During their participation in the WindMill Project, the Early Stage Researchers are also expected to publish articles regarding their findings. Here you can find the abstracts of these articles, as well as links to access their full versions.
The recently proposed QUIC protocol has been widely adopted at the transport layer of the Internet over the past few years. Its design goals are to overcome some of TCP’s performance issues, while maintaining the same properties and basic application interface. Two of the main drivers of its success were the integration with the innovative Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control mechanism, and the possibility of multiplexing different application streams over the same connection. Given the strong interest in QUIC shown by the ns-3 community, we present an extension to the native QUIC module that allows researchers to fully explore the potential of these two features. In this work, we present the integration of BBR into the QUIC module and the implementation of the necessary pacing and rate sampling mechanisms, along with a novel scheduling interface, with three different scheduling flavors. The new features are tested to verify that they perform as expected, using a web traffic model from the literature.
In a Flying Ad-Hoc Network, Unmanned Aerial Vehicles (UAVs), (i.e. drones or quadcopters), use wireless communication to exchange data, status updates, and commands between each other and with the control center. However, due to the movement of UAVs, maintaining communication is difficult, particularly when multiple hops are needed to reach the destination. In this work, we propose the Stochastic Multipath UAV Routing for FANETs (SMURF) protocol, which exploits trajectory tracking information from the drones to compute the routes with the highest reliability. SMURF is a centralized protocol, as the control center gathers location updates and sends routing commands following the Software Defined Networking (SDN) paradigm over a separate long-range low bitrate technology such as LoRaWAN. Additionally, SMURF exploits multiple routes, to increase the probability that at least one of the routes is usable. Simulation results show a significant reliability improvement over purely distance-based routing, and that just 3 routes are enough to achieve performance very close to oracle-based routing with perfect information.
We address an actively discussed problem in signal processing, recognizing patterns from spatial data in motion. In particular, we suggest a neural network architecture to recognize motion patterns from 4D point clouds. We demonstrate the feasibility of our approach with point cloud datasets of hand gestures. The architecture, PointGest, directly feeds on unprocessed timelines of point cloud data without any need for voxelization or projection. The model is resilient to noise in the input point cloud through abstraction to lower-density representations, especially for regions of high density. We evaluate the architecture on a benchmark dataset with ten gestures. PointGest achieves an accuracy of 98.8%, outperforming five state-of-the-art point cloud classification models.