Demo: Enhancing the Networking Performance of IPv6 IoT Devices Using Machine Learning and IVI

Mengying Zhou, Zhiyang Sun, Xin Wang, Yang Chen

Abstract: IPv6, with its larger address space, is well-suited for IoT scenarios, allowing each device to have a unique address for efficient communication. However, its adoption in sensor networks is hindered by underdeveloped IPv6 infrastructure and compatibility issues with IPv4-based networks. To address these challenges, this paper proposes a dual-stack load balancing approach to enhance seamless and reliable connectivity between IPv6 and IPv4. By adaptively distributing traffic between IPv6 and IPv4 channels, our system alleviates performance bottlenecks and improves reliability. A real-world deployment in the campus network of Fudan University demonstrates a 1.9x increase in throughput and a 27% reduction in congestion-related failures with only CPU overhead of 2.44% and a translation delay of 0.58 ms. These results highlight the potential of intelligent load balancing and IPv4/IPv6 translation in supporting scalable and efficient sensor networks and facilitating the transition to IPv6.