## 1 Introduction  
New computing environments, such as cloud computing and IoT (Internet of Things) provide convenient services, such as data sharing and fusion computing, greatly making use of the data processing capacity as well as computing and storage resources [1]. According to Cisco, the global network traffic reached 278 EB/month in 2021. Moreover, according to the forecast by Machina research, the number of global IoT connections will increase to 27 billion and cellular connections will reach 22 billion in 2025 [2]. The number of networked devices is increasing rapidly, resulting in an increasing amount of data and traffic bringing great pressure on network resources. It is difficult for traditional cloud computing to respond to requests of terminal devices in real-time in the case of insufficient bandwidth [3]. Moreover, the challenges are as follows [4]:  
* Cloud centralized-based services might become bottlenecks when distributed queries and updates are frequent. Meanwhile, malicious attacks can also lead to a single point of failure;
* With the diversification of business processes and dynamic scalability of functions, cloud centralized-based services, and data processing cannot adapt to the flexibility and real-time metrics;
* DOs (data owners) and DCs (data consumers) relying on data centers may have data risks of security and privacy, which may damage the rights and interests of DOs [5]. In order to respond to the existing challenges, meet business needs, and improve user experiences, the edge computing paradigm is proposed to \"sink\" the cloud computingpower and improve the processing capacity of edge devices, which provides edge services near the data source to meet the key requirements in terms of agile connectivity and real-time optimization and intelligent application and security and privacy [6]. The rise of edge intelligence of IIoT will promote the manufacturing industry to dig deeply into data value, to create a new ecology of industrial production. As a combination of edge computing and AI, edge intelligence uses computing resources from near data sources to complete intelligent tasks; it is a feasible solution for industrial intelligence applications. Thus, edge computing has gradually become an effective supporting platform [7] for new IoE applications, which have three-tier architecture, including \"cloud-edge-end\" [8]. The core layer edge supports the downward (southward) access to various field devices and upward (northward) connection with the cloud. EIS provides core value drivers for the implementation of IoT applications, helping customers improve operational efficiency and business transformation. In the post-cloud computing era, edge computing, as a distributed computing paradigm integrating the core capabilities of computing, storage, network, and application, providing edge intelligent services, such as real-time business, data optimization, intelligent application, security, and privacy protection nearby, enabling developers to quickly develop and deploy edge applications. Industry application scenarios are constantly enriched, such as autonomous driving [9], IIoT, VR/AR, smart medical care, etc. At present, the fusion of EIS, AI, and blockchain has also become a hot topic in academic research. It is indicated by searching for the keywords: blockchain, MEC (multi-access edge computing), ML (machine learning), and IoT [10], which, on the one hand, blockchain introduces security, privacy, and trust to MEC, and on the other hand, MEC improves the scalability of blockchain in a distributed and efficient manner. With various intelligent services showing great vitality, such as XaaS derived from cloud native computing, edge computing, and blockchain, such as BaaS and FaaS (functions as a service), as well as SDX defined by software, such as SDN (software-defined network) and SDEc (software defined edge computing), researchers continue to put forward new models and algorithms for solving the increasingly complex requirements.