  This study analyses the medium access control (MAC) layer aspects of a
low-Earth-orbit (LEO) satellite-based Internet of Things (IoT) network. A
transmission scheme based on change detection is proposed to accommodate more
users within the network and improve energy efficiency. Machine learning (ML)
algorithms are also proposed to reduce the payload size by leveraging the
correlation among the sensed parameters. Real-world data from an IoT testbed
deployed for a smart city application is utilised to analyse the performance
regarding collision probability, effective data received and average battery
lifetime. The findings reveal that the traffic pattern, post-implementation of
the proposed scheme, differs from the commonly assumed Poisson traffic, thus
proving the effectiveness of having IoT data from actual deployment. It is
demonstrated that the transmission scheme facilitates accommodating more
devices while targeting a specific collision probability. Considering the link
budget for a direct access NB-IoT scenario, more data is effectively offloaded
to the server within the limited visibility of LEO satellites. The average
battery lifetimes are also demonstrated to increase by many folds by using the
proposed access schemes and ML algorithms.
