# Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)
## 6 Conclusions and Future Work  
In this paper, we proposed MinT, an edge-fog-cloud architecture to enable a fair PoW consensus mechanism by leveraging miner twins. Experimentally, the paper validated the feasibility of the concept of using DT to monitor the miners' behaviors and to deter selfish nodes who violate the fair-mining rule. The reported preliminary results verify the effectiveness of using quick change point detection and the PoB consensus algorithm to catch fair mining violators; however, more intelligent solutions are needed to support dynamicity and optimization in fair mining network. Moreover, the above mentioned open questions need to be addressed in IoT-based mining networks. Our future work includes the following.  
* We will conduct a comprehensive evaluation on SSA method in anomaly detection, especially for detection accuracy and performance, and the impact of parameter selection. Moreover, AI/ML-based algorithms will be investigated to improve anomaly detection accuracy and to support efficient dynamic resources management in the fair mining network.
* To apply MinT in a large-scale application scenario such as a smart surveillance system [48], we will implement a fully function prototype based on edge-fog-cloud architecture, in which physical containerized miners are on edge devices while digital twins are in the fog or cloud. Then, we will make a comprehensive performance analysis and assessment of security features.
* Furthermore, MinT relies on microservices that encapsulate a fair PoW mining algorithm into independent containers running on host machines. Thus, the security and privacy of containers and data reliability are among the top concerns. We will investigate the security of the container running environment, and data audition and integrity in microservice-to-microservice communication.  
Conceptualization, Q.Q., R.X. and Y.C.; methodology, Q.Q. and R.X.; software, Q.Q. and R.X.; validation, Q.Q., R.X. and Y.C.; formal analysis, Q.Q. and R.X.; investigation, Y.C.; resources, Y.C., E.B. and A.A.; data creation, Q.Q.; writing--original draft preparation, Q.Q. and R.X.; writing--review and editing, Y.C., E.B. and A.A.; visualization, Q.Q. and R.X.; supervision, Y.C. and E.B.; project administration, Y.C. and A.A.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the U.S. National Science Foundation (NSF) via grants CNS-2141468.  
Not Applicable, the study does not report any data.  
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Air Force.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ AR & Augmented Reality \\ BFT & Byzantine Fault Tolerant \\ CUSUM & Cumulative Sum \\ DDDAS & Dynamic Data-Driven Applications Systems \\ DLT & Distributed Ledger Technology \\ \end{tabular}