Smart City Domain Research Articles

The Intersection of the Internet of Things and Smart Cities: A Tertiary Study

Since the transition from an agricultural to an industrial economy, cities have attracted large masses of people in search of their facilities. Cities are symbols of progress and opportunities. However, urbanization also brings with it several problems and challenges. Smart Cities (SC) offer a way to address these challenges by using technology to make cities more efficient, sustainable, and livable. There are numerous technologies that enable the concept of smart cities, including the Internet of Things (IoT). The IoT provides the fundamental sensing infrastructure that allows connecting and virtualizing the physical world, extracting environmental variables that serve as initial inputs for decision-making processes. Such processes are provided by software systems whose construction and execution need to deal with the dynamism, heterogeneity and often serendipitous nature that permeate both the domains of smart cities and IoT. As the integration of IoT and Smart Cities paradigms is still at an early stage, and there are not yet holistic solutions to explore the full potential of such a synergy, we carried out a literature review on the topic. In particular, the objective of this article is to describe the results of a structured literature review to identify general concepts about quality attributes, applications, technologies, and challenges of IoT solutions applied to the SC domain. Our main goal is to assist in understanding the basic concepts of the research area through the search for secondary studies. This review is a tertiary study covering 17 reviews and aims to promote a high-level discussion on the identified characteristics and provide an overview of the area to promote a better perception of current development needs and opportunities.

A semantic axiomatic design for integrity in IoT

AbstractIn the complex, critical, and rapidly evolving era of Industry 4.0 manufacturing, mature engineering disciplines can be developed to validate system designs at an abstract level, enabling effective fault‐free environments. However, designing integrated IoT platforms presents significant challenges due to their inherent complexity and interoperability issues. To address this challenge, the proposed framework integrates axiomatic design principles with considerations for data integrity and domain ontology. Furthermore, this paper introduces a multi‐level integrity rule repository for IoT application to provides a robust foundation for designing and maintaining IoT systems. To validate the effectiveness of this approach, we conducted a study across four domains of smart cities. The design's quality was assessed using model quality parameters, including completeness, consistency and validity. Our analysis shows that the semantic‐aware axiomatic design framework can effectively address the problem of inconsistencies and low‐quality design in scientific IoT environments, providing a more reliable and efficient solution.

A Survey on Knowledge Graph Related Research in Smart City Domain

A Survey on Knowledge Graph Related Research in Smart City Domain

An Ontological Model to Enhance Traffic Conditions in Smart City Domain

The rising trend of the increasing interconnection of city services refers to the growing integration and interconnectedness of various urban systems and services through the utilization of technology and data. One of the primary objectives of smart city initiatives is to establish more efficient and sustainable urban environments by optimizing resource utilization and infrastructure. In this paper, we introduce a traffic ontology named "TrafCsOnto," which encompasses a comprehensive range of traffic concepts. The primary aim of this ontology is to address traffic congestion issues within the smart city context. The formalization of this ontology has been carried out using the OWL language and the protégé tool. We endeavor to illustrate the practical application of the traffic ontology through the manipulation of real-world instances and inferences.

Exploring the Multifaceted Impact of Artificial Intelligence and the Internet of Things on Smart City Management

The evolution of cities into sustainable and intelligent entities is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and the Internet of Things (IoT). This study systematically examines 133 papers published between 2014 and 2021, predominantly sourced from Scopus (90%) and WoS (70%). Focusing on key smart city domains such as healthcare, education, environment, waste management, mobility, agriculture, risk management, and security, the analysis explores the applications of AI. As cities increasingly embrace AI for operational automation, data-driven decision-making, and environmental improvements, regulatory challenges surface, spanning concerns related to privacy, service delivery discrimination, and ethical considerations. The impact of AI adoption, especially in healthcare following the 2019 global health crisis, is underscored, emphasizing the pivotal role of AI algorithms, including ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM, in shaping urban development trajectories. This research provides insights into the multifaceted implications of AI in smart cities, offering a comprehensive overview of the benefits, challenges, and transformative potential of these technologies across diverse urban sectors.

