The European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) created the Space Resources Challenge to invite researchers and companies to propose innovative solutions for Multi-Robot Systems (MRS) space prospection. This paper proposes the Resilient Exploration And Lunar Mapping System 2 (REALMS2), a MRS framework for planetary prospection and mapping. Based on Robot Operating System version 2 (ROS 2) and enhanced with Visual Simultaneous Localisation And Mapping (vSLAM) for map generation, REALMS2 uses a mesh network for a robust ad hoc network. A single graphical user interface (GUI) controls all the rovers, providing a simple overview of the robotic mission. This system is designed for heterogeneous multi-robot exploratory missions, tackling the challenges presented by extraterrestrial environments. REALMS2 was used during the second field test of the ESA-ESRIC Challenge and allowed to map around 60% of the area, using three homogeneous rovers while handling communication delays and blackouts.

During natural disasters such as earthquakes, wildfires, hurricanes, landslides, tsunamis, CBRNE (chemical, medical, radiological, nuclear, or explosive) incidents, or terrorist threats, critical infrastructure can be severely damaged, with rescue workers facing immense obstacles. Providing help and reaching affected areas can be hazardous for human rescue personnel. To overcome this issue, 5G-enabled UAVs can play a significant role in deploying disaster-resilient networks. Such networks imply multiple challenges, including end-to-end communication reliability and low latency for UAV real-time control. In this paper, we focus on the end-to-end communication aspects of such networks. We present and implement our disaster-resilient network based on a combination of 5G-UAVs and satellite networks. We highlight the related challenges and define solutions based on 5G, Multi-access Edge Computing, UAVs, and dynamic geofencing. We integrate these solutions in an end-to-end network architecture and ultimately deploy it on a national joint 5G-satellite infrastructure to assess its performance. It performs well and demonstrates a recovery time of less than 30 seconds with $\mathbf{9 9 \%}$ network availability.

One of the primary requirements in cellular-enabled multiunmanned aerial vehicle (UAV) Internet of Things (IoT) networks is to preserve data rates according to the IoT users’ (IUs) requirements. The mobility of IUs, 3-D movement of UAVs, environmental conditions, and bandwidth allocation to the IUs increase the challenges to maintain the data rates due to the continual change in the channel state. A constant extraction of channel state is crucial in this regard. We construct a sum-rate maximization problem considering the channel-state information (CSI). Unlike previous work, we propose CSI-driven data rate optimization for multi-UAV IoT networks (CARTEL). First, it allocates optimized bandwidth to IUs and accomplishes UAV–IU associations by adopting the matrix minima method. Subsequently, it maximizes the sum-rate invoking four modules: 1) parameter selector (PS); 2) IU tracker (IT); 3) path-loss estimator (PE); and 4) policy generator (PG). PS, IT, and PE help to extract the CSI by selecting suitable environmental parameters, tracking the IU mobility, and estimating the path loss, respectively. Finally, PG maximizes the data rates by adjusting the 3-D position and transmitting the power of a UAV. Extensive simulation results depict that the sum-rate in CARTEL improves by 26.03% and 65.46% than learn-as-you-fly (LAYF) and random selection (RS), respectively.

One of the crucial challenges in networked Unmanned Aerial Vehicles (UAVs) is to configure them to serve as aerial base stations (BSs) for collecting data from distributed Internet of Things (IoT) devices in a region devoid of backbone connectivity. To address this challenge, it is required to compute optimized trajectories of UAVs to collect data while considering the different activation patterns of IoT devices. We propose a scheme to optimize the trade-off between the number of covered IoT devices and travel time of UAVs. The formulated cost minimization problem is known as the capacitated single depot vehicle routing problem (CSDVRP), which is NP-hard. We propose a solution scheme, named Cost-Effective Data Aggregation for UAV-Enabled IoT Networks (CEDAN), which operates in four steps. First, it determines the optimized hovering locations (HLs) for UAVs. Subsequently, CEDAN determines the optimized route adopting the Christofides's approximation algorithm for Travelling Salesman Problem (TSP). Further, a split function produces the optimized trajectories for all UAVs. Finally, a route adjustment algorithm applies the cost function and rearranges the order of visiting each HL. Extensive simulation results depict that the CEDAN outperforms than Clarke-Wright (CW) savings heuristics, CEDAN without route adjustment (CWRA), and Zhan et al., respectively.

5G-enabled UAV-based services have become popular for civilian applications. At the same time, certain no-fly zones will be highly dynamic, e.g. accident areas, large outdoor public events, VIP convoys etc. An appropriate geofencing algorithm is required to avoid the no-fly zone in such scenarios. However, it is challenging to execute a high computing process such as a geofencing algorithm for a resource constraint UAV. This paper proposes an architecture and a geofencing algorithm for 5G-enabled UAV using Mobile Edge Computing (MEC). Also, the 5G-enabled UAV must fly within the coverage area during a mission. Hence, there must be an optimal trade-off between 5G coverage and distance to travel to design a new trajectory for a 5G-enabled UAV. To this end, we propose a cost minimization problem to generate a new trajectory while a no-fly zone exists. Specifically, we design a cost function considering 5G coverage and the velocity of the UAV. Then, we propose a geofencing algorithm running at the MEC by adopting the fast marching method (FMM) to generate a new trajectory for the UAV. Finally, a numerical example shows how the proposed geofencing algorithm generates an optimal trajectory for a UAV to avoid a dynamically created no-fly zone while on the mission.

This work sketches a location priority-aware service availability scheme for use in a cellular-enabled multiple unmanned aerial vehicle (UAV) networks for Internet-of-Things (IoT) applications. These UAVs have the ability to sense the location-based data employing onboard heterogeneous sensors and send the sensed IoT data to the base station (BS). Existing works treat all locations equally and are inefficient in terms of assigning UAVs to provide on-demand location-based IoT services to the users. To address this issue, we formulate the objective as a optimization problem to maximize service availability. As the formulated objective is NP-hard, we propose a scheme named priority-aware service availability for multi-UAV networks (PRISM) to obtain the solution. PRISM operates in two steps. First, it offers a UAV assignment algorithm to prioritize locations and assign the UAVs to the high-priority locations proactively. Next, it employs a service assignment algorithm that assigns the requested location-based IoT services to UAVs. The results of simulation and real experiments depict the efficacy of PRISM in terms of availability and delay of the incoming IoT service requests. From the results, we observe that PRISM improves the average service availability by 20.61% and reduces average service delay by 29.4%, compared to the benchmark solutions.