# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 2 Background
### Integration of MEC and DLT  
Several terms are used in the literature to describe the computing collaboration among the end-user, the nearby server, and the cloud. They include fog computing [26], edge computing [27], and MEC [28]. Compared with the \"cloud\", the \"fog\" is closer to the \"ground\" (e.g., the IoT data source). It refers to the extended part of cloud computing, including distributed resources, wired and wireless data transmissions, and intermediate layers between edge and cloud. Edge computing, however, focuses on the task of executing using edge nodes in the RAN outside of the core network. Furthermore, mobile edge computing is a form of edge computing that includes the data caching and computation offloading strategies within the mobile network [29]. Moreover, recent interests in MEC reflect the practical situation with multi-technology RANs in edge computing [30]. It covers access points, hot spots, routers, etc., to establish an edge network. In this review, we use the acronym \"MEC\" to stand for multi-access edge computing, which also encompasses mobile edge computing. The relationships among fog computing, edge computing, and MEC are, thus, illustrated in Figure 2.  
In general, the integration between blockchain and MEC is mutually beneficial [15]. On one hand, blockchain introduces security, privacy, and trust to MEC [31; 32]. Efficient control and incentive of cooperation among edge devices and servers are securely enabled by blockchain. On the other hand, MEC improves the scalability of blockchain in a distributed and efficient manner by delivering computing and cache resources to the blockchain-enabled IoT systems. For example, blockchain mining requires a high computational capability in the PoW process, which imposes great challenges for IoT devices. The reason why IoT devices should actively mine is that a global consensus is required for transaction validation. Different from a distributed IoT system, a blockchain-enabled IoT system decentralize the authority to each IoT device. In other words, there is no single third party that could help IoT devices make a global decision. Therefore, the PoW mechanism needs to be in place to confirm and secure the integrity and validity of transactions. Fortunately, MEC can be introduced as a solution to this issue. By offloading computational tasks to an MEC server, resource-constrained IoT devices can use PoW to reach consensus for decentralized applications. [PERSON] et al. [33] discussed the privacy leakage issue in blockchain-MEC integration. In this article, mobile users act as miners in the blockchain system. Data processing tasks and mining tasks are offloaded from users to nearby MEC servers. The privacy level of this process is modeled and formulated. Furthermore, blockchain was introduced as a strong security mechanism for MEC systems in vehicular networks [34]. In addition, [PERSON] [35] introduced a blockchain-based trust mechanism for MEC systems. By establishing a reputation system for the edge nodes, the miner in the blockchain network was, thus, selected in a trusted manner. Additionally, blockchain-enabled payment systems for the video streaming industry were developed with an incentive mechanism for MEC servers [36]. Furthermore, the flexibility and scalability of block size could be significantly improved by MEC. However,  
Figure 2: The relationships among different phrases. not every edge device could have enough cryptocurrency to buy the offloading service. Therefore, [PERSON] et al. [37] proposed a loan strategy for this purpose. Although the mining task could be executed on MEC servers, competition exists among IoT devices. The reason is that the resources of edge servers are still limited compared to relatively numerous IoT devices. To deal with this issue, [PERSON] et al. [38] solved the computation resources allocation problem in the MEC-assisted public blockchain network.