Fog-based enhanced safety management system for miners

Tanwar, Sudeep; Vora, Jayneel; Kaneriya, Shriya; Tyagi, Sudhanshu

doi:10.1109/icaccaf.2017.8344726

Cited by 15 publications

(3 citation statements)

References 2 publications

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“…This approach uses a blockchain-based architecture to give a demand response solution. This solution included Fog-Enabled Intelligent Devices (FEID) [36] at the user side (act as a blockchain node) and managed smart contracts with the energy producer [36] in cloud computing platform [37], [38]. Then, a group of energy users combined into a centralized Virtual Node.…”

Section: Responsementioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion. INDEX TERMS Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

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Section: Responsementioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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“…48 As third-party administrators operate the cloud service, they are the chances of data manipulation and data misuse and it generates a problem of security and privacy in the IoT. 49 For overcoming the difficulties that exist in the IoT, this triggers for blockchain integration with IoT. Blockchain can authenticate the devices, improve storage capability of IoT, reduce the data manipulation, and provide the security to the cloud server.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Adoption of blockchain technology in various realms: Opportunities and challenges

Akram

Malik

Singh

et al. 2020

Security and Privacy

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Our society is becoming “smarter” and more interconnected, as evidenced by technological trends such as smart farming, smart healthcare, supply chain and logistics, and smart city and nation. Security and privacy are two major key considerations in these smart applications, such as blockchain‐based security solutions for Industry 4.0‐based applications (the focus of this study). In this study, we present a systematic review of existing blockchain‐based solutions, particularly for Industry 4.0‐based applications. We present the merits and demerits of various state‐of‐the‐art approaches. We also highlight the challenges of the blockchain with the scope of interoperability and governance so that this gives a clear understanding to researchers and professionals.

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“…The inherent limitations of centralization through third-party service providers form a closed system as users cannot interact with the external environment, thereby reducing the versatility of modern CPS applications. However, with the rise in edge/fog computing, users can store, retrieve, and access their data securely over mobile devices through wireless communication infrastructures [8]. Decentralized applications also mitigate the requirement of third-party service providers and edge/fog computing reduces the overall latency in communications [9].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

et al. 2020

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Modern industry 4.0 applications are shifting towards decentralized automation of computing and cyber-physical systems (CPS), which necessitates building a robust, secure, and efficient system that performs complex interactions with other physical processes. To handle complex interactions in CPS, trust and consensus among various stakeholders is a prime concern. In a similar direction, consensus algorithms in blockchain have evolved over the years that focus on building smart, robust, and secure CPS. Thus, it is imperative to understand the key components, functional characteristics, and architecture of different consensus algorithms used in CPS. Many consensus algorithms exist in the literature with a specified set of functionalities, performance, and computing services. Motivated from these facts, in this survey, we present a comprehensive analysis of existing state-of-the-art consensus mechanisms and highlight their strength and weaknesses in decentralized CPS applications. In the first part, we present the scope of the proposed survey and identify gaps in the existing surveys. Secondly, we present the review method and objectives of the proposed survey based on research questions that address the gaps in existing studies. Then, we present a solution taxonomy of decentralized consensus mechanisms for various CPS applications. Then, open issues and challenges are also discussed in deploying various consensus mechanisms in the CPS with their merits and demerits. The proposed survey will act as a road-map for blockchain developers and researchers to evaluate and design future consensus mechanisms, which helps to build an efficient CPS for industry 4.0 stakeholders.

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Fog-based enhanced safety management system for miners

Cited by 15 publications

References 2 publications

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Adoption of blockchain technology in various realms: Opportunities and challenges

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

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