation has associated with the themes of business, strategy, and values & environment. Finally, systems interconnect with networks, industry 4.0, and the supply chain. The findings are intended to inform and stimulate more research and policy-making debate on the potential interconnection between sustainability and digitization, particularly in the post-COVID-19 era.

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Background

As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train.

Methods

Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments.

Results

The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities

Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network's data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed "environmentally sustainable smart cities." This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991-2015, 2016-2019, and 2020-2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods-due to the acceleration of the digitalization and decarbonization agendas-thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time-some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies.

Fog computing-assisted path planning for smart shopping

A Smart City (SC) is a viable solution for green and sustainable living, especially with the current explosion in global population and rural-urban immigration. One of the fields that is not getting much attention in the Smart Economy (SE) is customer satisfaction. The SE is a component of SC that is concerned with using Information and Communication Technology (ICT) to improve stages of the traditional economy. In this paper, we propose a fog computing-based shopping recommendation system. Our simulations used Al-Madinah city as a case study. It aims to improve the customer shopping experience. Customers in shopping malls can connect to the system via Wi-Fi. Then the system recommends products to the shoppers according to their preferences. It optimizes shoppers' schedules using price, the distance between the shops, and the congestion. It also improves customers' savings by up to 30%. It also increases the shopping speed by up to 6.12% compared to the system proposed in the literature.

Machine learning based IoT system for secure traffic management and accident detection in smart cities

In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

Public Perceptions About Smart Cities: Governance and Quality-of-Life in Hong Kong

This study analyzes public perceptions about the impact of 'smart cities' programs on governance and quality-of-life. With smart city scholarship focusing primarily on technical and managerial issues, political legitimacy remains relatively underexplored-particularly in non-Western contexts. Drawing on a Hong Kong-based survey of over 800 residents conducted in 2019, this study analyzes the results of probit regressions on dependent variables for governance (participation, transparency, public services, communication, and fairness) and quality-of-life (buildings, energy-environment, mobility-transportation, education, and health). Findings show more optimism about the impact of smart cities on quality-of-life than on governance. Awareness about the smart city concept associates positively with expectations about smart city benefits, but the effect is sensitive to education level and income. This study deepens understandings about the political legitimacy of smart cities, at a time when urban governments are accelerating investments in related technologies. More broadly, it adds contextual nuance to research about state-society relations and, at a practical level, supports policy recommendations to strengthen information and awareness campaigns, better articulate smart city benefits, and openly acknowledge limitations.

Digital transformation: A meta-review and guidelines for future research

The emergence of digital transformation has changed the business landscape for the foreseeable future. As scholars advance their understanding and digital transformation begins to gain maturity, it becomes necessary to develop a synthesis to create solid foundations. To do so, significant steps need to be taken to critically, rigorously, and transparently examine the existing literature. Therefore, this article uses a meta-review with the support of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol. As a result, we identified six dimensions and seventeen categories related to digital transformation. The organizational, technological, and social dimensions are still pivotal in digital transformation, while two new dimensions (sustainability and smart cities) still need to be explored in the existing literature. The need to deepen knowledge in digital transformation and refine the dimensions found is of paramount importance, as it involves some complexity due to organizational dynamics and the development of new technologies. It was also possible to identify opportunities, challenges, and future directions.

A Real-World Scenario of Citizens' Motivation and Engagement in Urban Waste Management Through a Mobile Application and Smart City Technology

