Smart City Applications Research Articles

A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities

A Hybrid Optimized Intelligent Resource-Constrained Service Scheduling for Unified IoT Applications in Smart Cities

Sensing-Aware Machine Learning Framework for Extended Lifetime of IoT Sensors

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications that involve a plethora of sensing tasks. However, the BLE beacon network usually suffers from poor reliability and high maintenance costs due to the short-lived battery lifetime. Multiple works have attempted to extend the lifetime via energy harvesting hardware, adaptive advertising interval by user existence-aware operation, and energy-efficient routing schemes. However, few attempts were made to reduce the energy consumption related to sensing tasks. In light of this shortcoming, a sensing-aware framework is proposed to adjust the sensing task interval adaptively based on the predicted portion of changes of the sensor measurements. Furthermore, to estimate the impact of varying sensing task intervals on the amount of sensed information, a model that correlates energy and amount of information is proposed. The sensor portion of changes is predicted with a novel neural network, coined oracle-interpreter network, that significantly reduces the energy consumption while upkeeping a good prediction accuracy by leveraging two independent neural networks tailored for feature extraction and prediction tasks. The effectiveness of the proposed framework is verified by comprehensive simulations based on real-life data. The results demonstrate that the proposed framework can effectively reduce the energy consumption involved in sensing tasks up to 30%, machine learning tasks up to 60%, and finally, extend the lifetime up to 75%.

DDoS Attack Detection in SDN-Based Instrumented Ad Hoc Networks

The physical characteristics of today's urban intersection structures and the traffic flows caused by unplanned road intersections lead to many negative effects such as time/cash loss, stress, increased fuel consumption, and more. For this reason, many studies are being conducted on traffic management systems, an application of smart cities, in both academic and commercial circles. In recent years, it has been observed that the VANET (Vehicular Ad Hoc Networks) architecture, which easily enables communication between vehicles or with devices on the side of the field, thus transporting relevant traffic data to the center, is frequently used in these studies. When Software Defined Networking emerged as a new technology, it brought many advantages such as high availability, scalability, and performance, but also introduced new security vulnerabilities targeted by attackers. This research primarily focuses on a resource-based detection approach by combining the powers of Software Defined Networking and s-Flow-RT technology against Distributed Denial of Service Attacks. In the simulation study conducted within the scope of this work, an SDN (Software Defined Networking)-based DDoS (Distributed Denial of Service) attack was carried out, and changes in data before and after the attack were examined. Traffic for the DDoS attack was generated with the Hping3 application. The RYU controller (a component-based software-defined networking framework) was selected as the software-defined network controller to create software-defined networks, and the Mininet emulator was used. A traditional computer network's Ubuntu virtual machine was used to carry out the attack in the scope of the work.

The Effect of Smart City Applications on Smart City Life Inteniıon: The Moderating Role of Technology Anxiety

The human population living in cities in the world is constantly increasing. As a result of the increasing population, the use of digital technologies has become widespread to facilitate city life. The importance of a smart city has increased mainly due to the integration of artificial intelligence and Internet of Things technologies into cities. Therefore, this study aims to examine the effect of smart city applications on life intention and how technology anxiety moderates this effect. For this purpose, data were collected from 386 people using a questionnaire. The data were analysed with a structural equation model. As a result of the analyses, it was determined that smart public service, smart transportation and smart environmentalism practices significantly positively affect the intention to live in smart cities. As a result of the moderator analysis, it was found that technology anxiety has a moderating role in the effect of smart city applications on the intention to live in smart cities. Suggestions were made according to the research findings.

A scalable multi-density clustering approach to detect city hotspots in a smart city

In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.

Distance-Based Queuing for Scalable and Reliable Linear Wireless Sensor Networks in Smart Cities.

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes.

