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Fog computing is considered to be a key enabling technology for future networks. By broadening the cloud computing services to the network edge, fog computing can support various emerging applications such as IoT, big data, and blockchain with low latency and low bandwidth consumption cost. To achieve the full potential of fog computing, it is essential to design an incentive mechanism for fog computing service providers. Auction is a promising solution for the incentive mechanism design. However, it is challenging to design an optimal auction that maximizes the revenue for the providers while holding important properties: IR and IC. Therefore, this article introduces the design of an optimal auction based on deep learning for the resource allocation in fog computing. The proposed optimal auction is developed specifically to support blockchain applications. In particular, we first discuss resource management issues in fog computing. Second, we review economic and pricing models for resource management in fog computing. Third, we introduce fog computing and blockchain. Fourth, we present how to design the optimal auction by using deep learning for the fog resource allocation in the blockchain network. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme (i.e., the greedy algorithm) in terms of revenue, and IC and IR violations. Thus, the proposed scheme can be used as a useful tool for the optimal resource allocation in general fog networks. [ABSTRACT FROM AUTHOR]

The rapid proliferation of Internet-of-Things (IoT) devices has brought great challenges of data management, i.e., storing, retrieving and manipulating a large volume of IoT data. Conventional IoT systems rely on centralized architectures to manage IoT data, hence suffering from limited scalability, lack of transparency, and single point of failure issues. As such, we employ blockchain as a distributed ledger to support the decentralized approach of data management in IoT systems, where IoT data are stored in the deployed blockchain for further utilization, e.g., retrieve and audit. A general architecture combining blockchain and IoT systems is presented. Nevertheless, as the resource constraints of IoT devices may still exist during the process of data transmissions from IoT devices to the blockchain network, we propose a case study of a learning-assisted resource allocation method to support intelligent data management. The numerical results show that the proposed scheme achieves superior performance compared with baseline solutions. [ABSTRACT FROM AUTHOR]