Unmanned Aerial Vehicle Cluster Research Articles

A secure and efficient UAV network defense strategy: Convergence of blockchain and deep learning

Unmanned Aerial Vehicles (UAVs) are highly versatile and efficient tools utilized across diverse industries for data collection. However, they are vulnerable to wireless communication and data exchange risks, including unauthorized access, data theft, and network attacks. To address these problems, we introduce a secure and reliable UAV network service architecture that incorporates blockchain and deep learning to provide more secure and efficient network services for UAVs. We propose a UAV cluster identity management module by combining blockchain, encryption algorithms, and digital signatures to enhance the security of UAV communication data transmission. Then, based on machine learning, deep learning, and malicious process detection technology, we propose a real-time secure situational awareness system for UAV cluster terminal devices to enhance the security of the operating environment for UAVs. Finally, we propose a data-trustworthy interconnection platform based on blockchain, smart contracts, and consensus algorithms to realize secure and efficient sharing and transmission of terminal data. The results of the experiments demonstrate the feasibility and effectiveness of our UAV network service architecture.

UAV Cluster Mission Planning Strategy for Area Coverage Tasks.

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%.

Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications

This article investigates the transmission of downlink control signals for multiple unmanned aerial vehicle (UAV) clusters in collaborative search and rescue operations in mountainous environments. In this scenario, a reconfigurable intelligent surface (RIS) mounted on the UAV is utilized to overcome obstacles between the ground base station (BS) and UAVs. By leveraging the fixed channel of the RIS to the BS, the line-of-sight (LoS) path characteristics of the air-to-air channel, and the position information of the UAV, the RIS forms a directional beam by adjusting the RIS coefficient, which points towards UAVs in the cluster. To ensure low delay in control signaling and UAV state transmission, we adopt semi-persistent scheduling (SPS), which allocates pre-specified periodic intervals to each UAV for the formation of corresponding RIS coefficients. The allocation of time slots is constrained by the transmission intervals required by different UAVs and the number of RISs available. We propose a time slot scheduling scheme for UAVs to reduce inter-cluster interference caused by RIS beams. The time slot allocation problem is formulated as a combinatorial optimization problem. To solve this problem, we first propose an intuitive greedy scheme called local interference minimization (LIM). Building upon the LIM scheme, we propose a rollout-based algorithm called rollout interference minimization (RIM). Through simulation, we compare the LIM and RIM schemes with the benchmark scheduling scheme. The results demonstrate that our proposed scheme significantly reduces interference between UAV clusters while satisfying the conditions of periodic transmission and RIS quantity constraints.

Height Sensitive Multi-UAV Deployment Scheme in Edge Data Acquisition System

In data acquisition scenario of edge computing, the optimization of UAV (Unmanned Aerial Vehicle) deployment is of great significance for making use of resources of UAV. We establish an optimization model of UAV cluster deployment in the edge data acquisition system. The model takes the height of UAV as the solving variable, which is more in line with the realistic characteristics. DEVIPSK-SA-FWA is proposed according to the characteristics of this model. The algorithm uses a novel coding mechanism, and uses K-Means to accelerate the convergence process of the algorithm. A variety of differential evolution mutation operators are used to form a self-adaptive strategy pool mechanism to carry out variable scale variation of population, which complete the global search well. Then fireworks algorithm searches the population locally after each round of global search. In our algorithm, global search and local search are well balanced and local optimal is effectively escaped. Finally, experimental results indicate that DEVIPSK-SA-FWA is capable of solving the model with good results, and the superiority of DEVIPSK-SA-FWA is verified through the Wilcoxon rank sum test method. In the best case, the proposed algorithm reduces energy consumption of edge data acquisition system by 32.87[Formula: see text].

A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes

In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.

Research on Unmanned Aerial Vehicle Cluster Collaborative Countermeasures Based on Dynamic Non-Zero-Sum Game under Asymmetric and Uncertain Information

Unmanned aerial vehicle (UAV) swarm coordinated confrontation is a hot topic in academic research at home and abroad, and dynamic maneuver decision-making is one of the most important research fields for UAV countermeasures. Aiming at the complexity, uncertainty and confrontation of UAV cooperative confrontation, concepts such as relative advantage degree and advantage coefficient are introduced, and game theory is used as a framework to construct a dynamic non-zero-sum game UAV cluster cooperative confrontation decision-making model, and finally convert it into an optimization problem. On this basis, using the Nash equilibrium solution method of multi-strategy fusion particle swarm algorithm, by introducing adaptive inertia weight and local mutation strategy, while enhancing the diversity of the population, it can ensure the local accurate search ability of the particle swarm. The simulation results of the example are verified. The effectiveness of the proposed model and method is confirmed.

An apollonius circle based game theory and Q-learning for cooperative hunting in unmanned aerial vehicle cluster

Cooperative hunting has attracted great research interests with both pursuer and evader behavior strategies. Existing approaches typically utilize computing power to improve the accuracy of hunting. However, these methods ignore the real-time characteristic of unmanned aerial vehicle (UAV) cluster and timeliness of hunting process, directly using them into UAV cluster application would lose efficacy. To solve the problem of cooperative hunting of UAV cluster (pursuers) for one intelligent UAV (evader), we propose an apollonius circle based game theory and Q-learning for cooperative hunting (ACGQ-CH). Specifically, it constructs the behavior strategies and payment matrix of the pursuers and the evader by using game theory and apollonius circle. Then, a state-action matrix is built and a dynamically adjusting the greedy factor is designed based on Q-learning algorithm and reward mean, respectively. Finally, it derives Nash equilibrium solution by solving the state-action matrix, and guides the pursuers to achieve cooperative hunting. The simulation results demonstrate our approach reduces the number of learning steps by 50.8% compared to traditional Q-learning and reduces the hunting time by 16.83, 27.35 and 12.56% respectively compared to baseline methods.

