Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks
- Resource Type
- Periodical
- Authors
- Hao, X.; Yeoh, P.L.; She, C.; Vucetic, B.; Li, Y.
- Source
- IEEE Transactions on Communications IEEE Trans. Commun. Communications, IEEE Transactions on. 72(3):1414-1427 Mar, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Resource management
Security
Dynamic scheduling
Optimization
Consensus protocol
Protocols
Training
Dynamic resource allocation
low-latency blockchain consensus
secure mobile edge computing
- Language
- ISSN
- 0090-6778
1558-0857
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.