Abstract
With the rapid development of smart IoT technology, various innovative mobile applications improve many aspects of our daily life. End-edge-cloud collaboration provides data transmission in connecting heterogeneous IoT devices and machines with improvements in high quality of service and capacity. However, the end-edge cloud architecture still remains some challenges including the risks of data privacy and tolerance transmission delay. Blockchain is a promising solution to enable data processing in a secure and efficient way. In this paper, blockchain is considered as an infrastructure of the end-edge-cloud network and the time cost of the PBFT consensus is analyzed from the perspective of the leader’s position. Considering the concurrent processing of tasks in cellular networks, multi-intelligent deep reinforcement learning is used to train the assignment strategy of the edge server. The numerical results show that the proposed method can achieve better performance improvement in terms of the time consumption of data processing.
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Acknowledgements
This work was supported by the National Key R &D Program of China under Grant 2018YFB1402700, and in part by the National Natural Science Foundation of China under Grant 61690202.
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Ma, S., Wang, S., Tsai, WT., Zhang, Y. (2024). Delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network. In: Li, C., Li, Z., Shen, L., Wu, F., Gong, X. (eds) Advanced Parallel Processing Technologies. APPT 2023. Lecture Notes in Computer Science, vol 14103. Springer, Singapore. https://doi.org/10.1007/978-981-99-7872-4_14
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DOI: https://doi.org/10.1007/978-981-99-7872-4_14
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