Building Smart Cities Research Articles

A Tale of Smart Cities: How Collaboration Between Utilities and Communities Is Essential to Building Smart Cities

Cities are critically dependent on energy and the ability to manage environmental challenges. Emerging technology and design concepts provide new opportunities for meeting the needs of residents while contributing to major improvements in local and global environments. Cities are where grid infrastructure and resilience goals meet. Across the world, policymakers at every level are developing and executing plans to meet decarbonization and resilience goals for their residents. As about half of the world’s population is now urban, city leaders are looking for best practices on how to support the increased deployment of clean energy generation, including solar photovoltaics (PVs), while also integrating beneficial electrification to mitigate the effects of climate change.

The Use of Artificial Intelligence for Smart Decision-Making in Smart Cities: A Moderated Mediated Model of Technology Anxiety and Internal Threats of IoT

With the rapid development of artificial intelligence (AI), AI for smart decision-making is attracting a lot of attention, but research on this topic in smart cities is not yet comprehensive. Thus, the current research aimed to examine the direct and indirect (via technology anxiety) relationships between artificial intelligence (AI), technology anxiety, and smart decision-making (SDM). The research article also examines the moderating role of internal threats of IoT on AI and technology anxiety and the relationship between AI and smart decision-making. 614 cross-sectional data gathered from participants from public and private sectors in Turkey were utilized to investigate the aforementioned relationships. The results indicated that AI had a positive influence on smart decision-making. AI contributes negatively to technology anxiety. Technology anxiety has a negative effect on smart decision-making. Technology anxiety partially mediated the direct effect of AI on smart decision-making. The results revealed that internal threats of IoT moderated the negative relationship between AI and artificial intelligence, such that the negative relationship is further strengthened when internal threats of IoT are high. The results also indicated that internal threats of IoT moderated the positive direct relationship between AI and smart decision-making, such that the positive relationship is weakened when internal threats of IoT are high. The findings present crucial practical implications for government and local authorities in building smart cities.

Study on workflow of acquiring, processing, and classifying LiDAR point cloud for establishment of 3D city models

Along with the development of mapping technologies, the data acquisition system through the aerial laser scanning system has created a huge LiDAR point cloud data source. This is an essential and detailed data source that effectively serves various fields such as making maps and 3D maps, etc. The process of building 3D city models requires processing many types of data, at which point cloud data processing and classification play an essential role in creating an input data source for the model. Study on workflow for acquiring, processing, and classifying point cloud data is meaningful. It plays a vital role in the development and application of LiDAR technique in the current period, especially in building smart cities. The article introduces a method for acquiring, processing, and classifying LiDAR point cloud data to establish 3D city models. Based on the proposed approach, a pilot 3D model of the city in Thuong Ly ward, Hong Bang district, Hai Phong city was generated. The result of point cloud classification’s accuracy is very good, ranging from 92% to 99% depend on type of object. Based on the classification, the city’s 3D model is established with 2nd Level of Detail (LOD2) for Thuong Ly ward. The model is very useful in urban planning and management, and in the number of managing task such as infrastructure development, rescues and disaster prevention, military services and real estate business.

Crowdsourcing as an Open Innovation Strategy in Knowledge-Based Smart City Management

A smart city is defined as an area that offers numerous opportunities for learning and innovation, relies on the creative potential of its inhabitants and institutions, and employs digital infrastructure for communicating and managing knowledge. An important related research direction to be considered is knowledge management for smart cities and urban innovation. The idea in this area is to collect and share knowledge in order to arrive at a collaborative approach towards the development of future urban environments. Open innovation is a new paradigm for building smart cities, where governments and software developers take advantage of the experience, skills, and knowledge of citizens to develop digital services that are relevant to users of the urban environment. Citizen involvement is a key to the successful development of cities, but empirical research into the nature and characteristics of this involvement remains scarce. The objective of this study is to explore the potential of open innovation strategies in knowledge-based smart city management and the role of the municipality herein. A limited set of crowdsourcing initiatives has been introduced to illustrate the open innovation strategies. This paper will tackle a part of this problem by adding to the understanding of generating, evaluating and selecting innovative ideas for smart city innovation by means of online crowdsourcing. The case studies research presented in this paper generates some empirical evidence with regards to the usage of crowdsourcing through an online platform in the context of trying to establish a smarter city. The study offers recommendations arising from the empirical research. An improved approach should consider citizens as active actors within the development process of smart cities. Citizens can collaborate in co-creating smart cities together with the private sector, governments, academia and knowledge institutes. One important element however still needs to be researched more in depth is user motivation. It remains an interesting research topic for future studies to investigate the optimal motivation for citizens in order to get them to share their knowledge.

