Improving signal-strength aggregation for mobile crowdsourcing scenarios
Author
dc.contributor.author
Madariaga, Diego
Author
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Madariaga, Javier
Author
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Bustos Jiménez, Javier
Author
dc.contributor.author
Bustos, Benjamín
Admission date
dc.date.accessioned
2021-09-09T19:09:32Z
Available date
dc.date.available
2021-09-09T19:09:32Z
Publication date
dc.date.issued
2021
Cita de ítem
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Sensors 2021, 21, 1084.
es_ES
Identifier
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10.3390/s21041084
Identifier
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https://repositorio.uchile.cl/handle/2250/181922
Abstract
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Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic
and industrial research have actively focused on analyzing the received signal strength in
areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation
with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to
the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method
based on interpolation. Our paper presents two clear contributions. First, we discuss the misuse
of log-scaled signal-strength values, which is a persistent problem within the mobile computing
community. We present the physical and mathematical formalities on how signal-strength values
must be handled in a scientific environment. Second, we present a solution to the difficulties of
aggregating signal strength in Mobile Crowdsourcing scenarios, as a low number of measurements
and nonuniformity in spatial distribution. Our proposed method obtained consistently lower Root
Mean Squared Error (RMSE) values than other commonly used methods at estimating the expected
value of signal strength over an area. Both contributions of this paper are important for several recent
pieces of research that characterize signal strength for an area of interest.
es_ES
Patrocinador
dc.description.sponsorship
National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Nacional 2019 21190450
Millennium Institute for Foundational Research on Data (IMFD)