Evidence map›Paper›PMID 37420747›Full record

ArticleSensors (Basel, Switzerland)2023

Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring.

Garrett Kinman, Željko Žilić, David Purnell

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Garrett KinmanOctasic Inc., 2901 Rachel, Montréal, QC H1W 4A4, Canada.
Željko ŽilićDepartment of Electrical and Computer Engineering, McGill University, 3480 University, Montréal, QC H3A 0E9, Canada.ORCID 0000-0002-6887-3911
David PurnellDepartment of Electrical and Computer Engineering, McGill University, 3480 University, Montréal, QC H3A 0E9, Canada.

Funding

NSERC Canada Discovery Grant NA
6 · The paper itself

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.

Indexed as

Education, DistanceLearningMonte Carlo MethodTelemetryWaterWaterInternet of Remote Thingssparse LEO satellite transmissionwater-level monitoring

Identifiers

PMID37420747
PMCPMC10305013

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.