Effective Public Administration as a Tool for Building Smart Cities: The Experience of the Slovak Republic

This study focuses on examining the requirements forming the concept of the right to effective administration in relation to the communication of local governments with their residents. We pay attention to the electronization of public administration and the ambition to strengthen it through public participation in decision making concerning important matters of self-government, as the implementation of effective public administration is linked to the right management approach. In the first part of the study, we analyze European jurisprudence and legislation, and we further address the question of how the European concept of local government influences the communication of local authorities with their inhabitants in relation to the realization of the means of direct democracy. We focus our attention on the current state of the electronization of public administration and its development, including cyber security. The main goal of this study is to use critical analysis to assess the legal regulation of the activities of the Slovak public administration. In addition to the main goal, we also have several sub-goals, such as making a comparison of the development of the electronization of public administration in the countries of the former Czechoslovakia. Especially with the use of critical analysis and other scientific methods of investigation, we look for and find answers to selected application problems from practice. We also use scientific and doctrinal interpretation as well as scientific literature and jurisprudence. As a result of our study, recommendations are made to ensure the more efficient functioning of smart cities in the Slovak Republic.

Estimation of building height using a single street view image via deep neural networks

Building smart cities requires three-dimensional (3D) modelling to facilitate the planning and management of built environments. This requirement leads to high demand for data on vertical dimensions, such as building height, that are critical for the construction of 3D city models. Despite increasing recognition of the importance of such data, their acquisition in a low-cost and efficient manner remains a daunting task. Big data, particularly street view images (SVIs), provide an opportunity to efficiently solve this problem. In this study, we aim to derive information on building height from openly available SVIs by using single view metrology. Unlike other methods using multisource inputs, our method capitalizes on deep neural networks to extract a set of features – such as vanishing points, line segments, and semantic segmentation maps – for single view measurement and then estimates the height from single SVIs. The minimal input required by the method increases its competitiveness in large-scale estimation of building heights, especially in areas with difficulty to obtain the conventional remote sensing data. In addition to experiments that demonstrate the effectiveness and efficiency of the proposed method, we also conduct a thorough analysis of uncertainties and errors brought by the method, thereby providing guidance for its future applications.1Source code: https://github.com/yzre/SIHE.

Energy consumption prediction of air-conditioning systems in eco-buildings using hunger games search optimization-based artificial neural network model

The energy consumption of the heating, ventilation, and air-conditioning systems is a big concern of energy-efficient buildings in smart cities, especially for cooling and heating loads. Although many constant efforts have been adopted to improve the air-conditioning systems' performance; yet, the energy consumption for these systems is still high. This study is, therefore, to propose a novel soft computing model for accurately calculating the energy consumption of the cooling load and heating load systems in energy-efficient buildings of smart cities, namely hunger games search-based multiple layers perceptron neural network model. This model can assist energy-efficient buildings in improving the performance of the air-conditioning systems and sustainable environmental protection. Accordingly, the novel hunger games search optimization algorithm optimized a multiple layers perceptron neural network to decrease the model's error in estimating cooling and heating load. Three other benchmark intelligent models, such as Harris hawks optimization-based multiple layers perceptron neural network, particle swarm optimization-based multiple layers perceptron neural network, and grey wolf optimization-based multiple layers perceptron neural network, were also developed for similar purposes, and their accuracies were then considered as the indicators for evaluating the proposed hunger games search-based multiple layers perceptron neural network model. The results indicated that the proposed soft computing paradigm is the most robust model for estimating the air-conditioning systems' cooling load and heating load in this study. A critical report was given in this study based on the evidence regarding the unnecessaryness of the relative compactness and glazing area distribution variables in calculating the energy consumption of future buildings. They may lead to lower accuracy of predictive models for predicting the energy consumption of future buildings.

