In order to protect the privacy and data security of mobile devices during the transactions in the industrial Internet of Things (IIoT), we propose a mobile edge computing (MEC)-based mobile blockchain framework by considering the limited bandwidth and computing power of small base stations (SBSs). First, we formulate a joint bandwidth and computing resource allocation problem to maximize the long-term utility of all mobile devices, and take into account the mobility of devices as well as the blockchain throughput. We decompose the formulated problem into two subproblems to decrease the dimension of action space. Then, we propose a deep reinforcement learning additional particle swarm optimization (DRPO) algorithm to solve the two subproblems, in which a particle swarm optimization algorithm is leveraged to avoid the unnecessary search of a deep deterministic policy gradient approach. Simulation results demonstrate the effectiveness of our method from various aspects.

为了在工业物联网（IIoT）交易过程中保护移动设备的隐私和数据安全，我们考虑了小型基站的有限带宽和计算能力，提出了一种基于移动边缘计算（MEC）的移动区块链框架电台（SBS）。首先，我们制定联合带宽和计算资源分配问题，以最大化所有移动设备的长期效用，并考虑设备的移动性以及区块链吞吐量。我们将提出的问题分解为两个子问题，以减小动作空间的维数。然后，我们提出了一种深度强化学习附加粒子群优化（DRPO）算法来解决这两个子问题，其中利用了粒子群优化算法来避免不必要的对深度确定性策略梯度方法的搜索。仿真结果从各个方面证明了我们方法的有效性。