Machine Learning-Based Multi-stratum Channel Coordinator for Resilient Internet of Space Things

Sensing and transferring data are critical and challenging tasks for space missions, especially in the presence of extreme environments. Unlike terrestrial environments, space poses unprecedented reliability challenges to wireless communication channels due to electromagnetic interference and radiation. The determination of a dependable channel for exchanging critical data in a highly intemperate environment is crucial for the success of space missions. This paper proposes a unique Machine Learning (ML)-based multi-stratum channel coordinator in building the Resilient Internet of Space Things (ResIST). ResIST channel coordinator accommodates a lightweight software-defined wireless communication topology that allows dynamic selection of the most trustworthy channel(s) from a set of disparate frequency channels by utilizing ML technologies. We build a tool that simulates the space communication channel environments and then evaluate several prediction models to predict the bandwidths across a set of channels that experience the influence of radiation and interference. The experimental results show that ML-prediction technologies can be used efficiently for the determination of reliable channel(s) in extreme environments. Our observations from the heatmap and error analysis on the various ML-based methods show that Feed-Forward Neural Network (FFNN) drastically outperforms other ML methods as well as the simple prediction baseline method.