Smart City Services Research Articles

Unleashing The Power of Big Data: The Phenomenon Behind Indonesia's Smart City Policy Implementation

The concept of smart cities has emerged as a promising prospect for several major urban centers in Indonesia. Putting smart city projects together requires thinking about many things. Big data has become one of the most important parts for making sure that these projects will last. Employing big data technology makes it easier to improve the ways that data is stored and processed. This makes it possible to gather useful data that could greatly improve a wide range of smart city services. This study uses a library-based qualitative research approach to analyze scholarly journals and relevant literature. The method involves meticulous collection of data from carefully chosen libraries, aiming to provide comprehensive insights into the phenomenon of big data in smart cities implementation within government administration. This study aims to contribute to the identification and analysis of challenges encountered in emergency situations, with the ultimate goal of providing assistance in effectively mapping the problem at hand. The present discourse underscores the indispensability of Indonesia's contemporary human mindset in bolstering the viability of the smart city paradigm.

Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches

In recent years, the concept of smart cities has become increasingly important in the pursuit of sustainable development goals. In general, common urban challenges have been addressed through smart-city services, and new perspectives for more sustainable cities have emerged. To realize the full potential of such smart urban environments, geospatial approaches have been used as a focal point, offering a plethora of applications that contribute to a better understanding of urban challenges and innovation potentials. Nevertheless, although significant progress has been made, different problems may arise when the available technologies and resources are not understood or even when their potentialities are not properly capitalized. This article reviews the state of the art in the field, highlighting success cases and remaining challenges in exploiting geospatial data-driven strategies, particularly when leveraging geographic information systems, satellites, and distributed sensors to produce and process geospatial data and datasets in urban scenarios. Moreover, a more organized perspective of the area is provided in this article, as well as future development trends, supporting new research efforts in this area when empowering smart cities for a more sustainable future.

Lightweighting Process of Digital Twin Information Models for Smart City Services

A smart city digital twin refers to a system designed to digitally replicate real-world city objects in a virtual space and implement real-time synchronization with the real world, thus helping to solve urban problems through various service scenario simulations and data analysis processes. In relation to the development of city information models for implementing the smart city digital twin, many efforts have been made to integrate and utilize BIM-GIS data. However, due to problems with the capacity available for storing large amounts of data, there are limitations in practical applications because it is sometimes difficult to provide numerous realtime services in the era of web environments. Therefore, in this study, a lightweighting process for the Lightweight Digital Twin System was presented as a method to provide various services that a smart city offers based on the digital twin, and a lightweighting method was proposed accordingly. The process and method were verified through the development of a case application for the case of 00 city. It is expected that the Digital Twin System lightweighting method proposed in this study will serve as a basis for efficient city information processing and contribute to facilitating the provision of various smart city-related services.

Editorial: Artificial Intelligence-based Security Applications and Services for Smart Cities

Editorial: Artificial Intelligence-based Security Applications and Services for Smart Cities

Enhancing Smart City Services with AI: A Field Experiment in the Context of Industry 5.0

The practical effects of incorporating artificial intelligence (AI) into Industry 5.0 smart city services are made evident by this empirical research. The use of AI-powered smart traffic management yields a noteworthy 32.94% rise in traffic volume, signifying a noteworthy progression towards improved urban mobility. AI waste management optimization results in a 5.71% increase in collection efficiency, highlighting the importance of operational effectiveness and resource conservation. The control of energy use shows an 8.57% decrease, confirming AI's importance in sustainable energy practices. AI-enhanced public safety offers dependable event prediction, indicating safer cityscapes. These results highlight AI's revolutionary potential and establish smart cities as safe, secure, and sustainable urban environments.

Comparative Study of Horizontal and Vertical Approaches to IoT Services for Smart Cities

Smart cities aim to address social issues in the community using Internet of Things (IoT). IoT has traditionally been used to address various challenges in different regions. Recently, efforts have focused on defining smart cities and addressing regional issues horizontally as they are often shared by other regions. There are two technological approaches to IoT services: the ‘vertical approach,’ which involves building unique IoT services tailored to regional characteristics, and the ‘horizontal approach,’ which enables new services by horizontally linking data collected in each region within City OS in the cloud. This paper categorizes technological approaches into three groups: ‘service-specific type,’ ‘cloud-intensive type,’ and ‘edge-cloud cooperation type,’ and compares their respective features.

