Smart City Infrastructure Research Articles

Outdoor mobility aid for people with visual impairment: Obstacle detection and responsive framework for the scene perception during the outdoor mobility of people with visual impairment

Outdoor mobility of individuals with visual impairment is challenging particularly where collision with obstacles can have significant impact on both physical and mental health. A variety of technological mobility aids for visually impaired people (VIP) have been studied and proposed in the literature which mainly utilise machine intelligence and deep learning (DL) approaches for object detection. However, object detection via the existing approaches suffers from reliability challenge due to real-time dynamics or the lack of available domain knowledge for specific obstacles identified by the VIP as potential hazards. In the present study, an object detection model (ObDtM) based on deep transfer learning techniques was developed for a custom-built dataset comprising of specific obstacles identified by the VIP as potential hazards. A custom dataset was compiled and manually annotated from various publicly available sources to train the ObDtM. Experiments were conducted to evaluate the proposed ObDtM for unseen obstacles kept as the test set. Results showed that ObDtM outperformed the state-of-the-art with 97% mean Average Precision (mAP), indicating a robust and generalizable DL approach. The compiled dataset and the ObDtM is useful for several potential use cases, particularly highlighting the use of DL in IoT and smart city applications. Additionally, a smart synergetic outdoor mobility framework was proposed for VIP (SOMAVIP) allowing comprehensive and accurate semantic representation of the surroundings by utilising the proposed ObDtM, cloud services, internet of things (IoT), and digital environment in the context of emerging smart city infrastructure. The proposed SOMAVIP can be highly impactful for improving VIPs’ quality of life mainly for safer, cost-effective, and reliable independent outdoor mobility enriched with real-time perception and interpretations of the surroundings.

Critical success factors for public–private partnerships in smart city infrastructure projects

PurposeScaling up smart city infrastructure projects will require a large financial investment. Using public–private partnerships is one of the most effective ways to address budget constraints. Numerous factors have varying degrees of influence on the performance of Public private partnerships (PPP) projects; certain PPP factors are more crucial to the success of a smart city infrastructure project than others, and their influence can be greatly increased when they are fulfilled collectively. This study aims to find out what factors are unique to smart city PPP initiatives, as well as how these factors work together, so that successful smart city infrastructure PPP projects can be scaled up.Design/methodology/approachThe methodology included three sequential stages: identifying the critical success factors (CSF) of PPP for smart cities based on an extensive literature review, collecting data from a sample of 90 PPP practitioners using a Likert scale questionnaire and estimating interrelationships among the CSF and their emergent clusters using structural equation modelling.FindingsThe best fit model developed in this study demonstrated the significance of each factor and their interrelationships within their categories in enhancing the performance of PPPs in smart city infrastructure projects. Five categories of critical success factors for PPPs in smart city infrastructure projects have been established: partnership and collaboration; financial sustainability; contractual duties and outsourcing; smart integration; and contract governance.Practical implicationsThe proposed model represented the causal interrelationships among relevant critical success factors derived from literature, which may help in directing the organization’s attention and resources to more critical areas, leading to the effective fulfilment of the smart city infrastructure project’s objectives. In addition to the theoretical and methodological contributions, this study produced a usable and readily adaptable list and clusters of critical success factors for research in the area of the implementation of PPP in smart city infrastructure projects.Originality/valueTo the best of the authors’ knowledge, this is the first study to identify PPP critical success factors and their themed clusters for smart city infrastructure projects.