Circular bioeconomy is a key socioeconomic model for advancing the United Nations Global Sustainability Goals and promoting environmental and resource sustainability. However, circular bioeconomy concepts are unknown to most people and politicians worldwide who still have a fragmented picture of sustainability. Common perception of waste needs a cultural shift from "disposable" to commodity. This can happen with effective communication, active citizens' education, and awareness and engagement in core bioeconomy experiences and activities, like urban waste management and environmental sustainability. Citizen engagement methodologies are multiple. This paper proposes the combined use of Information and Communication Technologies (ICTs), citizens' hands-on project involvement, and a direct rewarding system. Similar European examples are displayed, while our key case study is the bitter orange waste problem in the metropolitan region of Attica in Greece, where approximately 40,000 tons of bitter oranges per year remain unmanageable and unexploited, causing serious problems. The Bitter Orange Project aims to educate citizens on bioeconomy and biomass value, hopefully changing the perception of urban waste through their rewarded engagement in fruit collection to produce high added value materials. This can be a versatile platform for urban waste management projects through citizen science regardless of the type of biomass. The project aims to engage all possible local society stakeholders to multiply awareness. The target of this paper is to highlight that environmental problems related to biomass misuse are closer than the average citizen experiences, and that active involvement of society through rewarding can help raise awareness.

Driver behavior indices from large-scale fleet telematics data as surrogate safety measures

Large-scale telematics data enable a high-resolution inference of road network's safety conditions and driver behavior. Although many researchers have investigated how to define meaningful safety surrogates and crash predictors from telematics, no comprehensive study analyzes the driver behavior derived from large-scale telematics data and relates them to crash data and the road networks in metropolitan cities. This study extracts driver behavior indices (e.g., speed, speed variation, hard braking rate, and hard acceleration rate) from large-scale telematics data, collected from 4000 vehicles in New York City five boroughs. These indices are compared to collision frequencies and collision rates at the street level. Moderate correlations were found between the safety surrogate measures and collision rates, summarized as follows: (i) When normalizing crash frequencies with traffic volume, using a traffic AADT model, safety-critical regions almost remain the same. (ii) The correlation magnitude of hard braking and hard acceleration varies by road types: hard braking clusters are more indicative of higher collision rates on highways, whereas hard acceleration is a stronger hazard indicator on non-highway urban roads. (iii) Locations with higher travel times coincide with locations of high crash incidence on non-highway roads. (iv) However, speeding on highways is indicative of collision risks. After establishing the spatial correlation between the driver behavior indices and crash data, two prototype safety metrics are proposed: speed corridor maps and hard braking and hard acceleration hot-spots. Overall, this paper shows that data-driven network screening enabled by telematics has great potential to advance our understanding of road safety assessment.

Advancing residents' use of shared spaces in Nordic superblocks with intelligent technologies

To support the sustainability of future cities, residents' living spaces need to be built and used efficiently, while supporting residents' communal wellbeing. Nordic superblock is a new planning, housing, and living concept in which residents of a neighborhood-a combination of city blocks-share yards, common spaces and utilities. Sharing living spaces is an essential element of this approach. In this study, our goal was to study the ways in which intelligent technology solutions-such as proactive, data-driven Artificial Intelligence (AI) applications-could support and even motivate the use of common areas in superblocks. To this end, we conducted a two-phase qualitative study: in the first phase, potential superblock residents (N = 12) shared their perspectives of sharing of living spaces in general, and more specifically of how intelligent technologies could support sharing spaces. In the second phase, two workshops with experts (N = 7) were held to gather understanding of possibilities of intelligent technologies in meeting the residents' expectations of space sharing. The results illustrate space sharing and communality as supportive factors for one another, enabled but also complicated by social interaction. Major possibilities for intelligent technologies to advance space sharing were seen in organizing the use of spaces and facilitating social interaction in the community. As an outcome, four roles incorporating several use purposes of intelligent technologies were found. The findings can inform the Human-Centered AI (HCAI) research and design improving sustainable living in future urban neighborhoods.

Smart cities: reviewing the debate about their ethical implications

This paper considers a host of definitions and labels attached to the concept of smart cities to identify four dimensions that ground a review of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic element for the future. Given the persisting disagreements around the definition of a smart city, the article identifies in these four dimensions a more stable reference framework within which ethical concerns can be clustered and discussed. Identifying these dimensions makes possible a review of the ethical implications of smart cities that is transversal to their different types and resilient towards the unsettled debate over their definition.

