Smart City Infrastructure Research Articles

A Deep Learning Approach for Securing IoT Infrastructure with Emphasis on Smart Vertical Networks

As a result of the Internet of Things (IoT), smart city infrastructure has been able to advance, enhancing efficiency and enabling remote management. Despite this, this interconnectivity poses significant security and privacy concerns, as cyberthreats are rapidly adapting to exploit IoT vulnerabilities. In order to safeguard privacy and ensure secure IoT operations, robust security strategies are necessary. To detect anomalies effectively, intrusion detection systems (IDSs) must employ sophisticated algorithms capable of handling complex and voluminous datasets. A novel approach to IoT security is presented in this paper, which focuses on safeguarding smart vertical networks (SVNs) integral to sector-specific IoT implementations. It is proposed that a deep learning-based method employing a stacking deep ensemble model be used, selected for its superior performance in managing large datasets and its ability to learn intricate patterns indicative of cyberattacks. Experimental results indicate that the model is exceptionally accurate in identifying cyberthreats, exceeding other models, with a 99.8% detection rate for the ToN-IoT dataset and 99.6% for the InSDN dataset. The paper aims not only to introduce a robust algorithm for IoT security, but also to demonstrate its efficacy through comprehensive testing. We selected a deep learning ensemble model due to its proven track record in similar applications and its ability to maintain the integrity of IoT systems in smart cities.

Edge AI and On-Device Machine Learning For Real Time Processing

Edge Artificial Intelligence (Edge AI) and on-device machine learning represent significant advancements in computing paradigms, enabling real-time data processing directly at the edge of the network. This approach minimizes the need for centralized cloud-based processing, thereby reducing latency, enhancing privacy, and improving operational efficiency. This paper provides a comprehensive review of Edge AI and on-device machine learning, focusing on their applications, challenges, and future directions. Key applications include smart home devices, healthcare monitoring, autonomous vehicles, and smart city infrastructure, where local data processing facilitates immediate responses and decision-making. However, deploying AI models on edge devices introduces challenges related to model compression, limited computational resources, and power constraints. Security and privacy concerns also arise, necessitating robust data protection and privacy-preserving techniques. Furthermore, the paper explores emerging technologies such as 5G integration and advances in AI hardware, which are expected to drive future developments in Edge AI. Research directions are highlighted, emphasizing the need for new algorithms tailored to edge environments and enhancements in system robustness and reliability. By addressing these challenges and leveraging emerging technologies, Edge AI is poised to transform a wide range of sectors, offering enhanced real-time processing capabilities and paving the way for innovative applications.

Smart Cities with Big Data

With the rise of data-driven technology and the growing demand for sustainable urban development, the idea of smart cities has attracted a lot of attention recently. This study provides a comprehensive analysis of smart cities within the big data framework, emphasizing issues, problems, and reference models. The report starts off by outlining the essential elements of a smart city, focusing on how different data sources should be integrated and how data analytics is crucial for making well-informed decisions. It then explores the examination of current reference models, stressing their advantages and disadvantages in handling the intricacy of smart city infrastructures. The study also addresses the difficulties of using big data technology in urban settings, including concerns about data security, privacy, and governance. It also lists important factors to take into account when implementing big data projects in smart cities, stressing the need of stakeholder involvement, scalable infrastructure, and the creation of strong legal frameworks. Overall, this paper gives a thorough review of how big data will affect urban living in the future and offers insights into the key issues that must be resolved in order to realize the full potential of smart cities.

Towards a sustainable edge computing framework for condition monitoring in decentralized photovoltaic systems

In recent times, the rapid advancements in technology have led to a digital revolution in urban areas, and new computing frameworks are emerging to address the current issues in monitoring and fault detection, particularly in the context of the growing renewable decentralized energy systems. This research proposes a novel framework for monitoring the condition of decentralized photovoltaic systems within a smart city infrastructure. The approach uses edge computing to overcome the challenges associated with costly processing through remote cloud servers. By processing data at the edge of the network, this concept allows for significant gains in speed and bandwidth consumption, making it suitable for a sustainable city environment. In the proposed edge-learning scheme, several machine learning models are compared to find the best suitable model achieving both high accuracy and low latency in detecting photovoltaic faults. Four light and rapid machine learning models, namely, CBLOF, LOF, KNN, ANN, are selected as best performers and trained locally in decentralized edge nodes. The overall approach is deployed in a smart solar campus with multiple distributed PV units located in the R&D platform Green & Smart Building Park. Several experiments were conducted on different anomaly scenarios, and the models were evaluated based on their supervision method, f1-score, inference time, RAM usage, and model size. The paper also investigates the impact of the type of supervision and the class of the model on the anomaly detection performance. The findings indicated that the supervised artificial neural network (ANN) had superior performance compared to other models, obtaining an f1-score of 80 % even in the most unfavorable conditions. The findings also showed that KNN was the most suitable unsupervised model for the investigated experiments achieving good f1-scores (100 %, 95 % and 92 %) in 3 out of 4 scenarios making it a good candidate for similar anomaly detection tasks.