Detection of false data injection attacks leading to line congestions using Neural networks

The connection between smart grids and buildings in smart cities can align energy supply and demand more efficiently with real-time communication. Because of the deep interactions between the cyber and physical systems, the detection of cyberattacks targeting the smart grid has become a challenge in recent years. False data injection (FDI) attacks can bypass the bad data detection algorithm to overflow multiple transmission lines and eventually cause cascading failures or blackouts. In this paper, a simplified neural network is developed to detect FDI attacks targeting transmission line overflows. Compared with the state-of-the-art that mainly focused on stealthy attacks bypassing state-estimation, the novelty of the proposed approach is detecting stealthy attacks that not only bypass the state-estimation, but also result in congestion of transmission lines in smart grids. A bad dataset, created by an attack model, is mixed with a set of clean data to train the proposed detection framework. The numerical results demonstrate high accuracy of this method in detecting cyber–physical attacks. Also, several case studies are included to test the resilience of the proposed method in various scenarios.

Global Megatrend of the Fourth Industrial Revolution in the Digital Economy: How to Realize It with Smart Cities as a Practical Measure?

The concept of the smart city represents the highest level of the Fourth Industrial Revolution (FIR) along with smartphones, smart homes, and smart factories. Therefore, most governments around the world have tried to build smart cities in order to strengthen their urban competitiveness and improve the quality of life for their citizens in the digital economy. North America, the European Union (EU), and Asia have already carried out several pilot projects to build smart cities based on private-led, public-private partnerships, and public-led strategies, respectively. Smart cities can improve overall problems and resolve difficulties by 10–30% on average, which is regarded as an overall benefit of smart cities. At the same time, they can contribute to labour force disruptions, digital discrepancies, and threats to social coherence and inclusiveness, all of which result in socio-economic and political costs. The author examines the roles smart cities can play in the digital economy and in the completion of the FIR and focuses on whether smart cities can contribute to the creation of new opportunities for global economic growth as a new industry in the digital economy. Finally, the author examines the challenges of transforming digitalization and smartness in reality and highlights future perspectives for the FIR in practical terms.

State-of-the-Art Strategies and Research Challenges in Wireless Communication for Building Smart Systems

The upcoming 6G communication network will revolutionize the scenario of customer services and related applications by building smart, autonomous systems. We first discuss here about 6G enabled smart applications like healthcare, smart city building, industrial IoTs, etc. We next provide a brief survey of the enabling technologies for achieving the required goals of 6G, such as terahertz communication, cell-free communication, holographic communication using beamforming, wireless power transfer, ultra-low latency communication, etc., with a view to tackle very large amounts of data traffic from several billions of smart interconnected devices while supporting ultra-low end-to-end communication latency. Finally, we present various research challenges involved in 6G communication towards building such smart systems.

Demand-aware mobile bike-sharing service using collaborative computing and information fusion in 5G IoT environment

Mobile bike-sharing services have been prevalently used in many cities as an important urban commuting service and a promising way to build smart cities, especially in the new era of 5G and Internet-of-Things (IoT) environments. A mobile bike-sharing service makes commuting convenient for people and imparts new vitality to urban transportation systems. In the real world, the problems of no docks or no bikes at bike-sharing stations often arise because of several inevitable reasons such as the uncertainty of bike usage. In addition to pure manual rebalancing, in several works, attempts were made to predict the demand for bikes. In this paper, we devised a bike-sharing service with highly accurate demand prediction using collaborative computing and information fusion. We combined the information of bike demands at different time periods and the locations between stations and proposed a dynamical clustering algorithm for station clustering. We carefully analyzed and discovered the group of features that impact the demand of bikes, from historical bike-sharing records and 5G IoT environment data. We combined the discovered information and proposed an XGBoost-based regression model to predict the rental and return demand. We performed sufficient experiments on two real-world datasets. The results confirm that compared to some existing methods, our method produces superior prediction results and performance and improves the availability of bike-sharing service in 5G IoT environments.