Prospective for urban informatics

The specialization of different urban sectors, theories, and technologies and their confluence in city development have led to a greatly accelerated growth in urban informatics, the transdisciplinary field for understanding and developing the city through new information technologies. While this young and highly promising field has attracted multiple reviews of its advances and outlook for its future, it would be instructive to probe further into the research initiatives of this rapidly evolving field, to provide reference to the development of not only urban informatics, but moreover the future of cities as a whole. This article thus presents a collection of research initiatives for urban informatics, based on the reviews of the state of the art in this field. The initiatives cover three levels, namely the future of urban science; core enabling technologies including geospatial artificial intelligence, high-definition mapping, quantum computing, artificial intelligence and the internet of things (AIoT), digital twins, explainable artificial intelligence, distributed machine learning, privacy-preserving deep learning, and applications in urban design and planning, transport, location-based services, and the metaverse, together with a discussion of algorithmic and data-driven approaches. The article concludes with hopes for the future development of urban informatics and focusses on the balance between our ever-increasing reliance on technology and important societal concerns.

Coverage path planning for spraying drones

The pandemic by COVID-19 is causing a devastating effect on the health of the global population. Currently, there are several efforts to prevent the spread of the virus. Among those efforts, cleaning and disinfecting public areas have become important tasks and they should be automated in future smart cities. To contribute in this direction, this paper proposes a coverage path planning method for a spraying drone, an unmanned aerial vehicle that has mounted a sprayer/sprinkler system, that can disinfect areas. State-of-the-art planners consider a camera instead of a sprinkler, in consequence, the expected coverage will differ in running time because the liquid dispersion is different from a camera's projection model. In addition, current planners assume that the vehicles can fly outside the target region; this assumption can not be satisfied in our problem, because disinfections are performed at low altitudes. Our method presents i) a new sprayer/sprinkler model that fits a more realistic coverage volume to the drop dispersion and ii) a planning method that efficiently restricts the flight to the region of interest avoiding potential collisions in bounded scenes. The algorithm has been tested in several simulation scenes, showing that it is effective and covers more areas with respect to two approaches in the literature. Note that the proposal is not limited to disinfection applications, but can be applied to other ones, such as painting or precision agriculture.

Using ANPR data to create an anonymized linked open dataset on urban bustle

ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article's key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making.

Disruptive technologies as a solution for disaster risk management: A review

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Questionnaire dataset: The Greek Smart Cities - Municipalities dataset

The dataset was formulated through field research in the Greek Municipalities. The survey was undertaken with an online questionnaire (available in Greek) and the contact person was the Chief Digital Officer or a person with similar responsibilities. The final sample is 252 out of a total of 325 Municipalities. Each respondent had to answer to 26 questions, including sub-questions, of different types (1) 5-point Likert scale, (2) rating scale, (3) closed type "Yes/no", (4) open-ended and (5) dropdown, and, depending on them, there are 3 types of variables (1) ordinal, (2) nominal, and (3) scale variables. The concepts included in the questionnaire are a) the level of integration and use of digital technologies, b) the difficulties and the challenges municipality authorities face when trying to implement a smart strategy and c) the level of diverse collaborations and partnerships which are necessary in order to develop a strategy. The data can be used to analyse the strategic capabilities of the Greek Municipalities under the three concepts evaluated in the questionnaire. The data's primary beneficiaries include researchers, public authorities, digital platforms entrepreneurs, smart city specialists and smart city entrepreneurs, as they can use the dataset to develop models against already known results, to identify factors important in an urban strategy and to further study the correlations between the different factors, design and draft an urban strategy on real data.

