Secure Deep Reinforcement Learning for Dynamic Resource Allocation in
  Wireless MEC Networks
Xin Hao Phee Lep Yeoh Changyang She Branka Vucetic Yonghui Li
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.