Predictive Configuration on DHCP in WLANs

作者：Pei Zhang, Yunzhe Wang, Hui-En Yin, Boshuang Wu, Qi Wang, Xiaohong Huang, Yan Ma, Jilong Wang, Congcong Miao · 发表于：IEEE Transactions on Networking · 年份：2025 · DOI：10.1109/ton.2025.3585618 · 研究领域：Network Packet Processing and Optimization、Context-Aware Activity Recognition Systems

DHCP is widely deployed in WLANs to automatically assign IP addresses to WiFi devices when users connect to the WLANs. However, frequent user mobility brings big challenges to the DHCP performance. Recently proposed IP configuration (e.g., IP lease time, size of IP address pool) decisions on DHCP are based on traditional models to study user mobility patterns which lead to poor DHCP performance since the online time of individuals varies due to their personal pReferences and the number of crowds differs spatially and temporally. In this paper, we propose PredHCP, a predictive configuration framework on DHCP to improve the DHCP performance. Specifically, PredHCP utilizes an attention-based recurrent neural network (ARNN) to learn sequential patterns of individual mobility and accurately predicts user online time to ensure the effective IP lease time configuration. Meanwhile, PredHCP introduces a spatio-temporal graph neural network (STGNN) to learn both spatial and temporal dependencies of crowd migration and accurately predict crowd size in each area to ensure effective IP pool configuration. We conduct comprehensive experiments on real network traces for a month to evaluate the performance of PredHCP. Experimental results show that PredHCP can accurately predict user mobility patterns by achieving lower prediction errors. By accurately modeling mobility patterns, PredHCP makes effective IP configuration to ensure high DHCP performance. Large-scale simulation results show tha...