The role of sport in the Smart City concept

Sport brings to cities innovative solutions that influence urban life, yet the considerations about sport is still rarely contributed to the development of very popular smart cities strategies. This raises the question: what is the perception of sport in the city from the perspective of the smart city concept? One of the challenges of future urban policy is to provide initiatives that ensure the well-being and promote the model of a healthy city, especially during the era of the Covid 19 pandemic. Therefore, sport creates an opportunity to combat these challenges. The study provides an in-depth literature review on sport factors influencing smart city domains through a systematic review process. Exploring this topic creates a paradigm for future studies on the discourse on smart cities and sport impact on evolution of cities of tomorrow. In particular, it indicate potential areas of interest from the point of view of local authorities and policymakers, who are able to incorporate governance through sport agendas.

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

Proactive Edge Computing for Smart City: A Novel Case for ML-Powered IoT

Purpose: In response to the challenges posed by traditional cloud-centric IoT architectures, this research explores the integration of Proactive Edge Computing (PEC) in context of smart cities. The purpose addresses privacy concerns, enhance system capabilities, and introduce machine learning powered anticipation to revolutionize urban city management.
 Methodology: The research employs a comprehensive methodology that includes a thorough review of existing literature on use of IoT devices, edge computing and machine learning in context of smart cities. It introduces the concept of PEC to advocate for a shift from cloud-centric to on-chip computing. The methodology is based on several case studies in various domains of smart city management focusing on the improvement of public life.
 Findings: This research reveal that the integration of PEC in various smart city domains leads to a significant improvement. Real time data analysis, and machine learning predictions contributes to reduced congestion, enhance public safety, sustainable energy practices, efficient waste management, and personalized healthcare.
 Unique Contribution to Theory, Policy and Practice: The research makes a unique contribution to the field of theory, policy and practice by proposing a paradigm shift associated with IoT for smart cities. The suggested shift not only ensures data security but also offers a more efficient and proactive approach to urban challenges. The case studies provide actionable insights for policymakers and practitioners, fostering a holistic understanding of the complexities associated with deploying IoT devices in smart cities. The research lays the foundation for a more secure, efficient, and anticipatory ecosystem, aligning technological advancements with societal needs in the dynamic landscape of smart cities.

Application of context-driven methodology for implementing the smart city concept in Czech republic

This paper investigates the significance of context comprehension within the smart city domain by emphasizing the various perspectives of stakeholders such as ministries, municipalities, and citizens. Since the value of service provision depends on the interactions across different service systems within distinct domains and contexts, through an exploration of the value chain and formulation of value propositions, the paper aims to achieve the synergy among diverse service contexts. Using the smart city context of the Czech Republic as a case study, the paper systematically examines the implementation of context-driven methodology and provides insights for the broader development of smart cities. Also, this study highlights the importance of understanding service overlaps and identifies key issues for future service research, with potential applicability in smart city initiatives, particularly in middle and eastern EU countries.

Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis

In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this study, we delve into the multifaceted dimensions of the smart city paradigm to unveil its underlying structure, employing a combination of quantitative and qualitative techniques. To achieve this, we collected textual data from three sources: scientific publication abstracts, news blog posts, and social media entries. For the analysis of this textual data, we introduce an innovative semi-automated methodology that integrates topic modeling and thematic analysis. Our findings highlight the intricate nature of the smart city domain, which necessitates examination from three perspectives: applications, technology, and socio-economic perspective. Through our analysis, we identified ten distinct aspects of the smart city paradigm, encompassing mobility, energy, infrastructure, environment, IoT, data, business, planning and administration, security, and people. When comparing the outcomes across the three diverse datasets, we noted a relative lack of attention within the scientific community towards certain aspects, notably in the realm of business, as well as themes relevant to citizens’ everyday lives, such as food, shopping, and green spaces. This work reveals the underlying thematic structure of the smart city concept to help researchers, practitioners, and public administrators participate effectively in smart city transformation initiatives. Furthermore, it introduces a novel data-driven method for conducting thematic analysis on large text datasets.