Smart-parking management algorithms in smart city

Recently, various advanced technologies have been employed to build smart cities. Smart cities aim at improving the quality of life through the delivery of better services. One of the current services that are essential for any smart city, is the availability of enough parking spaces to ensure smooth and easy traffic flow. This research proposes a new framework for solving the problem of parking lot allocation, which emphasizes the equitable allocation of people based on the overall count of people in each parking space. The allocation process is performed while considering the available parking lots in each parking space. To accomplish the desired goal, this research will develop a set of seven algorithms to reduce the gap in the number of people between parking spaces. Many experiments carried out on 2430 different cases to cover several aspects such as the execution time and the gap calculations, were used to explore the performance of the developed algorithm. Analyzing the obtained results indicates a good performance behavior of the developed algorithms. Also, it shows that the developed algorithms can solve the studied problem in terms of gap and time calculations. The MR algorithm gained excellent performance results compared to one of the best algorithms in the literature. The MR algorithm has a percentage of 96.1 %, an average gap of 0.02, and a good execution time of 0.007 s.

Case Study on Intelligent Road Lighting in Foreign Countries under the Background of Smart City

With the development of the city, the relationship between the city and wisdom has become more and more close. In this process, the attention of smart lighting construction has gradually increased, and many cities regard smart lighting as a promotion point for building smart cities. This paper adopts a qualitative research method to explore the typical road smart lighting construction cases in some foreign cities. By analyzing the cases of developing urban smart lighting construction in various countries, it is found that the functions of the transformed smart lighting can play an important role in energy saving, urban security, data collection, and citizen services for cities in various countries. Finally, by further summarizing the main content of its construction, it provides some reference suggestions for the development of smart lighting in my country.

Assessing the Intention to Evade Fares for Demographic Segments of Passengers: Empirical Research in Italy for Building Smart(er) Cities

A strong interest in fare evasion is currently emerging in all proof-of-payment transit systems owing to its severe implications. Recent research has investigated from the passenger perspective how sociodemographic variables, travel behavior, and situational factors affect the intention to evade fares for students, workers, and unemployed passengers. Conversely, testing the demographic segmentation of passengers clustered according to gender and age variables and isolating the key factors related to the intention to evade fares have not yet been addressed. Besides the context of the smart cities’ paradigm, the fight against fare evasion has been little dealt with, also owing to the difficulty in being able to frame such an issue within the classic pillars of smartness. This study covers these gaps using a two-step approach. First, gender and age variables are used to define five demographic segments: male, female, young, middle-aged, and older passengers. Next, crucial determinants are detected for each segment using statistical models. The analysis revealed that the intention to evade fares increases for males who travel a lot during the day and for females who are dissatisfied with the service and know the fine amount. Moreover, it increases for young, middle-aged, and older passengers that make short trips, often travel, and use various transit systems, respectively. Finally, the intention to evade fares increases for each segment when passengers have a history of fare evasion. The overall findings may help bus operators plan tailored countermeasures against specific segments.

Rethink on Smart City: Technology Approach, Showcase Approach or Demand Approach?

The author reveals three current approaches to build Smart Cities, i.e., technology-oriented, showcase-oriented and demand-oriented, and analyzes the causes, advantages, shortcoming and influences of the three approaches. In the conclusion, a composite approach with the demand approach as its core is advocated, which returns to the demands of the city that supports problem-solving, innovation, intelligent governance, economic development and regional synergy.

