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Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring.

Garrett Kinman¹, Željko Žilić², David Purnell^2,3

¹Octasic Inc., 2901 Rachel, Montréal, QC H1W 4A4, Canada.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This study introduces a learning-based transmission scheduling method for low Earth orbit (LEO) satellites, optimizing energy use for remote water level monitoring. The approach significantly cuts energy consumption, enabling IoT applications in unserved areas.

Keywords:

Internet of Remote Things sparse LEO satellite transmission water-level monitoring

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Area of Science:

Earth and Space Science
Computer Science
Environmental Science

Background:

Remote monitoring of water levels is crucial for environmental management.
Low Earth Orbit (LEO) satellite constellations offer potential for data transmission in remote areas.
Sporadic satellite connectivity necessitates optimized transmission scheduling to conserve energy.

Purpose of the Study:

To develop an energy-efficient transmission scheduling scheme for LEO satellite communications.
To enable long-term monitoring of water levels in remote regions using LEO satellites.
To create an adaptable and inexpensive scheduling solution applicable to various LEO transmission scenarios.

Main Methods:

An online learning approach combining Monte Carlo and modified k-armed bandit methods was developed.
The scheme focuses on scheduling sensor transmission times during satellite overfly periods.
The approach was tested and demonstrated in three common remote sensing scenarios.

Main Results:

The developed scheme achieved a 20-fold reduction in transmission energy consumption.
The learning approach demonstrated adaptability to different monitoring scenarios.
The method provides a cost-effective solution for LEO satellite transmission scheduling.

Conclusions:

The proposed energy optimization scheme is effective for LEO satellite-based remote monitoring.
This approach facilitates IoT applications in areas lacking wireless coverage.
The study offers a scalable solution for optimizing data transmissions in sparse satellite networks.