Collision-free cooperative Unmanned Aerial Vehicle protocols for sustainable aerial services

[EN] Unmanned Aerial Vehicles (UAVs) are offering many global industry sectors the opportunity to adopt more sustainable business models. They offer innovative ways of managing resources and water and offer newer opportunities to address key challenges in many areas like border surveillance, precision agriculture and search and rescue missions. All these new applications areas tend to require the cooperation of groups, or "swarms" of UAVs to provide collaborative sensing and processing solutions. These new scenarios impose new requirements in terms of safety, coordination, and operation management. This paper provides an overview of some of the technical challenges that multirotor UAVs are still facing in terms of aerial coordination and interaction. In this regard, it focusses on recent developments available in the literature and presents some contributions realised during the past few years by the authors addressing UAV interaction to achieve collision-free flights and swarm-based missions. Based on the analysis provided in this work, the paper is able to provide insight into the challenges still open that need to be solved in order to enable effective UAV-based solutions to support sustainable aerial services.Ministerio de Ciencia e Innovacion, Grant/AwardNumber: RTI2018-096384-B-I00Fabra, F.; Vegni, AM.; Loscri, V.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2022). Collision-free cooperative Unmanned Aerial Vehicle protocols for sustainable aerial services. IET Smart Cities. 4(4):231-238. https://doi.org/10.1049/smc2.120282312384

Adaptive Coding and Modulation Aided Mobile Relaying for Millimeter-Wave Flying Ad-Hoc Networks

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate \emph{agility} and \emph{mobility} of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an $\epsilon$-multiple objective genetic algorithm ($\epsilon$-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering $45.38\%$ more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering $26.86\%$ more data than the benchmark solutions when no stationary relay is available

Unmanned Aerial Vehicles (UAVs) for Integrated Access and Backhaul (IAB) Communications in Wireless Cellular Networks

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of UAVs. We consider two spatial configuration modes of UAVs: distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs cannot only be used for coverage improvement but also for capacity boosting in IAB cellular networks