Service coverage optimization for facility location: considering line-of-sight coverage in continuous demand space

The reliable service coverage of many facilities or sensors used in smart city infrastructure is highly susceptible to obstructions in urban environments. Optimizing the line-of-sight (LOS) service coverage is essential to locating these facilities for smarter city services. Despite progression in the maximal coverage location problem (MCLP) model for locating facilities, maximizing the LOS service coverage in continuous demand space for facility location problems remains challenging. This study defined the LOS-constrained MCLPs (LOS-MCLPs) and proposed a service coverage optimization model to solve these LOS-MCLPs. We employed a computational geometry algorithm named the visibility polygon (VP) algorithm to simulate the LOS coverage in two-dimensional (2D) continuous demand space. We then coupled this algorithm with a robust heuristic algorithm to search for the optimal solutions to maximize effective LOS service coverage. An experiment applied the developed model to a Wi-Fi hotspot planning problem. The experimental results demonstrated that the proposed model can obtain optimal solutions for LOS-MCLPs according to the distribution of obstacles. Comparative results show that ignoring the LOS effect in the optimization of LOS-MCLPs might lead to large areas of service dead zones.

A novel hybrid ensemble learning for anomaly detection in industrial sensor networks and SCADA systems for smart city infrastructures

Critical Infrastructures (CIs) use Supervisory Control and Data Acquisition (SCADA) systems for monitoring and remote control. Sensor networks are being integrated into all areas of the infrastructures of smart cities. The sensor network data stream contains information that can be utilized to model and control the activity of these infrastructures. However, SCADA systems are constantly exposed to a variety of diverse intrusions, making detection with traditional intrusion detection systems (IDS) extremely difficult. Due to their unique specifications, conventional security solutions, like antivirus and firewall software, are unsuitable for properly securing SCADA systems. In addition, anomaly detection in industrial sensor networks (ISNs) should occur in real time. Therefore, effectively identifying cyberattacks in major SCADA systems is unquestionably essential for enhancing their resilience, ensuring safe operations, and avoiding expensive maintenance. We developed a novel hybrid ensemble model approach to address these issues. This paper's primary objective is to detect hostile intrusions that have already circumvented firewalls and typical IDS. In this paper, we propose a hybrid Ensemble Learning Model (ELM) for intrusion detection in SCADA systems with ISNs utilizing a tangible data gathered from a gas pipeline system given by Mississippi State University (MSU), the water system, and the high-dimensional University of New South Wales-NB 2015 (UNSW-NB15) data that reflects a typical attack in the Internet of Things (IoT) environment. The unity normalization method was adopted for data preprocessing, and the Principal Component Analysis (PCA) was utilized for feature extraction of the high-dimensional datasets. Grey Wolf Optimizer (GWO) was used for optimizing the bagging, stacking, Adaboost, and an ensemble of classifiers Naive Bayes and Support Vector Machine with a majority voting technique. Then, we utilized the proposed approach founded on the bijective soft-set approach for efficient ELM selection. The experiment was conducted in two phases: Initially, without PCA + GWO for feature extraction and selection on the ELM, and subsequently, with PCA + GWO for feature extraction and selection on the ELM. PCA + GWO on the ensemble of classifiers NB + SVM provided an accuracy of 99%, precision of 100%, recall of 100%, and detection rate of 99.90%, outpacing the ensemble of classifiers without PCA feature extraction and GWO optimization approaches.

Securing the Internet of Things-enabled smart city infrastructure using a hybrid framework

Since the Internet of Things (IoT) employs a diverse range of new technologies, it is impossible to build a single recommended design adopted as a master plan for all possible requests. Some possible IoT application areas have not been looked into yet or do not have enough information on how to approach them. This shows that more research needs to be done in this difficult area to find new and potentially big benefits for society. Although smart cities offer residents and providers of capital several advantages, there are numerous ways that breaches could compromise the safety and security of individuals. As a result, several different recommendation designs can coexist in the IoT. This research examines the effects of ethics and technology on the security of IoT-enabled systems in smart city infrastructure. Hence provides a secure IoT network architecture for smart cities combining blockchain and deep learning to safeguard privacy and credibility. This research presents a Secure Smart City Infrastructure using Blockchain and Deep Learning (SSCI-BDL) framework to ensure privacy protection and trustworthiness among IoT communication in smart cities. This framework involves the blockchain network for security management in the smart city infrastructure. This framework integrates the deep learning model with an optimization algorithm that maintains efficient resource utilization in the smart city infrastructure. The simulation results show that the system has high security of 99.5% and the lowest latency rate of 4.1% compared to existing models. Overall, the proposed framework’s efficiency gives the highest rate of 99.8%.

