DeepBlockScheme: A Deep Learning-Based Blockchain Driven Scheme for Secure Smart City

【Author】 Singh, Sushil Kumar; El Azzaoui, Abir; Kim, Tae Woo; Pan, Yi; Park, Jong Hyuk

【Source】HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES

【影响因子】6.558

【Abstract】Today, the continuous deployment of sensors and the Internet of Things (IoT) has boosted the amount of manufacturing data in the smart city. Factories are rapidly becoming more interconnected in the world with sensing data. Big data are generally characterized by five V's: high volume, high value, high veracity, high variety, and high velocity. Latency, scalability, centralization, reliability, security, and privacy are the major challenges for advanced smart city applications such as smart manufacturing, smart factory, and others. Meanwhile, blockchain is an emerging distributed technology that is deployed to minimize central authority control and provide a secure environment in the recent applications above. On the other hand, deep learning is one of the leading-edge technologies that offer modern analytic tools for the processing and analysis of data and provide scalable production in the smart factory application in a smart city. In this paper, we propose DeepBlockScheme: A Deep Learning-based Blockchain Driven Scheme for a Secure Smart City, where blockchain is used in a distributed manner at the fog layer to ensure the integrity, decentralization, and security of manufacturing data. Deep learning is utilized at the cloud layer to increase production, automate data analysis, and increase the communication bandwidth of the smart factory and smart manufacturing applications in smart cities. We present a case study of car manufacturing with the latest service scenarios for the proposed scheme and compare it to existing research studies using crucial parameters such as security and privacy tools. Finally, open research challenges are also discussed based on the proposed scheme.

【Keywords】Deep Learning; Blockchain; Smart Manufacturing; Scalability; Security and Privacy

【发表时间】2021 44635

【收录时间】2022-01-02

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