SMART CITY MANAGEMENT SYSTEM UTILIZAING MICRO-SERVICES AND IOT-BASED SYSTEMS

Urban governance is without a doubt a very complicated activity. The city doesn't just consist of people and buildings, there are transport and road infrastructure, critical infrastructure, medical facilities, road cover, industrial equipment and many more. Besides, the city council provides a wide range of services to the public. Among them healthcare, welfare, economic and finance supply, labor, real estate management and others. Information management systems and web services are employed for digital management, while various embedded management systems are used for equipment management and surveillance. Being very complex and multilayered system, Smart City Managment solutions offer a platform that encapsulates main services for both public and for technological aspects of urban governance. The results presented in this paper are based on a study of the existing software, hardware and middleware platforms for smart city use case. The main focus is middleware platform as it serves as medium that can connect existing software and hardware solutions. In the global context of Smart city management service system all major components are broken down in the format of microservices (on the level of large enterprise distributed service). Presented Managment software suit model had been broken down into multiple software architecture abstraction layers, from hardware to end-user application. As a result, the three stages smart city service implementation roadmap had been presented. Using the Middleware platform and Web-services models, the Smart City Managment services can be implemented in any given city in porotype stage for future evaluation and full version implementation.

Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms

<abstract> <p>With the use of cloud computing, which provides the infrastructure necessary for the efficient delivery of smart city services to every citizen over the internet, intelligent systems may be readily integrated into smart cities and communicate with one another. Any smart system at home, in a car, or in the workplace can be remotely controlled and directed by the individual at any time. Continuous cloud service availability is becoming a critical subscriber requirement within smart cities. However, these cost-cutting measures and service improvements will make smart city cloud networks more vulnerable and at risk. The primary function of Intrusion Detection Systems (IDS) has gotten increasingly challenging due to the enormous proliferation of data created in cloud networks of smart cities. To alleviate these concerns, we provide a framework for automatic, reliable, and uninterrupted cloud availability of services for the network data security of intelligent connected devices. This framework enables IDS to defend against security threats and to provide services that meet the users' Quality of Service (QoS) expectations. This study's intrusion detection solution for cloud network data from smart cities employed Spark and Waikato Environment for Knowledge Analysis (WEKA). WEKA and Spark are linked and made scalable and distributed. The Hadoop Distributed File System (HDFS) storage advantages are combined with WEKA's Knowledge flow for processing cloud network data for smart cities. Utilizing HDFS components, WEKA's machine learning algorithms receive cloud network data from smart cities. This research utilizes the wrapper-based Feature Selection (FS) approach for IDS, employing both the Pigeon Inspired Optimizer (PIO) and the Particle Swarm Optimization (PSO). For classifying the cloud network traffic of smart cities, the tree-based Stacking Ensemble Method (SEM) of J48, Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied. Performance evaluations of our system were conducted using the UNSW-NB15 and NSL-KDD datasets. Our technique is superior to previous works in terms of sensitivity, specificity, precision, false positive rate (FPR), accuracy, F1 Score, and Matthews correlation coefficient (MCC).</p> </abstract>

Transformative effects of connectivity technologies on urban infrastructure and services in smart cities

The dynamic urban landscapes of smart cities, focusing on four critical dimensions, traffic volume, energy consumption, waste production, and water consumption, spanning from January 2023 to January 2024. Leveraging comprehensive datasets and advanced analytics, the intricate patterns that underlie these urban dynamics and offer insights to inform sustainable urban planning and development. The analysis of traffic volume reveals substantial fluctuations, emphasizing the multifaceted nature of urban transportation. Seasonal variations, economic activities, and technological advancements emerge as pivotal factors influencing traffic patterns. Strategies for optimizing traffic flow and enhancing transportation systems hinge on a nuanced understanding of the what, why, and how of traffic volume dynamics. Energy consumption, another critical facet, exhibits significant variability. The influence of seasonal shifts, economic factors, and technological innovations on energy usage. The exploration of the what, why, and how of energy consumption fluctuations underscores the importance of sustainable energy practices and effective energy management strategies in smart cities. Waste production and water consumption analyses underscore the dynamic nature of waste generation and water usage within smart city contexts. These insights illuminate the need for adaptable waste management and water conservation strategies. Ultimately, this research enhances the understanding of the complexities inherent in smart cities and provides a data-driven foundation for urban planners, policymakers, and researchers to formulate strategies that optimize resource allocation, bolster sustainability, and elevate the urban living experience.

