Blockchain-Based Trusted Traffic Offloading in Space-Air-Ground Integrated Networks (SAGIN): A Federated Reinforcement Learning Approach

【Author】 Tang, Fengxiao; Wen, Cong; Luo, Linfeng; Zhao, Ming; Kato, Nei

【Source】IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS

【影响因子】13.081

【Abstract】In the future era of intelligent networks, communication technology and network architecture need to he further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security.

【Keywords】Space-air-ground integrated networks (SAGIN); blockchain; malicious node; traffic offloading; federated learning; reinforcement learning

【发表时间】2022 DEC

【收录时间】2023-01-14

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-交通领域