This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks.} {We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability}. {We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to} validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and {MEC} resource allocation algorithms.

翻译：本文提出了一种基于区块链安全的深度强化学习（BC-DRL）优化框架，用于去中心化无线移动边缘计算（MEC）网络中的数据管理和资源分配。在该框架中，我们设计了一种低延迟的基于声誉的权益证明（RPoS）共识协议，以选择高可靠性的区块链使能基站，安全存储MEC用户请求并防止数据篡改攻击。我们将MEC资源分配优化问题建模为一个约束马尔可夫决策过程，该过程在最小处理延迟和拒绝服务（DoS）概率之间取得平衡。我们利用MEC聚合特征作为DRL输入，显著降低了单个MEC请求剩余服务处理时间的高维输入维度。所设计的约束DRL能够有效获取适应动态DoS需求的最优资源分配方案。通过大量仿真结果与分析，我们验证了所提出的BC-DRL框架在安全性、可靠性和资源利用效率方面优于基准区块链共识协议及MEC资源分配算法。