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Computer Sciences, Information Science
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.
I. Garcia-Magarino, G. Gray, R. Lacuesta and J. Lloret, "Smart Green Communication Protocols Based on Several-Fold Messages Extracted from Common Sequential Patterns in UAVs," in IEEE Network, vol. 34, no. 3, pp. 249-255, May/June 2020, doi: 10.1109/MNET.001.1900417.
University of Zaragoza; Foundation Ibercaja; CYTED; Spanish Council of Science, Innovation and Universities.