Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
Author:
Kinman Garrett1, Žilić Željko2ORCID, Purnell David23
Affiliation:
1. Octasic Inc., 2901 Rachel, Montréal, QC H1W 4A4, Canada 2. Department of Electrical and Computer Engineering, McGill University, 3480 University, Montréal, QC H3A 0E9, Canada 3. Department of Civil and Water Engineering, Laval University, pavillon Adrien-Pouliot 1065, av. de la Médecine, Québec, QC G1V 0A6, Canada
Abstract
This paper explores the use of low earth orbit (LEO) satellite links in long-term monitoring of water levels across remote areas. Emerging sparse LEO satellite constellations maintain sporadic connection to the ground station, and transmissions need to be scheduled for satellite overfly periods. For remote sensing, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our online learning-based approach combines Monte Carlo and modified k-armed bandit approaches, to produce an inexpensive scheme that is applicable to scheduling any LEO satellite transmissions. We demonstrate its ability to adapt in three common scenarios, to save the transmission energy 20-fold, and provide the means to explore the parameters. The presented study is applicable to wide range of IoT applications in areas with no existing wireless coverages.
Funder
NSERC Canada Discovery Grant
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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