Programming Nature as Infrastructure in the Smart Forest City

Smart cities typically involve the digitalization of transport and buildings, energy and communications. Yet urban natures are also becoming increasingly digitalized, whether through processes of monitoring, automation, mitigation, or augmentation. This text considers what "splintering urbanisms" materialize through programming nature as infrastructure. By focusing specifically on smart urban forests, I suggest that the management logics of smart infrastructures attempt to program and transform vegetation and its ecologies into uniquely efficient and responsive urban organisms. In the process, these programs of efficiency have the potential to exacerbate extractive economies and social inequalities that amplify and materialize through the "Internet of nature."

IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health

Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other's output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework

The complex and interdependent nature of smart cities raises significant political, technical, and socioeconomic challenges for designers, integrators and organisations involved in administrating these new entities. An increasing number of studies focus on the security, privacy and risks within smart cities, highlighting the threats relating to information security and challenges for smart city infrastructure in the management and processing of personal data. This study analyses many of these challenges, offers a valuable synthesis of the relevant key literature, and develops a smart city interaction framework. The study is organised around a number of key themes within smart cities research: privacy and security of mobile devices and services; smart city infrastructure, power systems, healthcare, frameworks, algorithms and protocols to improve security and privacy, operational threats for smart cities, use and adoption of smart services by citizens, use of blockchain and use of social media. This comprehensive review provides a useful perspective on many of the key issues and offers key direction for future studies. The findings of this study can provide an informative research framework and reference point for academics and practitioners.

Last-mile-as-a-service (LMaaS): An innovative concept for the disruption of the supply chain

Recent events such as Covid-19 vaccine distribution issues and the blockage of the Ever Given ship in the Suez Canal raised concerns about how fragile the traditional supply chain is. Last-mile personalized fulfillment can have a catalyst role in the proliferation of the Industry 4.0. This growing trend will reduce standard production, bringing manufacturing closer to the client and, ultimately, boiling down the supply chain to the last mile. However, the literature is not clear about the breakdown of the supply chain to enhance cities' sustainability and reducing the number of transports and circulating vehicles. Stemming from an empirical study to simulate the existing gap in the market and the development of a case study through structured interviews with privileged interlocutors complemented by the document analysis, this paper highlights how the integration of local stakeholders can efficiently enhance a personalized service based on dynamic collaborations to set up the supply chain, by introducing the Last-Mile-as-a-Service (LMaaS) concept. This concept relies on a revenue-sharing framework based on an open marketplace composed by last-mile manufacturing, transport, and storage assets and stakeholders to disrupt the supply chain, enabling any company to provide personalized products in almost real-time to any location.

Fantasy visions, informal urbanization, and local conflict: an evolutionary perspective on smart city governance in India

Smart city imaginaries have emerged in southern cities driven by neoliberal logics in the urban space. Scholarly work in India has continued to engage with sweeping accounts of cities as opposed to detailed empirical studies of local projects. This paper attempts to address this gap through an in-depth ethnographic inquiry of a slum redevelopment project in the city of Bhubaneswar, India. The key objective is to understand the ways in which informal residents adapted to and changed smart city policies in India in recent years. Using an evolutionary lens, and drawing on participant observation; document analysis; and semi-structured interviews, the paper puts forth a descriptive cases that advances the notion that smart cities imaginaries have resulted in abrupt changes in the institutional context while getting entangled itself within the legal system. The paper also demonstrates how smart cities discourses counter-intuitively result in emergent spaces of resistance in the form of counter-hegemonic practices, thus allowing spaces for the evolution of new actors and imaginaries from unfamiliar territories. The paper concludes by discussing that city planning and governance pathways in India risk creating complicated path dependencies and rigid governance future pathways that may amplify conflict.

On the development of flexible mobile multi-sensor units based on open-source hardware platforms and a reference framework

For some IoT applications, mobile entities are considered as the main source of sensed data, requiring the attachment of sensor modules on them. The endowing of sensing capabilities to such mobile entities can be performed in different ways, but the adoption of a reference hardware framework can bring a series of advantages, specially in dynamic complex scenarios. This article exploits the MSensorMob2 multi-sensor hardware framework for monitoring in areas with disconnection periods, comprising sensing, transmission and reconfiguration functions. Comprehensive analyses on multiple open-source hardware platforms are conducted, assessing their costs, deployment constraints and performance issues when implementing this development framework.