Performance analysis of deep learning-based object detection algorithms on COCO benchmark: a comparative study

This paper thoroughly explores the role of object detection in smart cities, specifically focusing on advancements in deep learning-based methods. Deep learning models gain popularity for their autonomous feature learning, surpassing traditional approaches. Despite progress, challenges remain, such as achieving high accuracy in urban scenes and meeting real-time requirements. The study aims to contribute by analyzing state-of-the-art deep learning algorithms, identifying accurate models for smart cities, and evaluating real-time performance using the Average Precision at Medium Intersection over Union (IoU) metric. The reported results showcase various algorithms’ performance, with Dynamic Head (DyHead) emerging as the top scorer, excelling in accurately localizing and classifying objects. Its high precision and recall at medium IoU thresholds signify robustness. The paper suggests considering the mean Average Precision (mAP) metric for a comprehensive evaluation across IoU thresholds, if available. Despite this, DyHead stands out as the superior algorithm, particularly at medium IoU thresholds, making it suitable for precise object detection in smart city applications. The performance analysis using Average Precision at Medium IoU is reinforced by the Average Precision at Low IoU (APL), consistently depicting DyHead’s superiority. These findings provide valuable insights for researchers and practitioners, guiding them toward employing DyHead for tasks prioritizing accurate object localization and classification in smart cities. Overall, the paper navigates through the complexities of object detection in urban environments, presenting DyHead as a leading solution with robust performance metrics.

Scale Attentive Aggregation Network for Crowd Counting and Localization in Smart City

Recent years have witnessed a remarkable proliferation of applications in smart cities. Crowd analysis is a crucial subject, and it incorporates two subtasks in smart city systems, i.e. , crowd counting and crowd localization. Nevertheless, the presence of adverse intrinsic factors, i.e. , scale variation and background noise severely degrades the performance of counting and localization. Although great efforts have been made on separate research on counting and localization, few works are capable of performing both tasks at the same time. To this aim, the scale attentive aggregation network (SA 2 Net) is proposed to solve the problems of scale variation and background noise in crowd counting and localization tasks synchronously. Specifically, the SA 2 Net has two vital modules, namely multiscale feature aggregator (MFA) module and background noise suppressor (BNS) module. The MFA module is designed in a four-pathway structure, and it aggregates the multiscale feature so as to facilitate the correlation between different scales. The BNS module utilizes the contextual information between the input keys matrix and self-attention matrix to suppress the background noise. Furthermore, a global consistency loss combined with the Euclidean loss is utilized to optimize the network in counting and localization tasks. Extensive experimental results prove that the SA 2 Net outperforms the state-of-the-art competitors both subjectively and objectively.

Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning

The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.

WEBGPU: A NEW GRAPHIC API FOR 3D WEBGIS APPLICATIONS

Abstract. As smart city applications increase today, the importance of web-based representations of data that serve as inputs for these applications, such as 3D city models and digital twins, is growing. Since 2011, GPU hardware has greatly advanced, and detailed 3DCMs and digital twins have increased the size of the data to be displayed, which in turn increases performance requirements. WebGL, which couldn’t fully harness the capabilities and power of modern GPUs, began to struggle to meet the increasing performance demands over time and become a bit outdated. Consequently, a new graphic API named WebGPU was developed by W3C as a response to limitations of WebGL and went live with Chrome 113. Now, we have a brand-new graphic API called WebGPU which harness the full power of modern GPUs and more performant than WebGL. As WebGPU is a new graphic API, its potential enhancements and what it can bring over WebGL in the terms of WebGIS have not been examined yet. Hence, the main idea and contribution of this work is to investigate WebGPU in real-world use cases for WebGIS applications and discuss what it brings over WebGL. For this purpose, a side-by-side performance comparison has been made. The comparisons have been made in the terms of API differences and performance. And finally, an experiment has been carried out how much data can be rendered can be rendered at minimum 60fps in both APIs. The experiments show that WebGPU is more low-level and way more performant than WebGL and it has a lot to offer in the terms of WebGIS applications.