DIMENSIONS OF DATA QUALITY FOR VALUES IN SMART CITIES DATAFICATION PRACTICES

Data quality facilitates data interoperability for optimal decision-making in smart cities datafication. But there are few studies on how technologists (e.g., data scientists), governance people (e.g., municipal workers), and third-party collaborators (e.g., smart city services vendors) assess data quality together in smart cities datafication. This paper offers a response to this knowledge gap, using interviews (n=10) with municipal workers, data scientists and smart city services vendors, and data structure documents (n=8) in a situated case, the Stavanger (Norway) smart city. Implicit the paper’s results is that data quality is a floating signifier – comprising the different articulations of data scientists, municipal workers and services vendors in assessment. This generates friction with implications on data interoperability. This paper therefore posits that assessing data quality in smart cities datafication is ambiguous, but not empty. It fluctuates between the articulations of data scientists, municipal workers, and services vendors, with implications on data interoperability through the friction this generates. Keywords: data quality, data interoperability, floating signifier, frictions, smart city datafication

A decision-making approach for determining strategic priority of sustainable smart city services from citizens’ perspective: A case study of Hong Kong

The development of sustainable smart city (SC) services is often carried out with limited resources, but there is little discussion about the strategic priority of promoting SC development from the citizens’ perspective. This study developed a decision-making approach to determine the supply and demand priority of SC services using utility and need theories, with Hong Kong as a case study. The results show that smart environment services should be prioritized for all population groups, while the differences in priority among older adults, people with lower digital literacy, and people with lower income were relatively small. On the one hand, the supply of SC services should be prioritized for those that are closely linked to citizens’ daily lives, have a long-term impact on communities, and are targeted to large user groups. On the other hand, priority should be given to those services that meet citizens’ demands for physiological, safety, and self-actualization needs. This study enriches the theoretical framework of people-centric SC research through the innovative integration of Bradley-Terry and rank-ordered logit models for determining the development priority for SC services, which can serve as a practical decision-making tool for policy makers to effectively allocate resources for sustainable SC development.

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.

Exploring the Success Factors of Smart City Adoption via Structural Equation Modeling

This study investigated the roles of security and technological factors in the adoption of smart cities, with the aim of developing a deeper understanding of the key aspects of the successful adoption of smart cities in Jordanian traditional cities. This study developed a conceptual model to investigate the importance of security and technological factors in the adoption of smart cities. The proposed model was tested using the structural equation modeling method after collecting data from ICT experts. The findings of the study revealed that perceived security, perceived trust, and service quality play pivotal roles in enhancing the adoption of smart city services. Moreover, the results indicated that information security and information privacy positively impact intentions toward adopting smart city services. These research findings provide valuable insights into the critical factors that can drive the adoption of smart city services. Policymakers and academics could utilize this knowledge to devise and implement new strategies aimed at increasing the adoption of smart city services.

Time-Aware Smart City Services Based on QoS Prediction: A Contrastive Learning Approach

Smart cities are designed to satisfy the needs of residents and improve their quality of life by providing a wide range of smart city services. The key to the efficient operation of smart city services is accurate forecast of the missing QoS (Quality of Service).. At present, many approaches utilize the context information of users and services, such as geographic location and network location, to somewhat increase the prediction accuracy and forecast the missing QoS values.. However, because the network conditions and server status are unpredictable, time is also considered as one of the important factors affecting QoS prediction, which brings more challenges as follows: higher data dimension, more complex data characteristics and higher data sparsity. To overcome these challenges,z we suggest an approach for time-aware Web service QoS prediction based on contrastive learning(named CLpred). CLpred utilizes a sequential data input format for QoS data and models these QoS sequences through Transformer encoder with contrastive learning framework. Therefore, it can downscale QoS data and extract a more efficient representation in complex QoS data. Furthermore, it makes it possible to apply data augmentation methods to address these probelms of data sparsity. In order to prove the superiority of the this approach, particularly inside the presence of extremely high data sparsity, extensive tests are carried out on the well-known service QoS data set WSDREAM.

Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse

The advances in Artificial Intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with AI capabilities. These technological advancements would not have been realized without the assistance of fast, secure, and fault-tolerant communication media. Traditional processing, communication and storage technologies cannot maintain high levels of scalability and user experience for immersive services. The metaverse is an immersive three-dimensional (3D) virtual world that integrates fantasy and reality into a virtual environment using advanced virtual reality (VR) and augmented reality (AR) devices. Such an environment is still being developed and requires extensive research in order for it to be realized to its highest attainable levels. In this article, we discuss some of the key issues required in order to attain realization of metaverse services. We propose a framework that integrates digital twin (DT) with other advanced technologies such as the sixth generation (6G) communication network, blockchain, and AI, to maintain continuous end-to-end metaverse services. This article also outlines requirements for an integrated, DT-enabled metaverse framework and provides a look ahead into the evolving topic.