Mapping Information Technology Adoption in Smart Cities: A Literature Review and Research Themes

In the growing digital era, Information Technology (IT) has created major breakthroughs in various fields, before finally playing a significant role in the Smart City (SC) concept as has been proven in many studies. Previous studies have shown that IT implementation in SC varies greatly as it has to be customized to the SC domain. Thus, it is necessary to map the feasible IT adoption for each SC domain. Here, we conducted a literature review and found several research themes of IT adoption in SC. Our findings include: (i) IT adoption in SC, and (ii) SC domains. In this study, a literature review was conducted manually on journals that examined IT adoption in SC and SC domains. The extraction and synthesis process results identified several research themes, including Geographic Information Systems, Internet of Things (IoT), Big Data Analytics, and Wireless Sensor Networks. Research questions are posed in each theme, which can be helpful to insights for future research.

A semantics-enabled approach for personalised Data Lake exploration

The increasing availability of Big Data is changing the way data exploration for Business Intelligence is performed, due to the volume, velocity and uncontrolled variety of data on which exploration relies. In particular, data exploration is required in Data Lakes that have been proposed to host heterogeneous data sources, given their flexibility to cope with cumbersome properties of Big Data. However, as data grows, new methods and techniques are required for extracting value and knowledge from data stored within Data Lakes, aggregating data into indicators according to multiple analysis dimensions, to enable a large number of users with different roles and competencies to capitalise on available information. In this paper, we propose PERSEUS (PERSonalised Exploration by User Support), a computer-aided approach for data exploration on top of a Data Lake, structured over three phases: (1) the construction of a semantic metadata catalog on top of the Data Lake, leveraging tools and metrics to ease the annotation of the Data Lake metadata; (2) modelling of indicators and analysis dimensions, guided by an openly available Multi-Dimensional Ontology to enable conformance checking of indicators and let users explore Data Lake contents; (3) enrichment of the definition of indicators with personalisation aspects, based on users’ profiles and preferences, to make easier and more usable the exploration of data for a large number of users. Results of an experimental evaluation in the Smart City domain are presented with the aim of demonstrating the feasibility of the approach.

Progress and prospects of future urban health status prediction

Predicting future urban health status is significant in terms of identifying urban diseases and urban planning. Current studies have focused on using machine learning and deep learning models to predict future urban populations, urban safety, and traffic conditions. The city is a complex system, and perceiving the future urban health status requires accurate predictions of the multifarious components that make up the urban system. In this work, firstly, the improved meta-analysis approach is applied to the smart city domain. Secondly, considering the city as a complex system, the concept of urban factors is proposed. Eight urban factors are selected and they are graded into three tiers. Finally, methods suitable for predicting urban factors reflecting the health of a city are discussed in depth, and which prediction method is most suitable for a particular urban factor is summarized. Among the final 40 studies included in the meta-analysis, the maximum values for Prediction Improvement Rate (PIR) and Weighted PIR (WPIR) reach 95.98% and 22.31%, respectively.

6G-Enabling the New Smart City: A Survey.

Smart cities and 6G are technological areas that have the potential to transform the way we live and work in the years to come. Until this transformation comes into place, there is the need, underlined by research and market studies, for a critical reassessment of the entire wireless communication sector for smart cities, which should include the IoT infrastructure, economic factors that could improve their adoption rate, and strategies that enable smart city operations. Therefore, from a technical point of view, a series of stringent issues, such as interoperability, data privacy, security, the digital divide, and implementation issues have to be addressed. Notably, to concentrate the scrutiny on smart cities and the forthcoming influence of 6G, the groundwork laid by the current 5G, with its multifaceted role and inherent limitations within the domain of smart cities, is embraced as a foundational standpoint. This examination culminates in a panoramic exposition, extending beyond the mere delineation of the 6G standard toward the unveiling of the extensive gamut of potential applications that this emergent standard promises to introduce to the smart cities arena. This paper provides an update on the SC ecosystem around the novel paradigm of 6G, aggregating a series of enabling technologies accompanied by the descriptions of their roles and specific employment schemes.

