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

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

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

[EN] 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.This work was mainly done during the stay of the first author at the Institute of Technology Blanchardstown (now called Technological University Dublin), with the support from the "Universidad de Zaragoza," " Fundacion Bancaria Ibercaja," and "Fundacion CAI" in the "Programa Ibercaja-CAI de Estancias de Investigacion" with reference IT1/18. This work also acknowledges the research project "Construccion de un framework para agilizar el desarrollo de aplicaciones moviles en el ambito de la salud" funded by the University of Zaragoza and the Foundation Ibercaja with grant reference JIUZ-2017-TEC-03. We also acknowledge "CITIES: Ciudades inteligentes totalmente integrales, eficientes y sotenibles" (ref. 518RT0558) funded by CYTED ("Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo") and "Diseno colaborativo para la promocion del bienestar en ciudades inteligentes inclusivas" (TIN2017-88327-R) funded by the Spanish Council of Science, Innovation and Universities from the Spanish Government. This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacin de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.García-Magariño, I.; Gray, G.; Lacuesta Gilaberte, R.; Lloret, J. (2020). Smart Green Communication Protocols Based on Several-Fold Messages Extracted from Common Sequential Patterns in UAVs. IEEE Network. 34(3):249-255. https://doi.org/10.1109/MNET.001.190041724925534