Tourism research after the COVID-19 outbreak: Insights for more sustainable, local and smart cities

This paper presents the results of a bibliometric analysis of academic research dealing with COVID-19 in the area of city destination development from 1 December 2019 to 31 March 2021. Particularly, by means of SciMAT software, it identifies, quantifies, and visually displays the main research clusters, thematic structure and emerging trends that city and tourism planners will face in the new normal. The search revealed that social media and smart tourism are the themes with the greatest potential; sustainable cities, local destination development, changes in tourist behavior, and tourists' risk perception are underdeveloped streams with enormous relevance and growth in the new normal. Research on the effects of COVID-19 on citizen health and its economic impact on the tourism industry and cities are intersectional and highly developed topics, although of little relevance. The current study also identifies the challenges of destination research for planners and proposes future research directions. Consequently, this paper contributes to the existing literature on COVID-19 and sustainable cities, as it develops a critical examination of the extant research and points out the research gaps that must be filled by future studies.

Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic

The spread of Covid-19 profoundly changed citizens' daily lives due to the introduction of new modes of work and access to services based on smart technologies. Although the relevance of new technologies as strategic levers for crisis resolution has been widely debated before the pandemic, especially in the smart cities' context, how individuals have agreed to include the technological changes dictated by the pandemic in their daily interactions remains an open question. This paper aims at detecting citizens' sentiment toward technology before and after the emergence of the Covid-19 pandemic using Fuzzy Formal Concept Analysis (FFCA) to analyze a large corpus of tweets. Specifically, citizens' attitudes in five cities (Berlin, Dublin, London, Milan, and Madrid) were explored to extract and classify the key topics related to the degree of confidence, familiarity and approval of new technologies. The results shed light on the complex technology acceptance process and help managers identify the potential negative effects of smart technologies. In this way, the study enhances scholars' and practitioners' understanding of the strategies for enabling the use of technology within smart cities to manage the transformations introduced by the health emergency and guide citizens’ behaviour.

Public health in times of crisis: An overlooked variable in city management theories?

The volume of research that associates the theme of city management with crises resulting from emerging infectious disease is modest, even after the occurrences of Ebola and Severe Acute Respiratory Syndrome. Similarly, the Coronavirus disease (COVID-19) pandemic has thus far contributed only modestly to the expansion of attention to people's health, through city management, in times of crisis. This study, by means of a systematic literature review, analyzes the gap in research on urban theory on how epidemics are confronted. The term "cities" had 2,440,607 articles published and were identified 665 that presents the combination of the term "pandemic". After the development of content analysis were identified 11 articles prior to 2019 and 10 articles published between January and June 2020, adhering to the objective of this investigation. Prior to 2019 studies addressed topics related to the construction of an urban structure aimed at reducing people's vulnerability to infectious diseases, starting in 2020, the focus of researchers' attention is on the use of information and communication technologies used as tools for prevention and control. Theories of the management of cities indicate the need to extrapolate the urban perimeter, incorporating the relations of dependence in cities with the other actors within the surroundings, especially in times of crisis. Studies have emphasized that cities are not isolated islands; rather, they are parts of a complex system with multiple exchanges. This thematic field of study enhances research that presents urban planning solutions by using data-driven management to consider conduct, parameters, and protocols relating to public health in moments of crisis.

MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0

Air, water, and soil are essential for terrestrial life, but pollution, overexploitation, and climate change jeopardize the availability of these primary resources. Thus, assuring human health and food production requires efficient strategies and technologies for environmental protection. Knowing key parameters such as soil moisture, air, and water quality is essential for smart farming and urban development. The MeteoMex project aims to build simple hardware kits and their integration into current Internet-of-Things (IoT) platforms. This paper shows the use of low-end Wemos D1 mini boards to connect environmental sensors to the open-source platform ThingsBoard. Two printed circuit boards (PCB) were designed for mounting components. Analog, digital and I²C sensors are supported. The Wemos ESP8266 microchip provides WiFi capability and can be programed with the Arduino IDE. Application examples for the MeteoMex aeria and terra kits demonstrate their functionality for air quality, soil, and climate monitoring. Further, a prototype for monitoring wastewater treatment is shown, which exemplifies the capabilities of the Wemos board for signal processing. The data are stored in a PostgreSQL database, which enables data mining. The MeteoMex IoT system is highly scalable and of low cost, which makes it suitable for deployment in agriculture 4.0, industries, and public areas. Circuit drawings, PCB layouts, and code examples are free to download from https://github.com/robert-winkler/MeteoMex.

Investigating response time and accuracy in online classifier learning for multimedia publish-subscribe systems

The enormous growth of multimedia content in the field of the Internet of Things (IoT) leads to the challenge of processing multimedia streams in real-time. Event-based systems are constructed to process event streams. They cannot natively consume multimedia event types produced by the Internet of Multimedia Things (IoMT) generated data to answer multimedia-based user subscriptions. Machine learning-based techniques have enabled rapid progress in solving real-world problems and need to be optimised for the low response time of the multimedia event processing paradigm. In this paper, we describe a classifier construction approach for the training of online classifiers, that can handle dynamic subscriptions with low response time and provide reasonable accuracy for the multimedia event processing. We find that the current object detection methods can be configured dynamically for the construction of classifiers in real-time, by tuning hyperparameters even when training from scratch. Our experiments demonstrate that deep neural network-based object detection models, with hyperparameter tuning, can improve the performance within less training time for the answering of previously unknown user subscriptions. The results from this study show that the proposed online classifier training based model can achieve accuracy of 79.00% with 15-min of training and 84.28% with 1-hour training from scratch on a single GPU for the processing of multimedia events.

Enhancing shopping experiences in smart retailing

The retailing market has undergone a paradigm-shift in the last decades, departing from its traditional form of shopping in brick-and-mortar stores towards online shopping and the establishment of shopping malls. As a result, "small" independent retailers operating in urban environments have suffered a substantial reduction of their turnover. This situation could be presumably reversed if retailers were to establish business "alliances" targeting economies of scale and engage themselves in providing innovative digital services. The SMARTBUY ecosystem realizes the concept of a "distributed shopping mall", which allows retailers to join forces and unite in a large commercial coalition that generates added value for both retailers and customers. Along this line, the SMARTBUY ecosystem offers several novel features: (i) inventory management of centralized products and services, (ii) geo-located marketing of products and services, (iii) location-based search for products offered by neighboring retailers, and (iv) personalized recommendations for purchasing products derived by an innovative recommendation system. SMARTBUY materializes a blended retailing paradigm which combines the benefits of online shopping with the attractiveness of traditional shopping in brick-and-mortar stores. This article provides an overview of the main architectural components and functional aspects of the SMARTBUY ecosystem. Then, it reports the main findings derived from a 12 months-long pilot execution of SMARTBUY across four European cities and discusses the key technology acceptance factors when deploying alike business alliances.

A Backdrop Case Study of AI-Drones in Indian Demographic Characteristics Emphasizing the Role of AI in Global Cities Digitalization

Urbanization of global populations with augmented and convenient living standards of people are driving towards techno-enabled and sustainable smart cities in the future. With this, technology plays a key role in making the existing cities smart and intelligent in a way that the citizens are being served better and safer. Over the past 1-decade, the application of Artificial Intelligence (AI) in different sectors like Environment, Education, Healthcare, etc. is well-supporting the idea of Global Digitalization and Smart Cities. In this paper, we highlight and discuss the multiple sectors where the AI approach is expected to grow to make a Global Smart City. Further, the paper contributes to presenting the AI approach for urban and rural India using AI-enabled drones. For Urban India, we discuss how and where the AI can be used to make urban India smart and sustainable. Lastly, the paper contributes to exposing the challenges faced by rural India and giving a wholesome approach to integrating AI into different sectors for rural enhancement and upliftment.

