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The convergence of mobile edge computing (MEC) and blockchain is transformingthe current computing services in mobile networks, by offering task offloadingsolutions with security enhancement empowered by blockchain mining.Nevertheless, these important enabling technologies have been studiedseparately in most existing works. This article proposes a novel cooperativetask offloading and block mining (TOBM) scheme for a blockchain-based MECsystem where each edge device not only handles data tasks but also deals withblock mining for improving the system utility. To address the latency issuescaused by the blockchain operation in MEC, we develop a new Proof-of-Reputationconsensus mechanism based on a lightweight block verification strategy. Amulti-objective function is then formulated to maximize the system utility ofthe blockchain-based MEC system, by jointly optimizing offloading decision,channel selection, transmit power allocation, and computational resourceallocation. We propose a novel distributed deep reinforcement learning-basedapproach by using a multi-agent deep deterministic policy gradient algorithm.We then develop a game-theoretic solution to model the offloading and miningcompetition among edge devices as a potential game, and prove the existence ofa pure Nash equilibrium. Simulation results demonstrate the significant systemutility improvements of our proposed scheme over baseline approaches.

Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning