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The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing. There are several applications of blockchain-fog computing integration that have been proposed, recently, due to their lucrative benefits such as enhancing security and privacy. There is a need to systematically review and synthesize the literature on this topic of blockchain-fog computing integration. The purposes of integrating blockchain and fog computing were determined using a systematic literature review approach and tailored search criteria established from the research questions. In this research, 181 relevant papers were found and reviewed. The results showed that the authors proposed the combination of blockchain and fog computing for several purposes such as security, privacy, access control, and trust management. A lack of standards and laws may make it difficult for blockchain and fog computing to be integrated in the future, particularly in light of newly developed technologies like quantum computing and artificial intelligence. The findings of this paper serve as a resource for researchers and practitioners of blockchain-fog computing integration for future research and designs. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [ABSTRACT FROM AUTHOR]

Background: The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word 'smart' and benefits from innovative applications of new kinds of information and communications technology to support communal sharing. Methods: By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective. Results: Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services. Conclusions: This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area. [ABSTRACT FROM AUTHOR]

When it comes to running and managing modern supply chains, 6G Internet of things (IoT) is of utmost importance. To provide IoT with security and automation, blockchain and machine learning are two upper-layer technology that can help. First, we propose to utilize blockchain in modern supply chains to ensure efficient collaboration between all parties. Second, we adopt multi-head attention (MHA)-based gated recurrent unit (GRU) to do inbound logistics task prediction. Finally, numerical results justify that multi-head attention-based GRU model has better fitting efficiency and prediction accuracy than its counterparts. [ABSTRACT FROM AUTHOR]