Smart City Internet Of Things Research Articles

The Impact of IoT on Smart City Infrastructure in India

Today, we live in a society where digital communication systems are so advanced that by the mid 2050’s the world will be completely filled by smart buildings on the internet highway like those portrayed in science fiction movies. This is possible because of tech savvy tiny sensors or Internet of Things (IoT) devices embedded in the smart network in order to collect meaningful information about the citizens and their interaction with the ubiquitous environment. Smart sustainable cities are modelled on the Internet of Things blending ICT for better standard of living and citizen services. IoT will modify indoor temperatures and switch off unnecessary lights in homes, detect snags in equipment and fix machines remotely, and provide commuters with information on traffic jams on the go. IoT in smart cities can be implemented by the development authorities but the stake holders of this task are citizens, the municipal government and private organizations engaged in this partnership. Using sophisticated ICT systems makes the city smarter and improves community building and connectivity by interweaving all the components of a city. Privacy and security are the two watch words when it comes to capturing citizens’ data. This data needs to be anonymised before it reaches the civic authorities. This study evaluates the advantages and disadvantages of using IoT in Indian smart cities after discussing an international case study of a smart sustainable city.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.

Comprehensive Review of Smart Cities using IOT in Coud

The rapid growth of urbanization necessitates smarter and more sustainable city management. The convergence of Internet of Things (IoT) devices and cloud computing offers a transformative solution for building Smart Cities. This review explores the integration of IoT and cloud technologies in creating intelligent urban infrastructure. It examines how data collected from a network of sensors and devices across the city can be leveraged to improve various aspects of urban life, including traffic management, energy efficiency, public safety, waste management, and parking. The paper discusses the key benefits of cloud computing for Smart City IoT applications, such as scalability, centralized data management, cost-effectiveness, security, and advanced analytics capabilities. Additionally, it explores the challenges associated with implementing Smart City IoT solutions, such as data privacy concerns, cybersecurity risks, and the need for interoperability between different devices and platforms. Finally, the review concludes by highlighting the potential of Smart City IoT powered by cloud computing to create a more efficient, sustainable, and livable future for our cities.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios. Evaluation on the UNSW-NB15 dataset demonstrates the effectiveness of the proposed approach, with comparative analysis showcasing its superiority over existing techniques.

Governing Smart City IoT Interventions: A Complex Adaptive Systems Perspective

Urban infrastructure systems (UIS) encompass physical infrastructure and human stakeholders, delivering essential services through distributed flow networks. These systems exhibit properties of Complex Adaptive Systems (CAS), such as self-organization and adaptivity, which makes them resilient to perturbations from the outside. Smart city strategies advocate for external technology interventions to improve the efficiency and sustainability of UIS. Internet of Things (IoT) is a key enabler for such strategies which helps to gather data from various infrastructure components and optimize flow networks in UIS. Although beneficial, such interventions often give rise to ethical concerns, underscoring the need for appropriate governance measures. In this study, we adopt a CAS perspective to analyse the ethical concerns of a proposed smart city IoT platform in the cities of Karnataka, India. Based on our analysis, we present a framework to aid city authorities in formulating appropriate strategies to govern Smart City IoT interventions in general.

The Internet of Things (IoT) in World Practice: Review and Analysis

This article explores the contemporary realm of the Internet of Things (IoT) and its influence on society and the economy. The primary emphasis lies in examining the principal patterns within the realm of IoT, encompassing the escalation of connected device quantities and the utilisation of 5G networks. The article encompasses various facets, including an overview of the evolution and possibilities of IoT, encompassing the advancement of intelligent urban areas and the influence of 5G networks. The analysis of threats and vulnerabilities related to this technology focuses on detecting common flaws, including weak passwords, unsecured network services, and inadequate privacy measures.
 Furthermore, the essay presents concrete hazards in diverse sectors that employ IoT technology. Particular emphasis is given to potential cybersecurity vulnerabilities in healthcare, manufacturing, agriculture, retail, transport and logistics, energy, and smart cities. These businesses have distinct challenges and hazards due to incorporating IoT technologies.
 The authors also analyse the substantial financial commitment to IoT and its profound influence on the worldwide economy, including data on the expansion of the IoT market and investments in IoT technologies and smart cities. The findings emphasise that the Internet of Things (IoT) substantially influences society and the economy. However, it necessitates meticulous consideration of cybersecurity and privacy concerns. The authors underscore the significance of creating efficacious cyber security policies to safeguard against the possible hazards presented by IoT technology.
 
