Edge Intelligent Networking Optimization for Internet of Things in Smart City

Abstract

Massive devices connected through fifth-generation (5G) networks constitute a ubiquitous Internet of Things (IoT), providing diverse service applications in a smart city. A robust network topology structure against cyber-attacks is the foundation of highly reliable service quality, especially in next-generation networks or beyond 5G (B5G) networks. Existing methods apply neural networks with deep reinforcement learning methods to advance the network topology. However, the reduction of unique hardware resource constraints and the application of edge intelligent networking capability of terminal nodes are emerging challenges for robustness optimization of IoT with 5G and B5G networks. To address these problems, we design a distributed learning framework utilizing edge intelligence, improving smart terminal nodes' networking capability, which is deployed on ordinary computers instead of specialized hardware such as GPUs. The proposed framework leveraging multi-core CPU and intelligent edge methods decreases the training time and economic cost and takes full advantage of computer resources. Furthermore, the best performing framework considers the distributed communication model of edge computing and optimizes the network topology by taking advantage of smart terminal nodes' contributions. We show that the framework succeeds in various topologies and outperforms compared with other state-of-art algorithms in improving the robustness for IoT topology in smart cities.