Intelligent resource allocation in mobile blockchain for privacy and security transactions: a deep reinforcement learning based approach

Zhaolong Ning, Shouming Sun, Xiaojie Wang, Lei Guo, Guoyin Wang, Xinbo Gao, Ricky Yu-Kwong Kwok

Abstract: 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.