Advanced rail transportation infrastructure as the basis of improved urban mobility: research into Sydney as a smart city

ABSTRACT This research evaluates the advanced rail transportation infrastructure as the foundation of sustainable urban mobility. Sydney's rail transportation was evaluated as a smart city. To undertake this evaluation, twelve of Sydney’s inner municipalities were selected within Sydney's urban area – a 35 km radial distance from the CBD. To further examine Sydney's rail transportation evaluation, CITYKeys, being the smart city framework, was utilised. CITYKeys was carefully evaluated and two of its domains, transportation indicators and improved urban mobility & public transport were thus adopted. This research has two key findings. Firstly, Sydney, as a possible smart city, possesses three primary smart transport procedures: Green practice, Transformation and Model Integration. Subsequently, Sydney's new advanced rail transportation infrastructure aligns with CITYKeys public transport indicators. Secondly, Sydney Metro not only provided a significant technological advantage for the city's suburbs but also enhanced its urban mobility. This study contributes to the field of sustainable urban mobility by advocating the alignment between advanced and innovative rail transportation infrastructure and smart cities. Such findings will support transportation scholars in acquiring a better understanding of the need for advanced rail transportation infrastructure for smart cities.

Blockchain: The Future of Smart City Development

As cities around the world are growing at an unprecedented rate, there is a pressing need for smarter, more sustainable, and efficient urban infrastructure. In recent years, blockchain technology has emerged as a potential solution for various challenges faced by smart cities. Blockchain's unique features such as decentralization, immutability, and transparency offer new opportunities for secure and efficient management of city systems. This paper explores the potential of blockchain technology in smart city development, highlighting its applications in areas such as energy management, transportation, governance, and public services. By analyzing various use cases and examples, the paper demonstrates how blockchain can enhance the reliability, efficiency, and transparency of smart city infrastructure. Additionally, the paper discusses the challenges and limitations of blockchain adoption in smart cities and suggests potential solutions for overcoming these obstacles. Overall, this paper argues that blockchain technology has the potential to transform the future of smart city development, enabling cities to become more resilient, sustainable, and citizen-centric

Security Framework for Internet-of-Things-Based Software-Defined Networks Using Blockchain

Presently, trillions of Internet of Things (IoT) devices are in use, with many more projected to join IoT networks in the future. These IoT devices create a massive volume of data, which cannot be transmitted over the network without proper security and privacy. Furthermore, as the amount of information and variety of interconnected devices grows, problems, including excessive response time, bandwidth constraints, and scalability, emerge in proper network design. To solve the constraints of today’s smart cities for next-generation networks, an effective, secure, and scalable distributed framework must be designed bringing computing and storage resources nearer to endpoints. In this article, combining the strengths of software-defined networks (SDNs) and blockchain technology, an innovative adaptable network infrastructure for smart cities is developed. The network is divided into different domains in which SDN will detect potential attacks and transmit the secured data to the blockchain. Our in-depth experimental analysis on performance evaluation show that the proposed framework achieves 12.75% improvement over baseline methodologies.

