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Title	Drl And Main-Side Blockchain Empowered Edge Computing Framework For Assistant Driving
ID_Doc	21061
Authors	Zhong Y.; Xu S.; Li Z.; Yu P.; Wang Y.; Zhou F.
Year	2023
Published	Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
DOI	http://dx.doi.org/10.1109/NOMS56928.2023.10154314
Abstract	To provide intelligent and accurate assistant driving services in smart city, as well as ensure the security and tamper proof of vehicle data, this paper build a deep reinforcement learning (DRL) and main-side blockchain empowered service framework. By storing driving data and vehicle information on the sidechain, while deploying index information on the mainchain, the main-side blockchain structure can enhance the scalability of blockchain, decrease the communication overhead, improve consensus efficiency, and avoid the leakage of data between different sidechains. However, the resource limited vehicles on sidechain cannot process numerous computation-intensive mining tasks in time, resulting in high service delays. Thus, this paper integrate mobile edge computing with blockchain system to design a double-layer mining service offloading mechanism, allowing the edge nodes and neighboring vehicles to form a cooperative mining network and collaboratively participate in mining process with specific offloading rates. The first layer uses Asynchronous Advantage Actor-critic (A3C) algorithm to efficiently offload partial mining task from the task vehicle to the road side unit (RSU), and the second layer applies double auction to specifically obtain the offloading rates from RSU to multiple service vehicles. Simulation results demonstrate that, our proposed mechanism outperforms other compared algorithms in the average profit and consensus delay. © 2023 IEEE.
Author Keywords	computation task offloading; edge collaboration; machine learning; Main-side blockchain; smart city

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