Drones, particularly when deployed in swarms, hold immense potential for various applications, such as aerial imaging, delivery services, disaster response, and advanced surveillance. Their effective and efficient use, however, hinges on the accurate estimation of energy consumption. This study focuses on determining energy consumption patterns for small drones weighing less than 2 kg and with diameters under 1 m. Through an extensive series of test flights, empirical energy consumption data was collected and used to develop four distinct energy models: a theoretical model based on fundamental flight forces, a simple linear regression model, a cubic polynomial regression model, and a machine learning-based model using the XGBoost Regressor algorithm. A cost function for swarm topology control was also derived for assessing energy consumption during various activities, like connectivity restoration and formation change, facilitating more energy-efficient decision-making in swarm operations. Our findings highlighted an energy-efficient “valley” within the airspeed range, indicating that flying at speeds outside this range results in higher energy consumption. The machine learning model demonstrated superior accuracy within its training range, achieving an R$^2$of 0.9999, whereas the polynomial regression model was deemed best for extrapolation purposes, delivering an R$^2$of 0.966. Simple linear regression and theoretical models, although less accurate, can offer quick energy demand calculations and insights into the effects of hardware modifications, respectively.

Maintenance of connectivity in mobile ad hoc networks (MANETs) and especially in flying ad hoc networks, consisting of unmanned aerial vehicles (UAV), has crucial importance. Missions planned within these types of networks can be interrupted due to node failures, link errors, etc. This case becomes more critical when the application is heavily communication-dependent. To alleviate such problems, in this article, we propose a distributed proactive$k$-connectivity recovery algorithm (DP$k$CR) for UAV-based MANETs. To this end, the algorithm has been developed and tested by providing realistic scenarios. The time and message complexity analysis of the algorithm is presented. Moreover, to analyze the performance of the proposed algorithm, we compared it with other$k$-connectivity restoration algorithms in the literature. Simulation results revealed that DP$k$CR outperforms the alternatives in terms of convergence time for the recovery phase and, subsequently, in terms of energy consumption. Furthermore, DP$k$CR provides improvements to the bandwidth requirements for the restoration.

Random geometric graphs can be used in constructing topology formations for wireless ad-hoc networks (WANETs), including wireless sensor networks, flying ad-hoc networks, and others. They are useful for energy-saving schemes where a randomly alternating subset of deployed nodes is turned off temporarily (without compromising the network connectivity). This also improves network security by periodically rerouting the data flow, which makes it harder for third parties to run a traffic analysis. In the area and target coverage scenarios, a WANET deployment is desired to have maximal area coverage efficiency, which implies covering the largest possible area using the fewest number of nodes by maintaining the connectivity as well. Although deterministic topology formations offering optimal area coverage are known, a random node deployment method that grants connectivity and area coverage maximality has not been presented, to the best of our knowledge. This study introduces a novel topology formation method, called CoRMAC, that consistently yields tree-formed random geometric graphs that are guaranteed to be connected and offer maximal area coverage for any given area size and node cardinality. The area coverage maximality of CoRMAC is formally proven. Extensive theoretical and computational analyses have been provided to demonstrate its graph-theoretic, Euclidean, and networking features. Moreover, comparisons were made with other approaches to show the effectiveness of the proposed method.

For many wireless ad-hoc network (WANET) applications, including wireless sensor, robotic, and flying ad-hoc networks, area coverage is a major challenge. This challenge, which may include the number of required nodes, cumulative energy consumption, or total distance travelled, involves covering the largest possible area with the least cost. Whatever the scenario and cost functions, efficiently deploying the nodes throughout their entire missions is an important factor. For larger networks, this can best be performed using proper topology formations that are essentially designated geometric graph patterns. As a network topology formation and node deployment strategy, this study presents a unique tree-formed geometric graph pattern. With any given number of nodes, it guarantees the maximum possible combined area coverage. Considering its graph theory and Euclidean characteristics, the pattern is then described as both algebraic and algorithmic relations. The formation is remarkably scalable because it may include an unlimited number of nodes while ensuring connectivity in a tree topology. In addition to presenting a breakdown of multiple features and attributes, including symmetry and fractality, we will make a comparative analysis with six known regular lattices and a line formation. Our analyses then show that MAX-Tree covers up to twice as much area compared to all other regular lattices of the same node cardinalities while being more robust and reliable than a line formation. MAX-Tree can be extremely beneficial for drone networks, smart industry and agriculture applications, ocean-bed monitoring systems, and many other WANET scenarios.

Electroencephalography (EEG) signal artefacts cause some problems in processing and analysis of EEG signals. There are several options in the literature to identify artefacts in signal areas. However, high accuracy level in artefact detection was not achieved with these methods. New methods are needed to increase the level of accuracy. Semantic segmentation algorithms, which are widely used in image processing, were used for the first time in this study to detect eye‐blink artefacts in EEG signals. In the proposed method, EEG recordings obtained from four channels were divided into 10‐s segments. These segments were converted into images in 256 × 256 resolution and were labelled as eye‐blink, not‐blink and background. The algorithm was trained with these labelled images. The trained algorithm was tested with images containing 1630 eye‐blink and not‐blink signal segments obtained from single‐channel and multi‐channel EEG signals. Classification accuracy of the algorithm was 94.4%. The proposed method successfully detected eye‐blink artefacts in simultaneous multi‐channel signal images.