Routing Optimization in Heterogeneous Wireless Networks Using Machine Learning and Network Modeling

Sara El Alaoui, Purdue University
Shutterstock image

The rising number of vendors and variety in platforms and wireless communication technologies have introduced heterogeneity to networks compromising the efficiency of existing routing algorithms.

Through our research on routing in heterogeneous wireless networks for space and Mission-Driven IoT, we show that precise modeling of network heterogeneity properties enables us to enhance network performance in terms of various metrics, such as end-to-end delay and network utilization. By using different tools including machine learning, edge computing, statistical analysis, MADM and age of information, we demonstrate that heterogeneity and the lack of network infrastructure can be overcome paving the way for heterogeneous wireless networks that are highly efficient and dynamic.