Performance Evaluation of Different ToS Using Heterogeneous Communication Interfaces in FANETs

The use of different types-of-services (ToS), such as voice, data, and video, has become increasingly present in the execution of applications involving networks composed of multiple UAVs. These applications usually require the UAVs to share different ToS in a dynamic and ad-hoc manner, such that they can support the execution of cooperative/collaborative tasks. The use of heterogeneous communication has showed gains in maintaining the connection among highly mobile nodes, while increasing the reliable transmission of data, as is necessary in MANETS, VANETs and, more recently, FANETs. The aim of this paper is to present a performance evaluation of a heterogeneous interface manager (IM), which applies a heuristic to choose the best among several single- and multi-band wireless communication interfaces, including IEEE 802.11n, IEEE 802.11p, IEEE 802.11ac, and IEEE 802.11ax. Simulated scenarios with three, five, and eight UAV nodes are developed by integrating NS-3 and Gazebo simulation tools. The IM performance is analyzed by applying different numbers of interfaces and comparing with interfaces applied homogeneously by defining two set of results, in terms of application and MAC and PHY metrics, respectively. Finally, we also evaluate the associated performance, considering voice, data, and video streaming ToS. The results indicate that the combination of different interfaces has a very powerful effect on maintaining or increasing the communication intensity.

Exploration of Artificial Intelligence Security in Multi Unmanned System Cooperation

In modern science and technology, artificial intelligence has become one of the most important and promising technologies in today’s society and plays a very important role in people’s life. In artificial intelligence, cooperation is a very important research direction, which includes the cooperation between sensors, coordinated man-machine interface and actuators on multiple UAVs. Therefore, based on the exploration of artificial intelligence security, this paper studies artificial intelligence in multi unmanned system cooperation. Firstly, this paper expounds the development of cooperative system, and then describes the purpose of multi unmanned system cooperation. Then, this paper studies the intelligent algorithm applied to the cooperation of multiple unmanned systems in the field of artificial intelligence. Finally, aiming at the existing security problems of artificial intelligence, this paper tests the functions of multiple unmanned systems. The test results show that when multiple unmanned systems work together, the accuracy of artificial intelligence in dealing with things is basically more than 90%. At the same time, it can be nearly 100% scientific, and can budget a variety of treatment schemes. This shows that in the multi unmanned system cooperation, artificial intelligence can almost meet its needs, but it still needs to be further improved.

Cooperative 3D Path Planning of Multi-UAV Via Improved Fruit Fly Optimization

Path planning of multiple unmanned aerial vehicles (UAVs) is a crucial step in cooperative operation of multiple UAVs, whose main difficulties lie in the severe coupling of time and three-Dimensional (3D) space as well as the complexity of multi-objective optimization. For this purpose, the time stamp segmentation (TSS) model is first adopted to resolve the timespace coupling among multiple UAVs. Meanwhile, the solution space is reduced by transforming the multiobjective problem to a multi-constraint problem. In consequence, based on the elite retention strategy, a novel improved fruit fly optimization algorithm (NIFOA) is proposed for multi-UAV cooperative path planning, which overcomes the shortcomings of basic fruit fly optimization algorithm in slow convergence speed and the potentials to fall into local optima. In particular, the multi-subpopulations evolution mechanism is further designed to optimize the elite subpopulation. At last, the effectiveness of the proposed NIFOA has been verified by numerical experiments.

Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

Target Assignment for Cooperative Quadrotors Unmanned Aerial Vehicles

The target assignment for cooperative Unmanned aerial vehicles is a critical topic. Most target assignment approaches yield computational complexity when dealing with multiple UAVs. This work offers target assignment approaches and resolves multiple UAVs problems by introducing a proposal of target assignment approaches. The main contribution of this work is solving the target assignment problem using optimal time approach and gain maximization approach in double stages. At the first stage, the urgently desired target to be fetched from the detected target group is selected by using the proposed objective function based on three attribute values of targets (threating weight - importance weight - separating distance). At the second stage, the proposed objective function is used and assign related UAVs for selected desired targets by considering the coverage factors, adaptive-limitation and constrains.

Flight Planning Optimization of Multiple UAVs for Internet of Things

This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric.

3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

In the last few years, the use of drones is increasing day by day in wireless networks and the applications of them are rapidly increased on different sides. Now, we can use the drone as an aerial base station (BS) to support cellular networks in emergency cases and in natural disasters. To take the advantage of both drones and fifth-generation (5G) and link between their features, we study an aerial BS considering millimeter waves (mm-waves). In this paper, we optimize the 3D placements for multiple unmanned aerial vehicles (UAVs) in an mm-wave network to achieve maximum time durations of the uplink transmission. First, we present a formulation for the placement problem, where we aim to allocate 3D locations for multiple UAVs to achieve the maximum sum of time durations of uplink transmissions. We propose an efficient algorithm to find the placements of UAVs. We propose an algorithm that starts by grouping the wireless devices into a number of clusters, and each cluster is served by a single UAV. After the clustering process, it applies the gradient projection-based algorithm (GP) or particle swarm optimization (PSO) in each cluster. In the results section, our proposed approach and the center projection algorithm will be compared to prove the efficiency of our approach.