Blockchain-Incentivized D2D and Mobile Edge Caching: A Deep Reinforcement Learning Approach

Abstract

D2D caching assists mobile edge caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this article, a Blockchain-based CDM is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and mobile edge caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on the transactions and prevent frauds, a consensus protocol among the SCENE is necessary. The pPBFT protocol is proposed to minimize the latency of reaching consensus while guaranteeing its confidence level. We build the model of cache sharing and transaction execution consensus, and we further formulate cache placement and SCENE selection as Markov Decision Process problems. Considering the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problems. Simulation results show that, compared to conventional solutions, the proposed schemes achieve efficient traffic offloading, and significantly improve the transaction execution consensus speed.