Post-Quantum Link-based Ring Signature Model and Artificial Bee Colony (ABC) Algorithm for Effective Data Management in IoT-based Smart Cities

The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

Chapter 5 - Blockchain-based energy-efficient smart green city in IoT environments

This chapter aims to explore the application of Internet of Things (IoT) and machine learning (ML) in assessing the planning and management of smart cities and new towns. Additionally, we have discussed the possible solutions that combine IoT and ML opportunities in this field. Similarly, our results support that ML and IoT optimize resources, address environmental challenges and enhance citizen well-being by using real-time data and predictive models. However, challenges such as interoperability, cybersecurity, data protection, algorithmic bias and high costs must be addressed. Finally, we support that by adopting transparent governance and innovation, smart cities and new towns can build resilient urban ecosystems that prioritize the needs of their residents and ensure equitable development.

As Smart City (SC) infrastructures evolve rapidly, securing critical systems like smart water management (SWM) becomes paramount to protecting against cyber threats. Enhancing the security, sustainability and execution of conventional schemes is considered significant in developing smart environments. Intrusion detection systems (IDS) can be effectively leveraged to realise this security objective in an Internet of Things (IoT)‐based smart environment. This research addresses this need by proposing a novel IDS model called TabNet architecture optimised using Sailfish Optimisation (SFO). The TabNet‐SFO model was specifically developed for SWM in SC applications. The proposed IDS model includes data collection, preprocessing, feature selection and classification processes. For training the model, this research used the CIC‐DDoS‐2019 dataset, and for evaluation, real‐time data collected using an IoT‐based smart water metre are used. The preprocessing step eliminates unnecessary features, cleans the data, encodes labels and normalises the applied datasets. After preprocessing, the TabNet model selects significant features in the dataset. The TabNet architecture was optimised using the SFO algorithm, which allows hyperparameter tuning and model optimisation. The proposed model demonstrated improved detection accuracy and efficiency on both the simulated and real‐time datasets. The model attained a 98.90% accuracy, a 98.85% recall, a 98.80% precision, a 98.82% specificity and a 98.78% f1 score on the CIC‐DDoS dataset and a 99.21% accuracy, a 99.02% recall, a 99.05% precision, a 99.10% specificity and a 99.18% f1 score on real‐time data. Compared to existing models, the TabNet‐SFO model outperformed all existing models in terms of performance metrics and validated its efficiency in detecting attacks.

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.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

Internet of things (IoT) paradigm is becoming ubiquitous in everyday life. IoT-based systems are increasingly getting deployed in generic household, healthcare, public utilities, etc., related application. The reason for such rapid acceptance is in majority due to the internet's feature of connect from anywhere. However, due to some weaknesses inherent with the internet itself, IoT deployments also face certain challenges. Though there is a growing number of IoT-related reference architectures, frameworks, guidelines, platforms and standards, majority of them are yet to address the concerns due to internet outage. Furthermore, such growing numbers of architectures, frameworks, etc., also means bountiful amounts of both opportunities and risks for IoT vendors and original equipment manufacturers (OEMs). Of course, the IoT-device consumers also run the risks if the purchased products frameworks incompatible. We present One IoT version 2 (v2), an IoT framework and protocol that addresses the internet outage scenario. Similar to version 1 (v1), One IoT v2 is also forward compatible.

PurposeThe purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities.Design/methodology/approachThis research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment.FindingsThe outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India.Originality/valueThe paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions

The application of sensor networks in control and management of smart cities are increasing rapidly. The Internet of Things (IoT) platforms are becoming a major infrastructure component of the smart cities. The requirement for development of Web of Things (WoT) architectures and patterns have significant importance for optimal management and decision support in the smart city. Due to the fact that the sensor networks in even a small city produce a very large amount of data, manual interpretation of this data is not feasible. In recent years machine learning algorithms such as deep learning give the computer systems the ability to interpret and annotate this big data stream and to produce patterns which can help decision makers. This high-level interpretation is often presented in form of dashboards in the composition layers of the WoT. In this paper a deep learning based multisensor dashboard for decision support in the smart city is presented. The proposed dashboard is capable of interpretation and decision level fusion on sensor network data. Simulation results show that the proposed model is a suitable solution for making decisions in the IoT framework for the smart city.

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities.

The rapid development of Internet of Things (IoT) technology has significantly transformed the conceptualization and implementation of smart city applications. This study aims to analyze the performance of Internet of Things (IoT) architecture in supporting smart city applications across various urban service domains, including transportation, environmental monitoring, public safety, and energy management. The research focuses on evaluating key performance indicators such as latency, throughput, scalability, reliability, and energy efficiency within a multilayer IoT architecture consisting of perception, network, middleware, and application layers.A quantitative experimental approach was employed by simulating smart city scenarios using heterogeneous sensors and communication protocols. Performance metrics were collected and analyzed under varying traffic loads and data transmission frequencies. The results indicate that network latency and bandwidth utilization significantly affect real-time data processing capabilities, particularly in time-sensitive services such as intelligent traffic systems and emergency response monitoring. Furthermore, the study found that edge computing integration enhances response time and reduces cloud dependency, thereby improving overall system efficiency and scalability.The findings suggest that optimizing network configuration, adopting lightweight communication protocols, and implementing distributed processing mechanisms are critical factors in enhancing IoT architectural performance for smart city applications. This research contributes to the development of a more reliable and efficient IoT infrastructure framework to support sustainable and data-driven urban governance. Future studies are recommended to explore security resilience and interoperability challenges in large-scale smart city deployments.

Purpose: The purpose of this article is the integration of Internet of Things (IoT) devices and deep learning techniques have been investigated to enhance smart city applications. This investigation addresses a critical challenge: the absence of standardized methods for data collection, processing, and analysis that optimize the interplay between these technologies. Materials and Methods: The research design employed in this study is a qualitative approach. Using extensive data acquisition from IoT sensors, urban infrastructure metrics, and evaluations of deep sustainability outcomes. This data is processed using deep learning algorithms to provide actionable insights. This study underscores the importance of interdisciplinary collaboration in the advancement of smart city solutions, as it facilitates more responsive and adaptive healthcare services in smart cities. Findings: The findings of this research reveal that the synergistic application of IoT and deep learning streamlines data-driven decision-making processes and increases operational efficiencies within urban healthcare systems. The study identified that the deep-learning models utilized large datasets, which holds promise for real-time analytics in urban environments. Implications to Theory, Practice and Policy: Moreover, it contributes to theoretical frameworks that elucidate the integration pathways for IoT and deep learning in smart cities, thus filling key gaps. Key findings revealed that by leveraging IoT for data acquisition and utilizing deep learning for data analysis, cities can improve urban management functions, such as traffic control, public safety, and resource allocation.

Smart city can be taken as the biggest implementation of Internet of things (IoT) applications. Smart city technologies promote cloud-based and IoT-based services in which real-world user interfaces use smart phones, sensors, RFIDs, etc. The IoT ecosystems cannot properly communicate between them. So it needs a bridge between them to fill the global common approach. Implementation of this can be done with the help of information and communication technologies (ICT) which may result in enhancing quality, interconnection, and the performance of various urban services. It highlights the need for cloud computing and IoT technologies and both have a major impact on how we build and deploy smart applications for it. There are still numerous difficulties that remain with regard to its deployment due to its varying requirements. In this paper, authors tried to converge the two domains that are cloud computing and IoT for smart city application deployment and also present the identified security threat types and their countermeasures in the context of smart city.