 

Dynamic Resource Management in MEC Powered by Edge Intelligence for Smart City Internet of Things

Dynamic Resource Management in MEC Powered by Edge Intelligence for Smart City Internet of Things

Smart Cities: AI and IoT Integration for Urban Sustainability

The rapid growth of cities is putting immense pressure on resources and infrastructure. In response, the concept of smart cities has emerged, aiming to leverage technology to improve the efficiency and sustainability of urban environments. Artificial intelligence (AI) and the Internet of Things (IoT) are two key technologies playing a critical role in the development of smart cities. This paper explores the integration of AI and IoT for urban sustainability. It discusses the various applications of AI and IoT in different aspects of urban life, such as transportation, energy, waste management, and public safety. The paper also highlights the challenges and opportunities associated with integrating AI and IoT in smart cities.

Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication

Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things, which enables gadgets to connect with one another mostly without human involvement, is made possible by AI. In line with this, The Ad Hoc Routing Function (ARF) AI computation is used for multi-rule simplification, the use of Adaptive Cloud Computing Virtual Machine Asset Allotment Technique (ACC-VMRA) is advised. To confirm its viability, the applied developments of IoT and CC in smart cities is examined and duplicated. The experiment results show that the recommended enhancement calculation is more productive than other currently used methods.

Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environments

Networks within the Internet of Things (IoT) have some of the most targeted devices due to their lightweight design and the sensitive data exchanged through smart city networks. One way to protect a system from an attack is to use machine learning (ML)-based intrusion detection systems (IDSs), significantly improving classification tasks. Training ML algorithms require a large network traffic dataset; however, large storage and months of recording are required to capture the attacks, which is costly for IoT environments. This study proposes an ML pipeline using the conditional tabular generative adversarial network (CTGAN) model to generate a synthetic dataset. Then, the synthetic dataset was evaluated using several types of statistical and ML metrics. Using a decision tree, the accuracy of the generated dataset reached 0.99, and its lower complexity reached 0.05 s training and 0.004 s test times. The results show that synthetic data accurately reflect real data and are less complex, making them suitable for IoT environments and smart city applications. Thus, the generated synthetic dataset can further train models to secure IoT networks and applications.

Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study

Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.

A Multi-Constraints Routing Scheme for MANET-assisted IoT in Smart Cities

The fifth-generation mobile network (5G) provides extreme throughput and extremely low latency, which enables the Internet of Things (IoT) era and a series of smart IoT ecosystems. The widespread equipping of Device-to-Device (D2D) modules for vehiculars allows transmitting directly between devices without relying on central devices such as access points or base stations. This is the foundation for the shaping of mobile ad hoc communications, so-called MANETs. The combination of MANETs and IoT technology has led to the development of MANET-assisted IoT applications, which offer unprecedented capabilities. However, due to the mobility of network nodes, routing is one of the main challenges in these networks. To address this problem, we propose a multi-constraints routing schema to enhance the performance of MANET-assisted IoT systems. Our simulation experiments show that the proposed solution significantly outperforms traditional routing solutions in terms of performance such as latency, packet delivery ratio, and throughput.

Leveraging AI‐enabled 6G‐driven IoT for sustainable smart cities

SummaryMany scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low‐latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost‐efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain‐enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in‐depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.