A Framework for Standardization of Distributed Ledger Technologies for Interoperable Data Integration and Alignment in Sustainable Smart Cities

AbstractDistributed ledger technologies (DLTs) are considered one of the foremost emerging technologies which can contribute to transform cities to smarter cities. DLT play important role in municipalities to accelerate the digitalization process toward changing the roles and services of enterprises in sustainable smart cities. Standardization of DLTs aids to reduce data and digital assets silos while decreasing vendor lock-in across distributed applications enabling a digital urban ecosystem that supports migration capabilities making it possible for cities to seamlessly achieve interoperability among DLTs and centralized digital platforms, although a few standards such as IEEE 2418, IEEE P2418.5, and ISO/TC 307 have been developed. The alignment and integration mechanisms required to support standardization of DLT for interoperable services in smart cities is lacking. Therefore, this study presents an understanding on current and open issues on standardization of DLTs in sustainable smart cities with a specific focus on data integration and alignment efforts related to interoperable DLTs. A framework is developed to promote standardization of DLTs to support integration and alignment for interoperability in smart cities. Design science research methodology was adopted based on three use case scenarios which illustrates how IOTA tangle is employs as a DLT for secured standardized communication between physical sensors, devices, and digital platforms in smart city environment. Findings from this article provide exploratory evidence demonstrating the potential uses of IOTA tangle through the developed framework applied for decentralized and centralized digital services. Based on this evidence, this study provides interface integration and alignment strategies to better exploit distributed applications full potential by improving DLT standardization in urban environment.

Empowering Smart Cities with Fog Computing: A Versatile Framework for Enhanced Healthcare Services and Beyond

Fog Computing represents a distributed computing infrastructure strategically positioned at the network's edge, acting as an intermediate layer between remote cloud services and the data-generating smart devices on the ground. Leveraging this concept, a flexible and efficient smart city design emerges, offering a diverse range of applications, including smart healthcare, car parking, power management, water management, and waste management. The implementation of Fog computing enables reduced data processing latency and equitable workload distribution across fog nodes. The smart city system comprises several layers, namely connection, real-time processing, neighborhood linking, main processing, and data server layers. The flexibility of this framework allows for the scaling up or down of layers depending on specific smart city applications. In a case study focused on Smart healthcare services, the iFogSim platform was utilized to evaluate the system's performance. Notably, the results demonstrated a significant reduction in network usage, data processing latency, and processing costs when compared to traditional cloud computing solutions. Consequently, this improvement in efficiency translated into an enhanced user experience, offering superior scalability and reliability to users utilizing smart city services, including healthcare facilities.

Selection of optimum green energy source under smart city environment for sustainable energy management and planning: An eclectic decision

The purpose of this paper is to present a decision support framework, able to assess and optimize the energy use in smart cities. This paper has taken a sincere endeavour to develop an innovative integrated analytical framework as a benchmark to understand to what extent the energy has been consumed in smart city spectrum. Initially five sectors (construction, water management, transport, waste treatment and public services) of smart city and their corresponding sub-factors are identified based on industry, academia partnership. Once the data were gathered, those were analysed using various statistical tools. This research is useful for the policymakers, executive people, especially those are working or involved in smart city development projects. The developed framework helps to identify the significant energy consumption sector and also suggest the suitable green energy alternatives for developing a cleaner and sustainable future. As this study discusses the various parameters related to smart city energy consumption sector in western India, it will have a huge practical potential on the proposed operational smart cities in India. Literature has witnessed minimum number of studies have been carried out on this proposed framework and that could improve the wellbeing of the people living in the cities.

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. This study presents a novel approach to road condition monitoring in smart cities through the development of an acoustic data processing and analysis module. It focuses on four types of road conditions: smooth, slippery, grassy, and rough roads. To assess road conditions, a microphone integrated road surface detector unit is designed to collect audio signals, and an ultrasonic module is used to observe the road depth information. The whole hardware unit is installed in the wheel rim of the vehicles. The data collected from the road surfaces are then analyzed using machine learning algorithms, such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrate the effectiveness of the proposed method in accurately identifying different road conditions. From these results, it was observed that the MLP provides better accuracy of 98.98% in assessing road conditions. The study provides valuable insights into the development of a more efficient and reliable road condition monitoring system for delivering secure transportation services in smart cities.

A Framework for Simulating the Suitability of Data Usage in Designing Smart City Services

A Framework for Simulating the Suitability of Data Usage in Designing Smart City Services