# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 3 Emerging Trends and Visions
### Recent Advances  
In this part, we select and introduce some recent advances related to blockchain-enabled edge intelligence. These topics are not fully investigated yet, but a few high-quality works have already been done. We aim at highlighting these new topics for researchers who wish to glimpse the latest research trends in this area. In addition, these advances further support that blockchain-enabled edge intelligence could be a game-changer across different industries. * _Video Streaming_: Traditional video streaming requires centralized governance of data, which leads to centralized and low-profit video processing. Moreover, this centralized management and distribution of a large volume of video content require substantial data storage and communication bandwidth at a huge cost. In addition, video streams have to be converted into several versions to meet the different requirements of downloaders, by a process called video transcoding [106] that is a computation task with \begin{table}
\begin{tabular}{c c c} \hline \hline
**Directions** & **Ref.** & **Contributions** \\ \hline Neural Networks & [92] & Design a federated approach for neural networks in the Internet of medical things. \\ \hline \multirow{8}{*}{DRL} & [93] & Propose a DRL-based solution for task scheduling in the mobile blockchain. \\ \cline{2-3}  & [94] & Proposed a cooperative computation offloading strategy based on DRL for blockchain-enabled MEC. \\ \cline{2-3}  & [95] & Propose a DRL-based routing control for blockchain-based MEC. \\ \cline{2-3}  & [96] & Propose DRL-based strategies for offloading and resources management in ultra-dense edge networks. \\ \cline{2-3}  & [97] & Propose DRL solutions for MEC-enabled video analytics on the Internet of autonomous vehicles. \\ \cline{2-3}  & [98] & Propose a framework for edge nodes to reach consensus and allocate resources by DRL approaches. \\ \cline{2-3}  & [99] & Develop a learning approach for decision making based on deep Q-learning in blockchain-based MEC. \\ \hline \multirow{8}{*}{FL} & [100] & Propose a blockchain-enabled FL method in haul railway control system. \\ \cline{2-3}  & [101] & Devise an innovative committee consensus mechanism for blockchain-enabled FL. \\ \cline{2-3}  & [102] & Propose a cognitive computing paradigm based on FL and blockchain. \\ \cline{2-3}  & [103] & Propose a novel property inference attack to evaluate the property leakage in blockchain-enabled FL for intelligence edge computing. \\ \cline{2-3}  & [104] & Design a novel and decentralized FL method based on forest algorithms and blockchain. \\ \cline{2-3}  & [105] & Propose a practical decentralized deep learning approach for IoT applications based on the FL and blockchain. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Research directions and literature related to ML in IoT. a heavy workload. By exploiting blockchain-enabled edge intelligence, transcoding tasks can be offloaded to MEC servers and user privacy is also secured. This approach was proposed by [PERSON] et al. [107], and smart contracts were further implemented to enable self-organized video streaming. [PERSON] et al. [36] further proposed an adaptive block size scheme in Reference [36], together with an autonomous content delivery market based on smart contracts. The authors further developed incentive mechanisms to facilitate collaboration among content providers, transcoders, and downloaders in Reference [88]. * _Tactile Internet_: Ultra-low delay communication is the main feature of the tactile Internet, which could be brought into reality With the help of 5G and beyond networks. This has motivated multiple research works and applications, such as haptic communications [108] and real-time telesurgery [109; 110]. By bringing computing and caching resources close to end devices, MEC becomes the key to realize the above delay-sensitive application. A few papers have investigated the blockchain-enabled tactile Internet incorporating MEC. For example, [PERSON] et al.