Blockchain for Big Data: Approaches, Opportunities and Future Directions

Abstract: The last several years have seen a significant increase in interest in big data across a range of scientific and engineering fields. Despite having several benefits and applications, big data still has some difficulties that must be overcome for a higher level of service, such as big data analytics, big data management, and big data privacy and security. Big data services and apps stand to greatly benefit from blockchain decentralisation and security features. In this article, we present an overview of blockchain for big data with an emphasis on current methods, possibilities, and upcoming trends. We begin by providing a succinct explanation of big data, blockchain, and the purpose of their integration. After that, we look at different types of blockchain assistance for big data, such as blockchain for security in big data collection, data privacy protection, storage, and collection. Next, we examine the latest work on the utilization of blockchain applications for big data across different industries, including smart grid apps and applications, smart city applications, and smart healthcare applications. A few illustrative blockchain-big data initiatives are given and discussed for a good understanding. Finally, difficulties and potential directions are examined to advance research in an exciting field.

NFTs in Smart Cities: Vision, Applications, and Challenges

Non-Fungible Tokens (NFTs) have recently received widespread attention as their market has experienced massive growth since early 2021. NFTs are blockchain-based tokens used to represent ownership of digital and physical assets in a manner that is transparent, traceable, tamper-proof, secure, and trustworthy. Although NFTs have enabled new applications in the gaming and art industries, their full extent and potential are yet to be explored. In this paper, we outline the vision of using NFTs in smart cities. We describe the main components of NFTs and how smart city applications, such as smart governance, smart services, smart economy, smart industry, smart environment, and smart mobility and transportation, can benefit from them. In addition, we show how leveraging NFTs can add value to the metaverse. Finally, we discuss research challenges that hinderthe adoption of NFTs in smart cities

Editorial for ambient intelligence and applications for smart environment and smart city

Editorial for ambient intelligence and applications for smart environment and smart city

PaSTG: A Parallel Spatio-Temporal GCN Framework for Traffic Forecasting in Smart City

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities are witnessing an increased deployment of sensors, resulting in the collection of extensive sensor data to provide more accurate insights into citywide traffic dynamics. Spatio-temporal graph modeling on large-scale traffic data is challenging due to the memory constraint of the computing device. For traffic forecasting, subgraph sampling from road networks onto multiple devices is feasible. Many GCN sampling methods have been proposed recently. However, combining these with STGCNs degrades performance. This is primarily due to prediction biases introduced by each sampled subgraph, which analyze traffic states from a regional perspective. Addressing these challenges, we introduce a parallel STGCN framework called PaSTG. PaSTG divides the road network into regions, each processed by an individual STGCN in a device. To mitigate regional biases, Aggregation Blocks in PaSTG merge spatial-temporal features from each STBlock. This collaboration enhances traffic forecasting. Furthermore, PaSTG implements pipeline parallelism and employs a graph partition algorithm for optimized pipeline efficiency. We evaluate PaSTG on various STGCNs using three traffic datasets on multiple GPUs. Results demonstrate that our parallel approach applies widely to diverse STGCN models, surpassing existing GCN samplers by up to 57.4% in prediction accuracy. Additionally, the parallel framework achieves speedups of up to 2.87x and 4.70x in training and inference compared to GCN samplers.

Smart surveillance using IoT: a review

In today’s modern society, video surveillance is a growing trend and it can revolutionize many aspects of technology, especially in future smart cities that will transform traditional surveillance systems into intelligent and interconnected networks. It may be difficult for even well-trained employees to process and respond immediately to monitored data. Moreover, IoT-enabled surveillance systems overcome the challenges and flaws of conventional passive monitoring techniques by offering real-time surveillance and automated notifications for suspicious activity, intrusions, or anomalies. Therefore, the objective of this article is to provide an Internet of Things (IoT)-based smart surveillance system that may successfully connect the ecosystem, resulting in enhanced monitoring for smart city services. The primary goal of this study is to provide a comprehensive review of several IoT-based surveillance techniques used in various smart city applications. The categorization of tasks for each section is as follows: to present the historical context overview; to examine the significance of IoT and its application in smart cities; to present a standardized architecture for IoT-based smart city surveillance; to highlight an authoritative and thorough review of current IoT-based smart surveillance systems; and to identify various research issues. The methods used are: statistical graph or chart approach, schematic, and timeline diagram. Conclusions. This article outlines numerous research challenges for future video surveillance that may be addressed by researchers. In summary, this comprehensive review provides a valuable and streamlined resource for future researchers exploring smart city surveillance through the IoT.