RETRACTED: Risk-based energy management of industrial buildings in smart cities and peer-to-peer electricity trading using second-order stochastic dominance procedure

• For an industrial site, the P2P electricity trading methodology is considered. • A central battery as the shared storage unit is applied. • Adaptation of stochastic dominance for evaluation of financial risks. • Risk-averse and risk-neutral models of an industrial area is studied. Generally, industrial consumers face various financial problems due to their high energy consumption. Because during the peak demand periods, the electricity power has its highest cost value. In this regard, the peer-to-peer electricity trading method is considered an industrial site that includes five interconnected buildings and is equipped with onsite flexibility and distributed energy resources (DERs). The regarded industrial site has been evaluated under three different case studies to optimize the system's performance in the electricity market against the financial risks. However, in the last case study that the shared storage unit is established, a risk evaluation method called stochastic dominance (SD) is applied to show the operation of the system in the risk-averse and risk-neutral models. According to the obtained results, 22% and 15% of the energy cost are deduced in the third case compared with the first and second case studies, respectively.

Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective

The goal of this study is to investigate the direct and indirect relationships that exist between artificial intelligence (AI), social innovation (SI), and smart decision-making (SDM). This study used a survey design and collected cross-sectional data from South Korea and Pakistan using survey questionnaires. Four hundred sixty respondents from the public and private sectors were obtained and empirically analyzed using SPSS multiple regression. The study discovered a strong and positive mediating effect of SI between the relationship of AI and SDM, as predicted. Previous researchers have investigated some of the factors that influence the decision-making process. This study adds to the social science literature by examining the impact of a mediating factor on decision-making. The findings of this study will contribute to the local government in building smart cities such that the factor of social innovations should be involved in the decision-making process because smart decision-making would share such collected data with entrepreneurs, businesses, and industries and would benefit society and all relevant stakeholders, including such social innovators.

The necessary permanent WiFi health monitoring of the structures as condition for the smart buildings in smart cities

In the past, our safety “standard” were much lower than now. We felt comfortable driving cars without a seat belt, without exterior rear mirror, without ABS nor AirBag. Today our cars are equipped with sensors and controls much better than the airplanes that flew in second World War. Our continuous request of comfort goes hand in hand with the increasing safety for our life. Most of the machines and equipments around us enjoy of sophisticated control system, but civil structures not yet. Only few special bridges, mainly suspension or cable stay bridges, have some permanent tools to control them, and provide alert communication in case of suspect behaviour. “Normal”, simple bridges miss this type of controls. Buildings, monuments, schools, hospitals, strategic buildings (Civil protection, Municipality, Police Stations, TV stations, communication centers…), all miss monitoring system for structural safety. Most of “smart solutions” refer to domotic (house automation) aspect like temperature, heating/air conditioning, lights, burglar alarm. All of them miss structural health control, forgetting problem like earthquake, structure aging, material decay and so on. Increasing the structural safety control of the buildings with smart devices means to increase global safety for the cities. The paper introduces some experiences of the authors in this field, and presents some possible scenarios for the future.

Smart Parking System Using Internet of Things: A Step towards Building Smart Cities

Smart Parking System Using Internet of Things: A Step towards Building Smart Cities

A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception

To create and maintain comfortable indoor environments, predicting occupant thermal sensation is an important goal for architects, engineers, and facility managers. The link between thermal comfort, productivity, and health is common knowledge, and researchers have developed many state-of-the-art thermal-sensation models from dozens of research projects over the last 50 years. In addition to these, the use of intelligent data-analysis techniques, such as black-box artificial neural networks (ANNs), is receiving research attention with the aim of designing building thermal-behavior models from collected data. With the convergence of the internet of things (IoT), cloud computing, and artificial intelligence (AI), smart buildings now protect us and keep us comfortable while saving energy and cutting emissions. These types of smart buildings play a vital role in building smart cities of the future. The aim of this study is to help facility managers predict the thermal sensation of the occupants under the given circumstances. To achieve this, we applied a data-driven approach to predict the thermal sensation of occupants of an indoor environment using previously collected data. Our main contribution is to design and evaluate a deep neural network (DNN) for predicting thermal sensations with a high degree of accuracy regardless of building type, climate zone, or a building’s heating and/or ventilation methods. We used the second version of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database to train our model. The hyperparameter-tuning process of the proposed model is optimized using the Bayesian strategy and predicts the thermal sensation of occupants with 78% accuracy, which is much higher than the traditional predicted mean vote (PMV) model and the other shallow and deep networks compared.