Development of a cost-effective optical network based on free space optical (FSO) and optical fiber links for enabling smart city infrastructure: A hybrid approach

Due to the gigantic rise in the use of high data rate-based applications and the ongoing transformation of Indian cities into smart cities, there is a need for a high-speed communication framework to handle the enormous data traffic. The radio frequency (RF) and microwave links seem to be incapable of offering sufficient bandwidth required by the data-hungry applications and consequently, the need for a more effective solution for tackling the issue of scarcity of bandwidth resources has become the necessity of current network infrastructure in India. In this study, a hybrid optical fiber-free space optical (OF-FSO) system for offering high-speed data services has been proposed to assist the implementation of smart city infrastructure in India. This study aims to inspect the system performance of the proposed model under the climatic conditions of mountainous, desert, flatland, and seaside regions of India. The proposed hybrid OF-FSO system comprises a fixed single-mode fiber (SMF) of 20 km and an FSO link (having a varied transmission range between 0–2 km). The proposed hybrid model can support an FSO link of 1.72 km for a fixed SMF of 20 km, corresponding to a bit error rate of the order of 10−4 for atmospheric conditions of the seaside location of Chennai and the range of FSO link varies from 1.32 km to 1.57 km for other locations of India, for the same length of SMF and bit error rate. Furthermore, the proposed hybrid OF-FSO has demonstrated a reduction of 30.8% in Capital Expenditure (CAPEX) cost in contrast to the conventional passive optical network (PON) system.

HPRoP: Hierarchical Privacy-preserving Route Planning for Smart Cities

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across multiple levels and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route-planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points ( endpoint location privacy ) and the route itself ( route privacy ). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by ≤ 20% in length from optimal shortest paths, with completion times within ∼ 25 seconds, which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy (∼ 1.0) and good route privacy (≥ 0.8).

Exploring bus tracking data to characterize urban traffic congestion

Quantification of traffic dynamics is a valuable tool for city planning and management. Metrics such as the vehicle average speed, travel time, delays, and count of stops, can be used to characterize mobility and traffic congestion in an area. However, effective study of mobility data is often hindered by the difficulty of gathering mobility data in a practical, inexpensive, and prompt way.In this work, we explore the use of city buses as mobility probes, using the existing smart city infrastructure deployed in Aveiro, Portugal. We propose a method for traffic congestion detection considering the low vehicle speed, low traffic flow and road occupancy close to its capacity. Three degrees of congestion are identified using the k-means approach; DBSCAN is used to characterize the typical level of congestion in a road. Using four-weeks of mobility data, it was possible to assess the congestion along the day and for the different days of the week; some road segments proved to be consistently prone to congestion. We also studied parameters of driving safety, considering speed and acceleration.In this work, we show that knowledge discovery can be applied to mobility data being collected by tracking buses, exploring data that is often collected for other purposes also to characterize traffic congestion. These methods can inform decision makers and are easily ported to other cities.

Study on Function and Appearance Design of Smart Street Lamps Based on Kansei Engineering: A Literature Review

The potential of smart cities to alleviate the challenges of urban development in relation to population, resources, and environment is widely recognized, making it a key urban development trend for the future. Smart street lamps (SSLs) are a crucial component of smart city infrastructure. However, their current unreasonable function settings and appearance design do not meet the emotional needs of residents and come at a high construction cost, resulting in decreased user satisfaction. Based on WOS and CNKI databases, 39 literatures on the aspects of theory, steps and technologies of KE, 32 literatures on the development, basic functions, construction, existing problems, and key technologies of SSLs, and 6 papers on street lamps functions or appearance design research based on KE be reviewed in this paper. Therefore, the application of KE method in SSL design be extensively reviewed, with emphasis on the future development direction of KE, the design principles of SSLs, and the implementation of KE in SSL design. This review aims to summarize the research gaps, future research directions, and future development trends of KE and SSL. Ultimately, the review concludes that the integration of KE in SSL design research is crucial to improve SSL products’ rationality, openness, and amicability, guided by scientific SSL design principles.