Dickson polynomial-based secure group authentication scheme for Internet of Things

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli’s Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

Energy-Efficient Dynamic MAC Protocol for M2M Communication

In the Internet of Things, which is vital for smart city applications, a vast network of battery-operated Machine Type Communication Devices (MTCDs) requires Machine Type Communication. These MTCDs connect directly to a network with a base station, facilitating a wide range of smart city functionalities. The development of energy-efficient communication protocols is essential to support the deployment of energy-constrained battery-operated devices. These protocols aim to minimize energy consumption, thereby enhancing network lifetime. Additionally, they must address scalability, reduce collisions and delays, and ensure a high Quality of Service (QoS) to effectively manage the vast number of devices involved. Clustering is a viable strategy to enhance scalability and energy efficiency in network communications. However, within this framework, particularly in the proposed Power Efficient Dynamic MAC protocol for M2M Communication (PMAC), there's still a need for energy-efficient communication between the Machine Type Communication Devices (MTCDs). This requirement is crucial to optimize the overall performance and sustainability of the network. In this paper, we analyses the Energy Efficient CSMA/CA (E-CSMA/CA) protocol, specifically developed for facilitating communication between Machine Type Communication Devices (MTCDs) in short-range communications. This analysis is set within the context of smart city applications, with a particular focus on smart metering. This approach underscores the importance of efficient, reliable communication protocols in modern urban infrastructure, especially for applications like smart metering that are integral to the smart city ecosystem.

Comprehensive systematic review of information fusion methods in smart cities and urban environments

Smart cities result from integrating advanced technologies and intelligent sensors into modern urban infrastructure. The Internet of Things (IoT) and data integration are pivotal in creating interconnected and intelligent urban spaces. In this literature review, we explore the different methods of information fusion used in smart cities, along with their advantages and challenges. However, there are notable challenges in managing diverse data sources, handling large data volumes, and meeting the near-real-time demands of various smart city applications.The review aims to examine smart city applications in detail, incorporating quality evaluation and information fusion techniques and identifying critical issues while outlining promising research directions. In order to accomplish our goal, we conducted a comprehensive search of literature and applied selective criteria. We identified 59 recent studies addressing machine learning (ML) and deep learning (DL) techniques in smart city applications. These studies were obtained from various databases such as ScienceDirect (SD), Scopus, Web of Science (WoS), and IEEE Xplore. The main objective of this study is to provide more detailed insights into smart cities by supplementing existing research. The word cloud visualisation of machine learning/deep learning and information fusion in smart cities papers shows a diverse landscape, covering both technical aspects of artificial intelligence and practical applications in urban settings. Apart from technical exploration, the study also delves into the ethical and privacy implications arising in smart cities. Moreover, it thoroughly examines the challenges that must be addressed to realise this urban revolution's potential fully.

The Impact of Database on Geographical Information System and Smart Cities

The research discusses the substantial influence of databases in smart cities, affecting various facets and people's lives. It emphasizes the impact on Information Technology (IT) spending and database features, allowing access from anywhere at any time. Key elements crucial for smart city implementation include sustainability, adaptability, governance, improved living conditions, resource management, and city amenities. The research highlights defined components of smart cities and their applications, such as healthcare, mass transit, education, and energy services. It also focuses on how integrating Geographic Information Systems (GIS) aids decision-making. The study emphasizes the use of cloud computing for smart city database systems, outlining its benefits in data collection, storage, and analysis across various cloud nodes and facilities. It also highlights industries such as health insurance, public transportation, smart buildings, and energy that benefit from these technologies in decision-making processes. The paper's objective is to review database applications in smart cities and explore the potential use of big data..

The Microverse: A Task-Oriented Edge-Scale Metaverse

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.