The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management

Since the early days of the COVID-19 crisis the scientific community has constantly been striving to shed light on various issues such as the mechanisms driving the spread of the virus, its environmental and socio-economic impacts, and necessary recovery and adaptation plans and policies. Given the high concentration of population and economic activities in cities, they are often hotspots of COVID-19 infections. Accordingly, many researchers are struggling to explore the dynamics of the pandemic in urban areas to understand impacts of COVID-19 on cities. In this study we seek to provide an overview of COVID-19 research related to cities by reviewing literature published during the first eight months after the first confirmed cases were reported in Wuhan, China. The main aims are to understand impacts of the pandemic on cities and to highlight major lessons that can be learned for post-COVID urban planning and design. Results show that, in terms of thematic focus, early research on the impacts of COVID-19 on cities is mainly related to four major themes, namely, (1) environmental quality, (2) socio-economic impacts, (3) management and governance, and (4) transportation and urban design. While this indicates a diverse research agenda, the first theme that covers issues related to air quality, meteorological parameters, and water quality is dominant, and the others are still relatively underexplored. Improvements in air and water quality in cities during lockdown periods highlight the significant environmental impacts of anthropogenic activities and provide a wake-up call to adopt environmentally friendly development pathways. The paper also provides other recommendations related to the socio-economic factors, urban management and governance, and transportation and urban design that can be used for post-COVID urban planning and design. Overall, existing knowledge shows that the COVID-19 crisis entails an excellent opportunity for planners and policy makers to take transformative actions towards creating cities that are more just, resilient, and sustainable.

Is there enough trust for the smart city? exploring acceptance for use of mobile phone data in oslo and tallinn

There are high hopes that a development towards smarter urban environments, backed up by various big data sources, can help solve many of the challenges facing today's large cities related to providing security, mitigating environmental damages, improving services and upscaling innovative and entrepreneurial activities. This study explores the acceptance of use of mobile phone data (MPD) in different areas, and how it is related to different types of trust. Based on a representative survey of citizens in the two smart cities, Oslo and Tallinn, four similar trust cultures are located. The acceptance of use of MPD differed significantly between the trust cultures and, as expected, was significantly stronger in groups with higher levels of trust, either generally or in terms of reliance on technologies. The acceptance of use of MPD for commercial product development was low for all groups. Findings suggest that future users of MPD need to be aware of the significant scepticism toward and rejection of the use of such data in large parts of the population. Unless visions of the smart city are grounded in the needs and wants of citizens, such plans are not likely to succeed, and negative understandings and images of a panoptic state may take stronger hold. As for now, however, there seems to be insufficient social trust to exploit this on a wider scale without creating even more scepticism and distrust.

Smart urban governance: an alternative to technocratic "smartness"

This paper argues for a specific urban planning perspective on smart governance that we call "smart urban governance," which represents a move away from the technocratic way of governing cities often found in smart cities. A framework on smart urban governance is proposed on the basis of three intertwined key components, namely spatial, institutional, and technological components. To test the applicability of the framework, we conducted an international questionnaire survey on smart city projects. We then identified and discursively analyzed two smart city projects-Smart Nation Singapore and Helsinki Smart City-to illustrate how this framework works in practice. The questionnaire survey revealed that smart urban governance varies remarkably: As urban issues differ in different contexts, the governance modes and relevant ICT functionalities applied also differ considerably. Moreover, the case analysis indicates that a focus on substantive urban challenges helps to define appropriate modes of governance and develop dedicated technologies that can contribute to solving specific smart city challenges. The analyses of both cases highlight the importance of context (cultural, political, economic, etc.) in analyzing interactions between the components. In this, smart urban governance promotes a sociotechnical way of governing cities in the "smart" era by starting with the urban issue at stake, promoting demand-driven governance modes, and shaping technological intelligence more socially, given the specific context.

