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Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
IEEE Transactions on Network and Service Management ( IF 5.3 ) Pub Date : 2020-12-01 , DOI: 10.1109/tnsm.2020.3010967
Dinh C. Nguyen , Pubudu N. Pathirana , Ming Ding , Aruna Seneviratne

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user’s power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.

中文翻译:

具有深度强化学习的移动区块链中的隐私保护任务卸载

区块链技术以其安全、透明和去中心化的特性,最近已被用于许多移动应用程序。然而,执行计算密集型数据应用和区块链挖掘等广泛任务的过程需要移动设备的高计算和存储能力,这将阻碍移动系统中的区块链应用。为了应对这一挑战,我们提出了一种基于移动边缘计算 (MEC) 的区块链网络,其中多移动用户 (MU) 充当矿工,通过无线通道将他们的数据处理任务和挖掘任务卸载到附近的 MEC 服务器。特别地,我们将任务卸载、用户隐私保护和挖矿利润制定为一个联合优化问题,建模为马尔可夫决策过程,我们的目标是最小化长期系统卸载实用程序并最大化所有区块链用户的隐私级别。我们首先提出了一种基于强化学习 (RL) 的卸载方案,该方案使 MU 能够根据区块链交易状态、MU 和 MEC 服务器之间的无线信道质量以及用户的功率哈希状态做出最佳卸载决策。为了进一步提高大规模区块链场景的卸载性能,我们通过使用深度 Q 网络开发了一种深度强化学习算法,该算法可以有效地解决大型状态空间,而无需任何系统动力学的先验知识。实验和仿真结果表明,所提出的基于 RL 的卸载方案显着增强了用户隐私,
更新日期:2020-12-01
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