REINFORCEMENT LEARNING EMPOWERED DIGITAL TWINS: PIONEERING SMART CITIES TOWARDS OPTIMAL URBAN DYNAMICS

Smart cities have emerged as a promising solution to address the challenges posed by rapid urbanization and the quest for sustainable urban development. To achieve optimal urban dynamics and enhance the quality of life for citizens, there is a growing need for innovative approaches that integrate cutting-edge technologies. This paper introduces the concept of Reinforcement Learning Empowered Digital Twins as a pioneering strategy for smart cities. By combining the power of digital twin technology with reinforcement learning algorithms, cities can create dynamic, real-time virtual representations that mirror urban systems and interact with the physical world. This integration enables data-driven decision-making, efficient resource management, and optimized traffic flow, ultimately leading to reduced congestion, decreased fuel consumption, and improved air quality. The paper explores the potential applications of reinforcement learning empowered digital twins in various smart city domains, such as intelligent transportation systems, energy management, and urban planning, but mainly with respect to traffic flow and optimisation particularly in the state of Chhattisgarh and its prospects. Moreover, it identifies research gaps and discusses future directions to unlock the full potential of this transformative approach in pioneering smart cities towards optimal urban dynamics. Various scientists have been focusing on this and suggest further investigation and examination as a response and have given their own assessments; this paper essentially discusses, and overviews created by 5 articles, Machine learning approaches for smart city applications: Emergence, challenges and opportunities [1] by Sonam Mehta, Bharat Bhushan & Raghvendra Kumar; Applications of artificial intelligence and machine learning in smart cities [2] by Zaib Ullah, Fadi Al-Turjman, Leonardo Mostarda, Roberto Gagliardi; Enabling cognitive smart cities using big data and machine learning: Approaches and challenges [3] by Mehdi Mohammadi, Ala Al-Fuqaha; A survey on algorithms for intelligent computing and smart city applications [4] by Zhao Tong, Feng Ye, Ming Yan, Hong Liu, Sunitha Basod; The Reversible Lane Network Design Problem (RL-NDP) for Smart Cities with Automated Traffic [5] by L Conceição, GHA Correia, JP Tavares. KEYWORDS: Smart Cities, Digital Twins, Reinforcement Learning, Intelligent Transportation Systems, Urban Dynamics, Traffic Flow Optimization, Energy Consumption Management

Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication

This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The proposed algorithm integrates the strengths of machine learning techniques, more specifically, the long short-term memory (LSTM) technique, with teaching learning-based optimization algorithms. To achieve optimization, the algorithm learns from historical data on energy consumption and communication patterns specific to the modeled system. By leveraging these insights, it can predict future energy consumption and communication patterns accurately. Additionally, the algorithm incorporates a modified teaching learning-based optimization approach inspired by the teaching and learning process in classrooms. It adjusts the system parameters based on feedback received from the system, thereby optimizing both energy consumption and communication systems. The effectiveness of the proposed algorithm is evaluated through a case study conducted on the test system, where historical data on energy consumption and communication patterns are analyzed. The results demonstrate that the algorithm efficiently optimizes the communication and energy management systems, leading to substantial energy savings and improved communication efficiency within the test system. In conclusion, this study presents a hybrid machine learning and modified teaching learning-based optimization algorithm that effectively addresses the communication and energy management challenges in the test system. Moreover, this algorithm holds the potential for application in various smart city domains beyond the test system. The findings of this research contribute to the advancement of smart city technologies and offer valuable insights into reducing energy consumption in densely populated urban areas.

The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities.

The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.