Compressed Wideband Spectrum Mapping in 3D Spectrum-Heterogeneous Environment

Given the explosive growth of wireless devices ranging from the 2D ground to the 3D space, the first task preceding any form of dynamic spectrum management for the smart city infrastructures is the sensing of heterogeneous spectrum situation in wireless environments. However, challenge arises when increasing wide bandwidth and enormous geographic space meet power-limited CR receivers with constrained hardware capability. In this paper, a 3D compressed wideband spectrum mapping model is established by exploiting the underlying sparse nature of wideband spectrum situation based on compressed sensing, where joint time-frequency-space compression is executed at sub-Nyquist sampling rate. Due to the ineffectiveness of traditional algorithms, a novel two-phase 3D compressed wideband spectrum mapping scheme is proposed which is composed of sampling points optimization and 3D wideband spectrum situation recovery. Therein, quadrature and right-triangular (QR) block pivoting based spatial sampling matrix optimization algorithm considers every frequency priority of each location jointly to select dominant sampling locations. Alternating direction method of multipliers is further applied to iteratively solve the situation recovery. Lastly, in-depth numerical simulations demonstrate the spectrum situation recovery performance, which proves the proposed 3D compressed wideband spectrum mapping scheme greatly reduces the number of measurements, while achieving a high level of wideband spectrum mapping accuracy.

Identifying threats, cybercrime and digital forensic opportunities in Smart City Infrastructure via threat modeling

Technological advances have enabled multiple countries to consider implementing Smart City Infrastructure to provide in-depth insights into different data points and enhance the lives of citizens. Unfortunately, these new technological implementations also entice adversaries and cybercriminals to execute cyber-attacks and commit criminal acts on these modern infrastructures. Given the borderless nature of cyber attacks, varying levels of understanding of smart city infrastructure and ongoing investigation workloads, law enforcement agencies and investigators would be hard-pressed to respond to these kinds of cybercrime. Without an investigative capability by investigators, these smart infrastructures could become new targets favored by cybercriminals.To address the challenges faced by investigators, we propose a common definition of smart city infrastructure. Based on the definition, we utilize the STRIDE threat modeling methodology and the Microsoft Threat Modeling Tool to identify threats present in the infrastructure and create a threat model which can be further customized or extended by interested parties.Next, we map offences, possible evidence sources and types of threats identified to help investigators understand what crimes could have been committed and what evidence would be required in their investigation work. Finally, noting that Smart City Infrastructure investigations would be a global multi-faceted challenge, we discuss technical and legal opportunities in digital forensics on Smart City Infrastructure.

Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN

Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a dominant research area among several fields in the domain of artificial intelligence. Traffic signal detection is a key module of autonomous vehicles where accuracy and inference time are amongst the most significant parameters. In this regard, the aim of this study is to detect traffic signals focusing to enhance accuracy and real-time performance. The results and discussion enclose a comparative performance of a CNN-based algorithm YOLO V3 and a handcrafted technique that gives insight for enhanced detection and inference in day and night light. It is important to consider that real-world objects are associated with complex backgrounds, occlusion, climate conditions, and light exposure that deteriorate the performance of sensitive intelligent applications. This study provides a direction to propose a hybrid technique for TLD not only in the daytime but also in night light.

Concept of Delivery System in the Smart City Environment

Regarding to the smart city infrastructures, there is a demand for big data processing and its further usage. This data can be gained by various means. There are many IoT devices in the city, which can communicate and share the information about the environment which they are situated in. Moreover every personal mobile device can also participate in this process and help to gain data via various applications. Every app provides the useful data, enabling the location and data sharing. This data can be further processed and used for improving the city infrastructure, transport or other services. We designed the system for shared delivery process, which can help to achieve the described situation. It consists of frontend and backend part. The frontend part, multiplatform mobile app, represents the graphical interface and the backend part represents the database for the gained data.

Leveraging UAVs to Enable Dynamic and Smart Aerial Infrastructure for ITS and Smart Cities: An Overview

Micro-unmanned aerial vehicles (UAVs), also known as drones, have been recognized as an emerging technology offering a plethora of applications touching various aspects of our lives, such as surveillance, agriculture, entertainment, and intelligent transportation systems (ITS). Furthermore, due to their low cost and ability to be fitted with transmitters, cameras, and other on-board sensors, UAVs can be seen as potential flying Internet-of-things (IoT) devices interconnecting with their environment and allowing for more mobile flexibility in the network. This paper overviews the beneficial applications that UAVs can offer to smart cities, and particularly to ITS, while highlighting the main challenges that can be encountered. Afterward, it proposes several potential solutions to organize the operation of UAV swarms, while addressing one of their main issues: their battery-limited capacity. Finally, open research areas that should be undertaken to strengthen the case for UAVs to become part of the smart infrastructure for futuristic cities are discussed.

