Blockchain-Based Cyberthreat Mitigation Systems for Smart Vehicles and Industrial Automation

Madaan, Gunjan; Bhushan, Bharat; Kumar, Raghvendra

doi:10.1007/978-981-15-7965-3_2

Cited by 19 publications

(4 citation statements)

References 31 publications

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“…Some of the sensors that are used to collect data are RFID, Infrared Sensor, Temperature Sensor, Bluetooth, humidity sensors, cameras, and so on. Sensors collect data with different matrices (Madaan et al, 2020; Sethi et al, 2020). Since data collection helps with decision making and automation, increased data collection vectors shall enhance a genuinely smart city.…”

Section: Smart City Backgroundmentioning

confidence: 99%

Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends

2021

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The emergence of smart cities and sustainable development has become a globally accepted form of urbanization. The epitome of smart city development has become possible due to the latest innovative integration of information and communication technology. Citizens of smart cities can enjoy the benefits of a smart living environment, ubiquitous connectivity, seamless access to services, intelligent decision making through smart governance, and optimized resource management. The widespread acceptance of smart cities has raised data security issues, authentication, unauthorized access, device-level vulnerability, and sustainability. This article focuses on the holistic overview and conceptual development of smart city. Initially, the work discusses the smart city idea and fundamentals explored in various pieces of literature. Further various smart city applications along with notable implementations, are put forth to understand the quality of living standards. Finally, the article depicts a solid understanding of different security and privacy issues, including some crucial future research directions.

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Section: Smart City Backgroundmentioning

confidence: 99%

Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends

2021

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“…For example, machine learning can automate decision-making in smart farming environments by predicting soil drought and crop productivity [46]. Smart vehicles also exist in almost all smart environments, including smart farming, smart factories, smart cities, and smart transportation systems, and we also see how Blockchain can secure the transactions between these smart vehicles [47].…”

Section: Smart Environmentsmentioning

confidence: 99%

Blockchain as privacy and security solution for smart environments: A Survey

Ebrahim¹,

Hafid²,

Elie³

2022

Preprint

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Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, the benefits of Blockchain are far beyond that. It has been recently used to support and augment many other technologies, including the Internet-of-Things (IoT). IoT, with the help of Blockchain, paves the way for futuristic smart environments, like smart homes, smart transportation, smart energy trading, smart industries, smart supply chains, and more. To enable these smart environments, IoT devices, machines, appliances, and vehicles, will need to intercommunicate without the need for a centralized trusted party. Blockchain can replace third trusted parties by providing secure means of decentralization in such trustless environments. They also provide security enforcement, privacy assurance, authentication, and other important features to IoT ecosystems. Besides the benefits of Blockchain-IoT integration for smart environments, other technologies also have important features and benefits that attracted the research community. Software-Defined Networking (SDN), Fog, Edge, and Cloud Computing technologies, for example, play an important role in enabling realistic IoT applications. Moreover, the integration of Machine Learning and Artificial Intelligence (AI) algorithms provides smart, dynamic, and autonomous decisionmaking capabilities for IoT devices in smart environments. To push the research further in this domain, we provide in this paper a comprehensive survey that includes state-of-the-art technological integration, challenges, and solutions for smart environments, and the role of Blockchain and IoT technologies as the building blocks of such smart environments. We also demonstrate how the level of integration between these technologies has increased over the years, which brings us closer to the futuristic view of smart environments. We further discuss the current need to provide general-purpose Blockchain platforms that can adapt to different design requirements of different applications and solutions. Finally, we provide a simplified architecture of futuristic smart environments that integrates all these technologies, showing the advantage of such integration.

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“…It has a very low reliability. The authors of [ 25 , 26 ] suggested a 5G-enabled IoT as the foundation for blockchain (blockchain 5G-IOT)- based industrial automation for use in smart cities, digital homes, healthcare 4.0, intelligent farming, automated cars, and supply chain administration, among other industries. IoT-integrated industrial automation provides an effective decentralized access control method for device-to-device (D2D) connectivity in numerous industrial domains.…”

Section: Performance Analysismentioning

confidence: 99%

Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

Shahrani

Alomar

Alqahtani

et al. 2022

Sensors

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Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented.

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Blockchain-Based Cyberthreat Mitigation Systems for Smart Vehicles and Industrial Automation

Cited by 19 publications

References 31 publications

Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends

Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends

Blockchain as privacy and security solution for smart environments: A Survey

Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things

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