Unmanned Aerial Vehicles (UAVs) are improving considerably search and rescue (SAR) operations by providing unprecedented capabilities in dynamic and hazardous environments. This study presents an innovative, collaborative multi-UAV data fusion approach that addresses the critical challenge of locating multiple moving targets within strict time constraints. This approach improves traditional search techniques by incorporating intelligent information sharing, fusion, and coordinated path planning. The core innovation of the algorithm lies in its ability to dynamically and collaboratively predict the geographical zones with the highest probability of needed rescue operations. This enables the group of UAVs to coordinate and optimize their search strategies in real-time. This research offers valuable insights into multi-UAV collaboration through high-fidelity simulations involving more than 600 different scenarios with UAV swarms and moving ground targets. The experimental results indicate that their effectiveness significantly improves as the number of UAVs increases, following a quadratic trend until it reaches a plateau. In particular, the accuracy rate remains above 90%, regardless of the number of UAVs after reaching the plateau. This suggests that while a higher density of UAVs enhances search efficiency, larger UAV swarms yield diminishing returns. Notably, the approach shows superior efficiency in environments with clustered targets, which makes it particularly suitable for disaster response scenarios that involve more concentrated target locations.

The exponential growth of the Internet of Things (IoT) has introduced unprecedented challenges in data processing, privacy preservation, and energy efficiency. Traditional centralized approaches are often unsuitable for IoT environments due to bandwidth limitations, data heterogeneity, and privacy concerns. This study proposes a novel framework combining federated learning (FL) and autoencoders to address these issues in IoT-based image classification tasks. By lever-aging Unmanned Aerial Vehicles (UAVs) as intermediaries for model aggregation and distribution, the framework minimizes communication overhead while maintaining data privacy. Autoencoders are employed for unsupervised feature extraction, enabling effective data representation even in the absence of labeled data. Results demonstrate that, while autoencoders achieve lower classification accuracy compared to supervised approaches, they provide significant advantages in bandwidth efficiency, scalability, and privacy preservation. The integration of UAVs further enhances the system by optimizing communication and enabling model improvement in real-time. This framework offers a flexible and resource-efficient solution for IoT applications, particularly in scenarios where data labeling is impractical or privacy is paramount.

When dealing with large-scale natural disasters such as floods, landslides, hurricanes, or heavy snowfalls, there are often many victims who are trapped in hard-to-reach places, making the time to locate and rescue them critical. In this context, deploying unmanned aerial vehicles (UAVs) alongside a swarm of unmanned ground vehicles (UGVs) has the potential to speed up the Search and Rescue (SAR) missions, as the collaboration between these agents can combine advantages from both of them. The idea is that any simple UAV equipped GPS and communication capabilities can locate besieged and isolated individuals, referred to as Points of interest (POIs), and when it comes across (flies over) a GPS-limited UGV, it guides the UGVs toward the POIs. In this paper, we explore different approaches to Air-to-Ground (A2G) coordination in a number of distinct scenarios among unmanned vehicles, and through simulation compare their efficiency based of parameters and metrics.

The article presents the GrADyS Framework v2, which is a multi-purpose hybrid testbed for FANETs and ground devices. The framework aims to fill the gap between pure simulations and real-world validations, which remains the challenge within these testbeds. Higher-level simulation models are blended with experiments on low-end hardware by directly deploying simulation codes onto nodes like UAVs and ground sensors. Data is captured during field tests from experiments with UAV’s alongside the ground units with the use of 802.112.4 GHz networks for communication and mobility operational stability checking the bandwidth use. UAV’s performance suggests that the system’s effective working range extends up to 200 meters. This research draws attention to some of the practical difficulties that lie in the transition from computer-modeled environments to real-life tests and are associated with the underlying logistics of employing such models and the context in which they are deployed. This design of the framework assists evolution, eases the construction of more intricate modeling, and enhances the simulation models, which would make it possible to unravel complex FANET systems in real life.

In the research on FANETs (Flying Ad-Hoc Networks) and distributed coordination of UAVs (Unmanned Aerial Vehicles), also known as drones, there are many studies that validate their proposals through simulations. Simulations are important, but beyond them, there is also a need for real-world tests to validate the proposals and enhance results. However, field experiments involving drones and FANETs are not trivial, and this work aims to share experiences and results obtained during the construction of a testbed actively used in comparing simulations and field tests.

Communication approaches for autonomous robots in surveillance missions, remotely acting or collecting point-of-interest data, are widely researched. In this line of research, most works address the use of unmanned aerial vehicles because of the mobility flexibility of these vehicles to cover an area. However, these proposals are verified almost exclusively through network simulations. Simulations are efficient for speeding up experiments. In most cases, most experiments are simulated because of the difficulty of validating a proposal in the real world. Real-world experiences are doubly important because they provide much more robust validation to the proposals, real-world tests can be compared to simulated tests, and the gaps between the results can be used to enrich simulated environments that will be used for validations without real-world tests. In this line, this paper presents tests performed in simulated and real-world environments, compares the results of both experiments and presents how enhancement can be applied.

Unmanned Aerial Vehicle (UAV) are increasingly used as data collectors for Wireless Sensors Networks (WSN) on the ground. Most of current research proposes optimizations for itinerary creation for a single UAV. Contrary to this, our work proposes a distributed algorithm for collecting WSN data using a dynamic set of UAVs, that takes into account that UAVs leave or join the group due to recharging or malfunctions. In our work, we consider that the UAVs only have medium-range communication capability (a few meters) to deliver collected data, similar to the assumptions in related work. Compared to the costly and non-real-time Traveling Salesman Problem (TSP)-approach our algorithm delivers approximately 3% more efficient sensor visiting on certain scenarios without using the optimized tour.

Unmanned Aerial Vehicles (UAVs) are increasingly used as data collectors for Wireless Sensors Networks (WSN) on the ground. Most current research proposes optimizations for the itinerary creation of a single UAV. Our work, however, proposes a distributed algorithm for collecting WSN data using a dynamic set of UAVs. The algorithm takes into account the fact that UAVs could leave or join the group due to either recharging requirements or malfunctions. In our work we also consider the fact that UAVs only have medium-range communication capabilities (a few meters) in order to deliver collected data. Compared to the computationally costly and non-real-time Traveling Salesman Problem (TSP) approach, our algorithm delivers approximately 13% more efficient sensory visiting at certain scenarios without using the optimized tour.