A Reinforcement Learning and Blockchain-Based Trust Mechanism for Edge Networks

Liang Xiao, Yuzhen Ding, Donghua Jiang, Jinhao Huang, Dongming Wang, Jie Li, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

83 Scopus citations


Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.

Original languageEnglish (US)
Article number9095319
Pages (from-to)5460-5470
Number of pages11
JournalIEEE Transactions on Communications
Issue number9
StatePublished - Sep 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


  • Blockchain
  • deep reinforcement learning
  • mobile edge computing


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