Smart Green Communication Protocols Based on Several-Fold Messages Extracted from Common Sequential Patterns in UAVs

Ivan Garcia-Magarino, Geraldine Gray, Raquel Lacuesta, Jaime Lloret

Research output: Contribution to journalArticlepeer-review

Abstract

Green communications can be crucial for saving energy in UAVs and enhancing their autonomy. The current work proposes to extract common sequential patterns of communications to gather each common pattern into a single several-fold message with a high-level compression. Since the messages of a pattern are elapsed from each other in time, the current approach performs a machine learning approach for estimating the elapsed times using off-line training. The learned predictive model is applied by each UAV during flight when receiving a several-fold compressed message. We have explored neural networks, linear regression and correlation analyses among others. The current approach has been tested in the domain of surveillance. In specific-purpose fleets of UAVs, the number of transmissions was reduced by 13.9 percent.

Original languageEnglish
Article number8961911
Pages (from-to)249-255
Number of pages7
JournalIEEE Network
Volume34
Issue number3
DOIs
Publication statusPublished - 1 May 2020

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