Abstract
Underwater wireless communications play a significant role in the Blue Economy, supporting the operations of sensing platforms like Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs). These platforms require reliable and fast communications to transmit the extensive data gathered without surfacing. Yet, the ocean poses challenges to signal propagation, restricting communications to high bitrate at short ranges via optical and RF, or low bitrate at long distances using acoustic communications. This paper introduces Aquacom, a multimodal manager for underwater communications that integrates acoustic, RF, and optical communnications, ensuring seamless handover between technologies and link aggregation to enhance network performance. Upon validation in freshwater tank lab tests, Aquacom demonstrated the capability for switching interfaces without data loss and effective link aggregation through the simultaneous use of multiple wireless interfaces.

Abstract
The exploration of the ocean has got an increasing interest, including activities such as offshore wind farms and deep-sea mining. However, the ocean environment and the high cost of operations, namely for manned missions, have led to the development of Autonomous Underwater Vehicles (AUVs) and other sensing platforms. AUVs play a vital role in these environments, relying on communications systems to operate and exchange sensor data. Yet, reliable and energy-efficient broad-band wireless communications underwater remain an unsolved challenge, despite the recent advances in the field. We present a novel multimodal approach, named DURIUS, that considers the movement of the AUV to convey the sensor data and selects the most suitable underwater wireless communications technology - acoustic, optical or radio - according to the underwater context, targeting maximum performance and minimum energy consumption. Our analytical results show that DURIUS increases data throughput and reduces energy consumption when compared with the state of the art approaches.

Abstract
<p>Flying networks, composed of Unmanned Aerial Vehicles (UAVs) acting as mobile Base Stations and Access Points, have emerged to provide on-demand wireless connectivity, especially due to their positioning capability. Still, existing solutions are focused on improving aggregate network performance using a best-effort approach. This may compromise the use of multiple services with different performance requirements. Network slicing has emerged in 5G networks to address the problem, allowing to meet different Quality of Service (QoS) levels on top of a shared physical network infrastructure. However, Mobile Network Operators typically use fixed Base Stations to satisfy the requirements of different network slices, which may not be feasible due to limited resources and the dynamism of some scenarios.</p> <p>We propose an algorithm for enabling the joint placement and allocation of communications resources in Slicing-aware Flying Access and Backhaul networks – SurFABle. SurFABle allows the computation of the amount of communications resources needed, namely the number of UAVs acting as Flying Access Points and Flying Gateways, and their placement. The performance evaluation carried out by means of ns-3 simulations and an experimental testbed shows that SurFABle makes it possible to meet heterogeneous QoS levels of multiple network slices using the minimum number of UAVs.</p>

Abstract
With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Abstract

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly used as cost-effective and flexible Wi-Fi Access Points (APs) and cellular Base Stations (BSs) to enhance Quality of Service (QoS). In disaster management scenarios, UAV-based networks provide on-demand wireless connectivity when traditional infrastructures fail. In obstacle-rich environments like urban areas, reliable high-capacity communications links depend on Line-of-Sight (LoS) availability, especially at higher frequencies. Positioning UAVs to consider obstacles and enable LoS communications represents a promising solution that requires further exploration and development. The main contribution of this paper is the Traffic- and Obstacle-aware UAV Positioning Algorithm (TOPA). TOPA takes into account the users' traffic demand and the need for LoS between the UAV and the ground users in the presence of obstacles. The network performance achieved when using TOPA was evaluated through ns-3 simulations. The results show up to 100% improvement in the aggregate throughput without compromising fairness.