Technology Enabled Intelligent Solution in Human Resource Management for Smart Cities

The foundation of smart cities is based on an autonomous and decentralized architecture, which consists of sophisticated information and communication technologies (ICT) in convergence with technology enabled solution to improve the business management process in industry 4.0. This study tends to examine the adoption of blockchain technologies (DLT) in the human resource management (HRM) of organizations in building solutions for IOT (Internet of things) smart cities. The current study explores a unique set of factors selected from the extensive literature and acquired information from fifteen experts having significant experience of blockchain technology in their respective organizations. An integrated fuzzy analytic hierarchy process (F-AHP) is applied to prioritize the identified success factors. Further, the modified decision-making trial and evaluation laboratory (M-DEMATEL) method is utilized to represent the complicated causal relationships among different sub-factors on blockchain-HRM integration. The findings show the application of blockchain will foster a paradigm change in IOT based smart communities, where recruiters verify the candidate credentials including education, skills, and work experience. The payroll managers would determine the more effective way to make work less complex and moderate, enabling timelier payments to global employees. Furthermore, DLT would enhance the employee learning records and update the real-time information in HRM database technologies. Thus, providing a detailed guide for future Industry 4.0 developers about how blockchain can improve the next generation of industrial applications. The developed method can help the decision-makers and provide a foundational view to examine the benefits of implementing blockchain technology in the HRM setting of an organization before they choose to integrate in order to enhance Industry 4.0 technologies. This research will be a novel attempt to synthesize the key factors and subfactors about technology enabled solution within the intelligent HRM process, shedding light to rethink HRM strategies to incorporate blockchain technology in organizations.

Application Research and Implementation of VR in the Integration of Culture and Tourism Under the Background of Smart City Internet of Things

Application Research and Implementation of VR in the Integration of Culture and Tourism Under the Background of Smart City Internet of Things

A comparative study of low and high resolution infrared cameras for IoT smart city applications

The goal of smart cities is to improve efficiencies, enhance sustainability, advance quality of life and reduce energy consumption. One of the key factors to accomplish a smart city involves the use of IoT and information technology infrastructure which can be described as the foundation of a smart city. Its effective implementation will allow the city to meet its wide range of requirements while being able to respond to innovations, such as advanced sensors, analytic tools, measurement, and artificial intelligent based solutions. This paper investigates and compares between the use of low and high resolution infrared sensors as part of the Internet of Things (IoT) to estimate crowds in cities to enhance and optimise the efficiency of the transportation process and other public services for low density scenarios. A case study was conducted in Nottingham city at one of the tram stops. An experimental methodology is used where different number of people are captured and the results are compared using different image processing techniques. The findings show that both technologies are useful in the estimation of crowd density, however, the high resolution camera has been found to be more accurate in estimating the number of people albeit it is more expensive for the integration into infrastructures. The practical implication is that low-cost and low resolution infrared cameras could provide reasonable results. However, for higher accuracy, high resolution infrared cameras will be needed; and they are potentially more expensive. So a compromise might be needed between cost and performance to encourage the installation of more IoT systems using infrared technologies.

SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair

The modern innovation called the Internet of Things (IoT) empowers individuals to connect to anybody and anything at any point, wherever. The application of the IoT in smart cities concerning smart healthcare management can improve patient welfare, user acceptance, the standard of living, and accurate illness monitoring. Powered wheelchairs (PW) with sensors, computers, and other connected assistive technologies are called smart wheelchairs. Smart wheelchairs with sensing abilities are intended to offer universal connectivity using cloud and edge computing technology. Numerous outstanding people were impacted by paralyzing phenomena, including Stephen Hawking and Max Brito. The issue of legitimacy is one of the most important difficulties in e-health applications, because of how sensitive the technology is, and this needs to be appropriately handled. To safeguard the data transport, usage, and interchange between sensor nodes/smart wheelchairs and servers, e-health applications require an authentication method. As all conversations use wireless channels, e-health apps are exposed to various vulnerabilities. Additionally, the IoT has limited computational and power capacity limitations. To combat the various security risks, the present research offers a user authentication technique that is efficient and ensures anonymity. The suggested method creates a safe connection for the authorized entity and forbids unauthorized entities from accessing the Internet of Things sensor nodes. The suggested approach has lower communication and computation overheads than the traditional techniques, making it more effective. In addition, the security verification of the presented protocol is scrutinized through AVISPA. The proposed scheme, on average, requires only 12.4% more computation cost to execute. Compared to the existing approaches, the suggested protocol’s extra computational cost can be compensated for by its enhanced security, while the suggested method’s communication cost is 46.3% smaller.

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.