Defense and Deterrence as the Foundation of the A2/AD System in Smart City Air Defense

The extreme concentration of tangible and non-tangible resources in the environment of the Smart City calls for their securing and protection. In the case of threat emergence, the military would have to enter areas where local authorities cannot guarantee the security of Smart City assets. Thus, the question arises whether or not Smart Cities could potentially assist the military forces in performing their tasks better on the battlefield. Some military agencies are currently exploring the possibility of using the military in Smart Cities. For instance, the United States Army Research Laboratory (ARL) is studying whether smart city communications and infrastructure could be of use on the future battlefield. This paper aims to indicate the potential for expansion and integration of the anti-aircraft defense systems to serve the needs of urban defense, thereby allowing the achievement of air superiority and implementing the assumptions of A2/AD systems. In reference to the research objective formulated in such a way, discovering the answers to the following questions was deemed crucial: What is the state of the current Anti-Aircraft Defense (AAD) system of urban centers, in particular Smart Cities? What are the possibilities of expanding and improving this system? How can the SMART CITY system be integrated and serve as an element of strengthening the AAD system? In order to answer the indicated questions, this article was written with the use of qualitative method consisting of text and literature exploratory research and analysis, and comparative analyzes of Smart Cities and Air Defense in contemporary military operations. To achieve the assumed aim, the authors have also conducted the evaluation of Air Defense combat capabilities, as well as the changes that have occurred in the environment of Smart Cities. The authors used thematic analysis methods to interpret patterns and meanings in the data

Converged Security and Information Management System as a Tool for Smart City Infrastructure Resilience Assessment

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers to the practical application of the converged security theoretical principles presented in the publication to a functional sample, deployed and tested in practical conditions in context of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the resilience assessment context is defined. In the beginning, the general principles of security/safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment (CRA) method is presented for better understanding. Subsequently, Physical Security Information Management (PSIM) and Security Information and Event Management (SIEM) systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process, and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

A Survey of Video Surveillance Systems in Smart City

Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

Special Section: Consumer Healthcare Technologies in Smart Cities

This Special Issue takes an in-depth look into the recent developments in consumer technology that blend into everyone's health and safety in a smart city environment. Smart wearables, such as watches and wristbands, have been widely used in health monitoring in recent years. These connected consumer electronics devices not only provide indicative information for consumers' health management, but also play a vital role in communicable disease management and prevention. In the context of consumer technology as part of a smart city infrastructure, wireless communication and sensing technologies have evolved substantially over the past decade and have seen an accelerating trend of technical advances, particularly in the consumer healthcare sector. Optimization techniques have been developed significantly and smart devices have become more efficiently designed and implemented both for the betterment of individual consumers and health service providers.

Parametric Evaluation Techniques for Reliability of Internet of Things (IoT)

Reliability analysis aims to assess the dependability and performance of a system over time, considering factors such as device failures, communication issues, and data integrity. By integrating taxonomy and reliability analysis, researchers can systematically analyze and enhance the reliability of IoT systems by addressing specific components and categories identified in the taxonomy. IoT based on its reliable applications is classified among various categories such as smart home devices, smart watches, electric vehicles with IoT capabilities, industrial, healthcare, agriculture, soil monitoring sensors, smart city infrastructure, environmental monitoring devices, and smart energy management devices. The need for a consistent taxonomy was one of the problems recognized and examined in this paper even though IoT management technology is developing. To examine the reliability analysis of IoT systems, we created a brand-new taxonomy for IoT gadgets that places a strong focus on administration. We have released a completely new IoT reliability RBD model analysis at different gateways and Round-Trip Time is to be analyzed. It features an aircraft for dependability. Our goal is to help administrators of networks, as well as designers, put processes in place that will analyze and enhance the reliability of IoT systems. To implement techniques to increase the IoT system's reliability, we may utilize the management capabilities to keep an eye on the devices. ChatGPT can act as the conversational interface for ISABELA. By integrating ChatGPT with ISABELA's functionality, users can have natural language conversations with ISABELA, making the interaction more intuitive and user-friendly. A use case study of ISABELA has enhanced the position of the CHATGPT system in the IoT system which increase the efficiency and reliability of IoT designs and networks.

Performance Evaluation of an IEEE 802.15.4-Based Thread Network for Efficient Internet of Things Communications in Smart Cities

The increasing demand for Internet of Things (IoT) applications has resulted in vast amounts of data, requiring the utilization of big data analytics. The integration of big data analytics in IoT-based smart cities can greatly benefit from the development of wireless communication protocols, among which the Thread protocol has emerged as a promising option. Thread is IEEE 802.15.4 based and has advanced capabilities like mesh networking, IPv6 support, and multiple gateways providing no single point of failure. This paper presents the design and evaluation of a low-cost mesh network using Raspberry Pi, nRF52840 dongle, and OpenThread 1.2 (i.e., an open-source software implementation of the Thread protocol stack). The research elaborates on the hardware and software solutions used, as well as the network topologies adopted. To evaluate the performance of the developed system, extensive real-time tests are performed, considering parameters, such as jitter, packet loss, and round trip time. These tests effectively demonstrate the effectiveness of the Thread network. Furthermore, the impact of varying payload size and bitrate on the network is analyzed to understand its influence. The behavior of the multi-hop network is also examined under link failure scenarios, providing insights into the network’s robustness. Our findings provide valuable insights for researchers interested in designing low-cost and efficient mesh networks for various IoT applications, including home automation, building/campus monitoring systems, distributed industrial IoT applications, and smart city infrastructure.

