Digital twin for the industrial Internet of Things (DT-IIoT) creates a high-fidelity, fine-grained, low-cost digital replica of the cyber-physical integrated Internet for industry. Powered by artificial intelligence (AI) and security technologies, DT-IIoT provides advanced features such as real-time monitoring, predictive maintenance, remote diagnostics, and rapid response for smart IIoT systems. A systematic review of key enabling technologies such as digital twin, AI, and blockchain is essential to develop DT-IIoT and reveal pitfalls. This paper reviews the preliminaries, real-world applications, architectures and models of digital twin-driven IIoT. In addition, advanced technologies for intelligent and secure DT-IIoT are investigated, including state-of-the-art AI solutions such as transfer learning and federated learning, as well as blockchain-based security solutions. Moreover, software tools for high-fidelity digital twin modeling are proposed. A case study on reinforcement learning-based integrated-control, communication, and computing (3C) design is developed to demonstrate the AI-driven intelligent DT-IIoT. Finally, this paper outlines the prospective applications, challenges, and integrations with ABCDE (i.e., AI, Blockchain, cloud computing, big data, edge computing) as the future directions.

Nowadays, benefit from more powerful edge computing devices, edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find knowledge derived from massive sensory data, such as cyber results or models of classification, detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. While most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We firstly propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts and a new consensus mechanism Proof-of-Trading (PoT). Besides, a non-cooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and the incentive effect of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.

Currently, blockchain technology has been widely used due to its support of transaction trust and security in next generation society. Using Internet of Things (IoT) to mine makes blockchain more ubiquitous and decentralized, which has become a main development trend of blockchain. However, the limited resources of existing IoT cannot satisfy the high requirements of on-demand energy consumption in the mining process through a decentralized way. To address this, we propose a decentralized on-demand energy supply approach based on microgrids to provide decentralized on-demand energy for mining in IoT devices. First, energy supply architecture is proposed to satisfy different energy demands of miners in response to different consensus protocols. Then, we formulate the energy allocation as a Stackelberg game and adapt backward induction to achieve an optimal profit strategy for both microgrids and miners in IoT. The simulation results show the fairness and incentive of the proposed approach.

Social networks are very important social cyberspaces for the human being to establish efficient relations and collaborations for life and production. Currently, information-centric networks (ICN) are main trend of next generation networks, which promote traditional social networks to information-centric social networks (IC-SN). Because of the complexity and openness of social networks, it is a very key issue to filter security services for users. However, existing scheme were proposed just for traditional social networks, which cannot satisfy the new requirements of IC-SN including extendibility, data mobility, non-IP address, flexible deployment, etc. To address this challenge, a fog computing based content-aware filtering for security services, FCSS, is proposed in information centric social networks. Firstly, the assessments and content matching scheme is proposed for IC-SN. Secondly, fog computing based content-aware filtering scheme is proposed for security services, in which the content label technology is used. The simulations and evaluations show the advantage of FCSS in terms of hit ratio, filtering delay, and filtering accuracy.