Efficient Transmission Scheme for LEO Satellite-Based NB-IoT: A Data-Driven Perspective

TL;DR

This paper presents a data-driven transmission scheme for LEO satellite-based NB-IoT that enhances device capacity and energy efficiency by leveraging change detection and machine learning, validated with real-world smart city data.

Contribution

It introduces a novel, data-driven MAC layer transmission scheme incorporating change detection and ML algorithms to improve IoT network performance in LEO satellite scenarios.

Findings

Increased device capacity while maintaining collision probability targets.

Significant improvements in battery lifetime for IoT devices.

Real-world data shows non-Poisson traffic patterns post-implementation.

Abstract

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.