Toward efficient vehicular-based virtual network infrastructure for smart cities

Future smart cities will undoubtedly generate and process an immense volume of data. Developing a smart network infrastructure is thus becoming a key component for the better development and efficient governance of smart cities. The main objective of this article is to examine the feasibility of utilizing moving vehicles to develop an efficient Vehicular-based Virtual Network Infrastructure (VVNI) for smart cities. The basic idea here is to divide the whole city into distinct cells, then predict and evaluate the vehicle eligibility to act efficiently as a centralized communication and information hub for each cell. Specifically, we present the basic framework of the proposed VVNI, and identify the corresponding efficiency requirements in terms of service availability, cost, and quality; then we introduce novel metrics to characterize the vehicle's ability to cope with these requirements. Several simulation scenarios were conducted to assess the efficiency of the proposed VVNI. The simulation results demonstrate how the proposed model outperforms existing models and improves system performance in three benchmark areas: throughput, packet delivery ratio, and latency, especially in medium- and high-density network scenarios with slower vehicle velocities. The simulation results are promising to further enhance the development of the proposed network infrastructure.

An actionable maturity planning model for smart, circular cities

In this paper, we propose a macroeconomics approach to assist decision-makers and stakeholders when considering the transition of a city towards the circular economy paradigm. We reflect on approaches that leverage smart city maturity models to capture the evolution and trajectory of a city in its quest for smartness. By adopting the cross-methodology borrowed from the macroeconomics domain, we illustrate how a maturity model can be actionable in the sense that it can reveal the equilibria of supply and demand of a given smart city dimension or parameter. To evaluate this methodology, we study a well-known use case in cybersecurity, specifically incident response, against crowdsourcing, a key enabler of a data-driven circular economy. Our findings confirm that when crowdsourcing is injected into smart city infrastructure, the city's maturity is expected to increase as the additional capabilities can contribute to addressing cybersecurity incidents.

Design and Implementation of Artificial Intelligence-based Real-time Pothole Detection for IoT Smart Infrastructure

The Internet of Things (IoT) has been extensively deployed for Smart Cities due to its ability to process many different and heterogeneous end systems. The IoT innovation encourages artificial intelligence applications to process data. In Smart City infrastructure, the road is a critical component of transportation infrastructure that supports the economic, social, and cultural things of community life and various aspects of community life. Road conditions affect a variety of community activities. Good roads enhance comfort and support local businesses. However, many roads remain in bad condition, such as potholes. Various methods have been attempted to identify potholes, especially the two-dimensional imaging method. This paper proposes the real-time Artificial Intelligence detection of potholes using the Convolutional Neural Network (CNN), which leverages the Edge Tensor Processing Unit (TPU) with the MobileNet SSD v2. The system was set up on a Jetson Nano with a few extras, including a camera and GPS, to support the IoT infrastructure. Evaluation for the model consists of device implementation, model evaluation, GPS position deviation, and on-road implementation. The effectiveness is confirmed through experiments using a system test-bed that generates ideal mAP off 0.22 and recall values.

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends.

As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

A Novel Algorithm of Safe-Route Traversal of Data for Designing the Secured Smart City Infrastructures

A Novel Algorithm of Safe-Route Traversal of Data for Designing the Secured Smart City Infrastructures

ATM Vehicle Location Privacy and Security Using End -To - End Quantum Key Crypto System

Internet-of-things (IoT) is the latest revolution in electronic industry after internet. Smart appliances, portable computing devices, mobile phones and handheld system dominates in IoT, because a large portion of world population use it. Major applications, mainly financial, e-commerce, information security and sensitive data-communication need special attention in terms of security. Device authentication, encryption, and key distribution are of vital importance to any Internet-of-Things (IoT) systems, such as the new smart city infrastructures. This is due to the concern that attackers could easily exploitthe lack of strong security in IoT devices to gain unauthorized access to the system or to hijack IoT devices to perform denial-of-service attacks on other networks. This creates a strong requirement for providing security solutions into these devices. Key Word: IOT; Communication; Data; Application; Server.