Establishing IoT Cyber Hygiene Frameworks with Continuous Monitoring and Risk Assessment in Smart City Infrastructures

This study shows a cybersecurity design for Smart City infrastructures that is made up of five programs that work together. There are several tools that work together to make a dynamic and complete strategy. These are Continuous Threat Intelligence Feeds Integration (CTIFI), Machine Learning Anomaly Detection (MLAD), Vulnerability Scanning and Patch Management (VSPM), Network Segmentation and Access Control (NSAC), and Incident Response Planning (IRP). The framework's ablation study shows how important each method is, focusing on how they work together to solve important cybersecurity problems. Comparative tests show that the suggested method is better than others in terms of being able to be used on a larger scale, being accurate, and being cost-effective. For instance, waterfall, bullet, and funnel charts show patterns of scalability, while bar and line charts show signs of dynamic performance. The suggested framework is flexible enough to adapt to new cybersecurity threats thanks to its iterative and linked design. It provides a proactive and effective way to protect Smart City IoT environments.

Self-sensing Sustainable Cementitious Mixtures Incorporating Carbon Fibres

This paper focuses on sustainable cementitious composites in terms of their conductivity, hydration and self-sensing properties, which are key features for smart city infrastructures. Smart cities have massive infrastructures that are interconnected, transmitting data and information for health-monitoring and performance optimization. In this regard, having them made of sustainable building materials (concrete) that are also sufficiently conductive, will be a suitable solution for structures’ performance. The studied sustainable cementitious mixtures are made by sea components (sea water and sea sand), which are abundant resources. The primary goal of this study is to improve the mixes’ electrical conductivity and sensitivity. To achieve this goal, milled carbon fibres (MCFs) and chopped carbon fibres (CCFs) in 6 different proportions were added to the cementitious mixes. The experimental study is divided into impedance spectroscopy to study the conductivity and hydration development, and self-sensing properties, conducted on various mix designs. The results show that incorporating sea components improve the electrical conductivity of the mixes by 40-50%. Further improvements were achieved by adding MCF as it shows a remarkable reduction by 60% compared to the plain ss-sw (sea sand and sea water) samples. Adding CCF improved the conductivity even further and resulted in sample’s resistivity as low as 53 Ωcm after 1 year of curing time.

A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure

A Review of Energy Hole Mitigating Techniques in Multi-Hop Many to One Communication and its Significance in IoT Oriented Smart City Infrastructure

ПЕРСПЕКТИВИ РОЗВИТКУ ГРОМАДСЬКОГО ТРАНСПОРТУ В КОНТЕКСТІ СТВОРЕННЯ SMART-ІНФРАСТРУКТУРИ МІСТ УКРАЇНИ

The article explores the main directions and principles regarding the prospects for the development of public transportation in Ukraine in the context of creating a smart city with advanced smart infrastructure in the short and medium term. The possibility of consolidating the entire existing transportation potential of the country into a unified E-transport complex is envisaged, discussing the advantages and weaknesses of such transformations. Drawing on international experience and domestic achievements, the article identifies key directions for the future development of public transportation in the context of creating smart city infrastructure in Ukraine. The necessity of applying smart city principles to the post-war period of urban public transportation and transportation infrastructure recovery is demonstrated, emphasizing the need to make public transportation infrastructure truly «smart», aligned with the challenges of today and oriented towards integration with the transportation infrastructure of EU cities. The article discusses general and some specific issues that need to be addressed on the path to creating E-transport. Possible sources of funding are also discussed. The article proposes an organization that can lead the implementation of all necessary measures to create E-transport in Ukraine. It has the experience and all the necessary capabilities and tools for this purpose. In the conclusions, it is emphasized that digitization in transportation (especially in urban public transportation) will provide access to greater opportunities, support local economic development and the well-being of communities and citizens, optimize costs, ensure rapid response to emergency situations, and simplify interaction between different stages of the transportation management process, ultimately improving the efficiency of the entire transportation system in the country. However, the article points out not only the advantages but also the drawbacks and potential risks associated with these processes, including the lack of a regulatory framework for the development of smart infrastructure and the absence of an education system for the training and retraining of personnel involved in the processes under consideration.

