QoS and Privacy-Aware Routing for 5G-Enabled Industrial Internet of Things: A Federated Reinforcement Learning Approach
Xiaoding Wang, Jia Hu, Hui Lin, Sahil Garg, Georges Kaddoum, Md. Jalil Piran, M. Shamim Hossain
Abstract
The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.