A 2-UAV: Application-Aware resilient edge-assisted UAV networks

Andrea Coletta, Flavio Giorgi, Gaia Maselli, Matteo Prata, Domenicomichele Silvestri, Jonathan D. Ashdown, Francesco Restuccia

Abstract: During advanced surveillance missions, Unmanned Aerial Vehicles (UAVs) usually require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints, and the possible node failures. To address these critical challenges, we propose a novel A 2<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msup></math>-UAV framework that optimizes the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem (A 2<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msup></math>-TPP) to optimize routing, data pre-processing and target assignment for each UAV. Our formulation explicitly takes into account (i) the relationship between CV task accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs, and (iv) the possible node failures. We demonstrate A 2<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msup></math>-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A 2<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msup></math>-UAV through simulation and real-world experiments using a testbed composed by four DJI Mavic Air 2 UAVs. Results on image classification show that A 2<math><msup is="true"><mrow is="true"></mrow><mrow is="true"><mn is="true">2</mn></mrow></msup></math>-UAV attains on average around 38% more accomplished tasks w.r.t. the state of the art, with a 400% improvement in tasks-intensive scenarios. Moreover, we show that our framework is able to reconfigure the network in case of nodes failure.