Article
On-Demand Satellite Payload Execution Strategy for Natural
Disasters Monitoring Using LoRa: Observation Requirements
and Optimum Medium Access Layer Mechanisms
Lara Fernandez
1,2,3,
*, Joan Adria Ruiz-de-Azua
4
, Anna Calveras
2
and Adriano Camps
1,3
Citation: Fernandez, L.;
Ruiz-de-Azua, J.A.; Calveras, A.;
Camps, A. On-Demand Satellite
Payload Execution Strategy for
Natural Disasters Monitoring Using
LoRa: Observation Requirements and
Optimum Medium Access Layer
Mechanisms. Remote Sens. 2021, 13,
4014. https://doi.org/10.3390/rs
13194014
Academic Editor: Nikos Koutsias
Received: 10 August 2021
Accepted: 27 September 2021
Published: 7 October 2021
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Attribution (CC BY) license (https://
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4.0/).
1
CommSensLab-UPC, Department of Signal Theory and Communications, UPC BarcelonaTech,
08034 Barcelona, Spain; adriano.jose.camps@upc.edu
2
Department of Network Engineering, UPC BarcelonaTech, 08034 Barcelona, Spain; anna.calveras@upc.edu
3
Institut d’Estudis Espacials de Catalunya (IEEC)-CTE-UPC, 08034 Barcelona, Spain
4
i2Cat Foundation-Space Communications Research Group, 08034 Barcelona, Spain; joan.ruizdeazua@i2cat.net
* Correspondence: lara.fernandez.c@upc.edu
Abstract:
Natural disasters and catastrophes are responsible for numerous casualties and important
economic losses. They can be monitored either with in-situ or spaceborne instruments. However,
these monitoring systems are not optimal for an early detection and constant monitoring. An optimi-
sation of these systems could benefit from networks of Internet of Things (IoT) sensors on the Earth’s
surface, capable of automatically triggering on-demand executions of the spaceborne instruments.
However, having a vast amount of sensors communicating at once with one satellite in view also
poses a challenge in terms of the medium access layer (MAC), since, due to packet collisions, packet
losses can occur. As part of this study, the monitoring requirements for an ideal spatial nodes density
and measurement update frequencies of those sensors are provided. In addition, a study is performed
to compare different MAC protocols, and to assess the sensors density that can be achieved with
each of these protocols, using the LoRa technology, and concluding the feasibility of the monitoring
requirements identified.
Keywords:
natural disasters; sensors; satellite communications; constellations; CubeSats; IoT; LoRa;
MAC protocols
1. Introduction
Natural disasters cause the loss of lives and assets, leaving a dent in the society, and
the economy of the affected regions. These losses can be minimised by monitoring systems
that may provide continuous information and early warnings in the areas at risk. However,
for these monitoring systems to be performant, it is necessary to have dense coverage, and
near real-time data, to be able to react to a potential natural disaster occurring.
One of the current monitoring and early warning systems for natural disasters are the
networks of in-situ instruments, placed on the Earth’s surface. Often, instruments such
as buoys or profiling floats are placed in remote areas and need satellite communications
to retrieve the data. Some use geostationary orbit (GEO) satellites, such as INMARSAT
or the global telecommunication system (GTS) [
1
], since permanent coverage of the areas,
except the poles, is ensured. Others use polar low earth orbit (LEO) satellite constellations,
which provide global coverage, and the transmitted power is significantly lower. Either of
these solutions often requires the in-situ instruments to have custom ad-hoc proprietary
hardware, that is not modular. Moreover, once the satellite has retrieved the data, they have
to be downloaded to the ground, which introduces a non-negligible latency. An example is
the National Oceanic and Atmospheric Administration (NOAA) [
2
] tsunami detection and
monitoring buoys that have a latency of between 25 and 60 min [3].
Another type of in-situ instruments are arrays of sensors located along the coastlines
and on the land. Usually, they use a base station to retrieve the data, following a star-shaped
Remote Sens. 2021, 13, 4014. https://doi.org/10.3390/rs13194014 https://www.mdpi.com/journal/remotesensing