Modeling Real-Life Urban Sensor Networks Based on Open Data

Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and metropolitan areas. As the trend continues to expand, the need to efficiently monitor and manage smart city infrastructure, public transportation, service vehicles, and commercial fleets has become of higher importance. This, in turn, requires new methods for dissemination, collection, and processing of data from massive number of already deployed sensing devices. In order to transmit these data efficiently, it is necessary to optimize the connection structure in wireless networks. Emerging open access to real data from different types of networked and sensing devices should be leveraged. It enables construction of models based on frequently updated real data rather than synthetic models or test environments. Hence, the main objective of this article is to introduce the concept of network modeling based on publicly available geographic location data of heterogeneous nodes and to promote the use of real-life diverse open data sources as the basis of novel research related to urban sensor networks. The feasibility of designed modeling architecture is discussed and proved with numerous examples of modeled spatial and spatiotemporal graphs, which are essential in opportunistic routing-related studies using the methods which rely on graph theory. This approach has not been considered before in similar studies and in the literature.

Understanding citizen perceptions of AI in the smart city

Artificial intelligence (AI) is embedded in a wide variety of Smart City applications and infrastructures, often without the citizens being aware of the nature of their “intelligence”. AI can affect citizens’ lives concretely, and thus, there may be uncertainty, concerns, or even fears related to AI. To build acceptable futures of Smart Cities with AI-enabled functionalities, the Human-Centered AI (HCAI) approach offers a relevant framework for understanding citizen perceptions. However, only a few studies have focused on clarifying the citizen perceptions of AI in the context of smart city research. To address this gap, we conducted a two-phased study. In the pre-study, we explored citizen perceptions and experiences of AI with a short survey (N = 91). Second, scenario-based interviews (N = 7) were utilized to gain in-depth insights of citizen perceptions of AI in the Smart City context. Five central themes were recognized: (1) I don’t like them monitoring me, (2) I want maximum gain for minimum effort, (3) I don’t want AI to mimic people, (4) I’ll avoid using AI if I consider the risk too high, and (5) I don’t need to be concerned about AI. These offer an idea of human-centered requirements worth considering while designing AI applications for future Smart Cities.

Review of Digital Twins for Constructed Facilities

Technological advances have enabled the monitoring and control of construction operations and assets remotely. Digital twins, based on computational modeling, have enabled the creation of a digital map for physical structures. Research on digital twins (DTs) for constructed facilities projects has gained widespread traction in the industry. While these applications have increased over the years, there has been sparse review of them. This paper systematically reviews the applications of digital twins in construction using content analysis. We identified and analyzed 53 academic journal and conference papers, which revealed several DT applications that could be categorized into nine areas: lifecycle analysis, facility management, energy, education, disaster, structural health monitoring, DT for cities, infrastructure management, and miscellaneous. This enables the visualization of the current state of DT, comparison with the desired state, and possible integrations with other technologies. Among the observed benefits of DTs are the ability to increase engagement and collaboration, reduce construction and operating costs, reduce human error, automate energy demand, manage assets throughout their lifecycle, and apply structural health monitoring. It also enables the collection of real-time data on an asset’s status, history, maintenance needs, and provides an interactive platform for managing an asset. Future directions include addressing how to standardize data acquisition as well as the semantic interoperability and heterogeneity of data. Additionally, modeling human cognitive processes as well as spatiotemporal information would be beneficial to a smart city and other infrastructure systems, especially in disaster situations.