REST framework: A modelling approach towards cooling energy stress mitigation plans for future cities in warming Global South

Future cities of the Global South will not only rapidly urbanise but will also get warmer from climate change and urbanisation induced effects. It will trigger a multi-fold increase in cooling demand, especially at a residential level, mitigation to which remains a policy and research gap. This study forwards a novel residential energy stress mitigation framework called REST to estimate warming climate-induced energy stress in residential buildings using a GIS-driven urban heat island and energy modelling approach. REST further estimates rooftop solar potential to enable solar photo-voltaic (PV) based decentralised energy solutions and establish an optimised routine for peer-to-peer energy sharing at a neighbourhood scale. The optimised network is classified through a decision tree algorithm to derive sustainability rules for mitigating energy stress at an urban planning scale. These sustainability rules established distributive energy justice variables in urban planning context. The REST framework is applied as a proof-of-concept on a future smart city of India, named Amaravati. Results show that cooling energy stress can be reduced by 80 % in the study area through sensitive use of planning variables like Floor Space Index (FSI) and built-up density. It has crucial policy implications towards the design and implementation of a national level cooling action plans in the future cities of the Global South to meet the UN-SDG - 7 (clean and affordable energy) and SDG - 11 (sustainable cities and communities) targets.

Smart rural futures: Will rural areas be left behind in the 4th industrial revolution?

The 4th Industrial Revolution (4IR) is the term given to encompass a range of technological developments that many argue will fundamentally change society, much in the same way that electricity and digital technology did during previous industrial revolutions. This paper argues that current debates around 4IR are centred on the urban core, with rural areas being relegated to the peripherality and the remainder. The paper therefore examines these technologies from a rural perspective and considers what impact they could have in rural areas, both positive and negative. The analysis shows that the impacts of 4IR technologies could be just as important in rural as in urban places. Drawing on extant theories of rural development, the paper examines the physical and cultural barriers facing rural areas when attempting to engage with 4IR. The paper concludes by proposing that rural theorists engage with smart urban theoretical debates. New research should seek to understand the multi-faceted aspects of 4IR in rural regions, and to support the transition to smart rural futures.

Social media mining for smart cities and smart villages research

The imperative of well-being and improved quality of life in smart cities context can only be attained if the smart services, so central to the concept of smart cities, correspond with the needs, expectations and skills of cities’ inhabitants. Considering that social media generate and/or open real-time entry points to vast amounts of data pertinent to well-being and quality of life, such as citizens’ expectations, opinions, as well as to recent developments related to regulatory frameworks, debates, political decisions and policymaking, the big question is how to exploit the potential inherent in social media and use it to enhance the value added smart cities generate. Social mining is traditionally understood as the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. In the context of smart cities, this special issue focuses on how social media data, also potentially combined with other data, can be used to optimize the efficiency of city operations and services, and thereby contribute more efficiently to citizens’ well-being and quality of life.

Urban biocycles - Closing metabolic loops for resilient and regenerative ecosystem: A perspective

Cities are at crossroads, confronting challenges posed by increasing population growth, climate change and faltering livability. These problems are prompting urban areas to chart novel path towards adopting sustainable production/consumption strategies. The alluring concept of circular economy (CE) that focuses on reuse and recycling of materials in technical and biological cycles to reduce waste generation is a critical intervention. Present article aims on precisely highlighting the importance of biogenic materials which have an immense potential to be transformed into a source of value in an urban ecosystem. It also sets out to explore the scope of implementing 'urban biocycles' that strategically directs the flow of resources, their use, extracting value in the form of nutrients, energy and materials post consumption within an urban metabolic regime. The concepts discussed contribute to biocycle economy by outlining emerging requirements, identification of common strategies, policies and emerging areas of research in line with sustainable development goals.