Understanding Security in Smart City Domains From the ANT-Centric Perspective

A city is a large human settlement that serves the people who live there, and a smart city is a concept of how cities might better serve their residents through new forms of technology. In this paper, we focus on four major smart city domains according to Maslow’s hierarchy of needs: smart utility, smart transportation, smart homes, and smart healthcare. Numerous IoT applications have been developed to achieve the intelligence that we desire in our smart domains, ranging from personal gadgets such as health trackers and smart watches to large-scale industrial IoT systems such as nuclear and energy management systems. However, many of the existing smart city IoT solutions can be made better by considering the suitability of their security strategies. Inappropriate system security designs generally occur in two scenarios: first, system designers recognize the importance of security but are unsure of where, when, or how to implement it; and second, system designers try to fit traditional security designs to meet the smart city security context. Thus, the objective of this paper is to provide application designers with the missing security link they may need in order to improve their security designs. By evaluating the specific context of each smart city domain and the context-specific security requirements, we aim to provide directions on when, where, and how they should implement security strategies and the possible security challenges they need to consider. In addition, we present a new perspective on security issues in smart cities from a data-centric viewpoint by referring to the reference architecture, the Activity-Network-Things (ANT)-centric architecture. This architecture is built upon the concept of “security in a zero-trust environment", to achieve end-to-end data security. By doing so, we reduce the security risks posed by new system interactions or unanticipated user behaviors while avoiding the hassle of regularly upgrading security models.

UrbanEnQoSPlace: A Deep Reinforcement Learning Model for Service Placement of Real-Time Smart City IoT Applications

Multi-access Edge Computing (MEC) enables IoT applications to place their services in the edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-efficiency. Previous works consider compute requirements, while the IoT and latency/bandwidth per-flow communicate requirements are largely ignored. Moreover, the Smart City domain presents unique challenges - modeling the Urban Smart Things (USTs - urban IoT clients), their connectivity with MEC network, diverse resource requirements (compute, communicate, and IoT) of application services, modeling the federation of multiple MEC providers in a city, which we consider in this paper. To address these research gaps, we propose: i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UrbanEnQoSMDP</i> - formulation for energy and QoS (latency) optimized service placement for a set of applications in the ‘Urban IoT-Federated MEC-Cloud’ architecture to satisfy applications’ compute, per-flow communicate, and IoT requirements; ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‘’<inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-greedy with mask”</i> policy for apriori satisfaction of IoT requirements by shortlisting suitable USTs; iii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UrbanEnQoSPlace</i> - proposed multi-action Deep Reinforcement Learning (DRL) model, designed from Dueling Deep-Q Network, that uses the proposed policy to solve the UrbanEnQoSMDP for simultaneously placing all services of an application. Extensive simulation results illustrate efficacy and scalability of proposed model against state-of-the-art DRL algorithms (better convergence, higher rewards, lesser runtime; proposed policy w.r.t fewer violations).

A Review and Comparative Study of Explainable Deep Learning Models Applied on Action Recognition in Real Time

Video surveillance and image acquisition systems represent one of the most active research topics in computer vision and smart city domains. The growing concern for public and workers’ safety has led to a significant increase in the use of surveillance cameras that provide high-definition images and even depth maps when 3D cameras are available. Consequently, the need for automatic techniques for behavior analysis and action recognition is also increasing for several applications such as dangerous actions detection in railway stations or construction sites, event detection in crowd videos, behavior analysis, optimization in industrial sites, etc. In this context, several computer vision and deep learning solutions have been proposed recently where deep neural networks provided more accurate solutions, but they are not so efficient in terms of explainability and flexibility since they remain adapted for specific situations only. Moreover, the complexity of deep neural architectures requires the use of high computing resources to provide fast and real-time computations. In this paper, we propose a review and a comparative analysis of deep learning solutions in terms of precision, explainability, computation time, memory size, and flexibility. Experimental results are conducted within simulated and real-world dangerous actions in railway construction sites. Thanks to our comparative analysis and evaluation, we propose a personalized approach for dangerous action recognition depending on the type of collected data (image) and users’ requirements.