The fast and unconstrained mobility of Flying Ad- hoc NETworks (FANETs) brings about the need to develop solutions for packet routing in a highly dynamic topology scenario. Previous works in this direction aim at extending protocols designed for Mobile Ad-hoc NETworks (MANETs) to the more challenging domain of FANETs. Unlike previous approaches, we aim at exploiting the device controllable mobility to facilitate network routing. We propose MAD (Movement Assisted Delivery): a packet routing protocol specifically tailored for networks of aerial vehicles. MAD enables adaptive selection of the most suitable relay nodes for packet delivery, resorting to movement-assisted delivery upon need, which is supported by a reinforcement learning approach. By means of extensive simulations we show that MAD outperforms previous solutions in all the considered performance metrics including average packet delay, delivery ratio, and communication overhead, at the expense of a moderate loss in average device availability.
MAD for FANETs: Movement Assisted Delivery for Flying Ad-hoc Networks / Bartolini, Novella; Coletta, Andrea; Gennaro, Andrea; Maselli, Gaia; Prata, Matteo. - (2021). (Intervento presentato al convegno IEEE International Conference on Distributed Computing Systems (IEEE ICDCS 2021) tenutosi a Virtual Conference).
MAD for FANETs: Movement Assisted Delivery for Flying Ad-hoc Networks
Novella Bartolini
;Andrea Coletta;Gaia Maselli;Matteo Prata
2021
Abstract
The fast and unconstrained mobility of Flying Ad- hoc NETworks (FANETs) brings about the need to develop solutions for packet routing in a highly dynamic topology scenario. Previous works in this direction aim at extending protocols designed for Mobile Ad-hoc NETworks (MANETs) to the more challenging domain of FANETs. Unlike previous approaches, we aim at exploiting the device controllable mobility to facilitate network routing. We propose MAD (Movement Assisted Delivery): a packet routing protocol specifically tailored for networks of aerial vehicles. MAD enables adaptive selection of the most suitable relay nodes for packet delivery, resorting to movement-assisted delivery upon need, which is supported by a reinforcement learning approach. By means of extensive simulations we show that MAD outperforms previous solutions in all the considered performance metrics including average packet delay, delivery ratio, and communication overhead, at the expense of a moderate loss in average device availability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.