A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

The small UAV (unmanned aerial vehicle) cluster has become an important trend in the development of UAVs because it has the advantages of being unmanned, having a small size and low cost, and ability to complete many collaborative tasks. Meanwhile, the problem of GPS spoofing attacks faced by submachines has become an urgent security problem for the UAV cluster. In this paper, a GPS-adaptive spoofing detection (ASD) method based on UAV cluster cooperative positioning is proposed to solve the above problem. The specific technical scheme mainly includes two detection mechanisms: the GPS spoofing signal detection (SSD) mechanism based on cluster cooperative positioning and the relative security machine optimal marking (RSOM) mechanism. The SSD mechanism starts when the cluster enters the task state, and it can detect all threats to the cluster caused by one GPS signal spoofing source in the task environment; when the function range of the mechanism is exceeded, that is, there is more than one spoofing source and more than one UAV is attacked by different spoofing sources, the RSOM mechanism is triggered. The ASD algorithm proposed in this work can detect spoofing in a variety of complex GPS spoofing threat environments and is able to ensure the cluster formation and task completion. Moreover, it has the advantages of a lightweight calculation level, strong applicability, and high real-time performance.

UAV cluster detection algorithm based on weighted tri-layer window local contrast

Infrared images of unmanned aerial vehicle (UAV) cluster targets are characterized by the low signal-to-noise ratio (SCR), closely spaced, and limited detail characteristics, which exacerbate the detection complexity of contemporary infrared (IR) small target detection algorithms. This paper proposes a weighted tri-layer sliding window local contrast (NTLLCM) algorithm for UAV cluster targets detection. Firstly, the IR imaging characteristics of UAV cluster targets are modeled and evaluated. Secondly, after evaluating the limitations of LCM, the local contrast of the filtered images is calculated by designing a tri-layer sliding window structure with core, middle, and outermost layers. The local contrast is then weighted utilizing a new regional intensity level (NRIL) strategy to suppress the background further, and the target is detected by adaptive threshold segmentation. Finally, a considerable number of experimental results illustrate the superiority of this algorithm in detecting UAV cluster targets under sophisticated backgrounds, especially the proposed algorithm detects multiscale targets utilizing single-scale windows instead of using multiscale windows, which significantly decreases the computationally costs. Furthermore, the algorithm can reduce the overlapping effect of UAV cluster target imaging and correctly detect closely spaced targets.

A Study of Collaborative Trajectory Planning Method Based on Starling Swarm Bionic Algorithm for Multi-Unmanned Aerial Vehicle

This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV’s physical model properties.

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm.

Damage study of UAV cluster by high-power electromagnetic radiation

With regard to the threat of Unmanned Aerial Vehicle(UAV) clusters, the mechanism and rule of the damage to UAV cluster caused by electromagnetic wave is explored in this paper. A typical satellite navigation antenna high power electromagnetic radiation is studied with the experimental and simulation, in which, the damage sensitive parts and threshold of damage is obtained, which provides support for interference and damage UAV cluster of enemy and protection research for UAV of our own.

Drone Swarm Path Planning for Mobile Edge Computing in Industrial Internet of Things

Drone-swarm-assisted mobile edge computing (MEC) provides extra computation and storage capacity for smart city applications and the industrial Internet of Things. To solve the problems of traditional fixed base stations in complex terrain, including cost of deployment, transmission loss of telecommunication, and limited coverage, this paper brings forward the unmanned aerial vehicles (UAVs) as mobile edge computing nodes in the air. For the purpose of matching the dynamic mobile devices and UAV trajectory, this paper raises a multi-UAVs-assisted mobile edge computing offloading algorithm based on global and local path planning controlled by ground station and onboard computer. Firstly, this paper considers a drone swarm scheduling and allocation strategy based on the priority of monitoring areas, UAVs residual energy and distance to target points, so that to minimize the global flight length and energy consumption. Secondly, based on user mobility, this paper calculates the optimal communication coverage of UAV, and jointly optimizes the local path planning and computing offloading, so that to maximize the number of offloading services and minimize the total latency in completing the computation task. Finally, based on the total latency and energy consumption of path planning and computation offloading, a UAV cluster computation offloading strategy with optimized energy efficiency is realized. Experimental results prove that the proposed algorithm can provide more offloading services while obtaining shorter path length and greater energy efficiency.

Survivability Analysis of Unmanned Aerial Vehicle Network based on Dynamic Weighted Clustering Algorithm with Dual Cluster Heads

The unmanned aerial vehicles (UAVs) network is vulnerable due to the high mobility and energy-constrained characteristics of UAVs. Nonetheless, as a UAV-based communication network, a stable network topology is crucial for efficient communication. To this end, in this paper, we propose a dynamic weighted clustering algorithm with dual cluster heads (DWCA-DCH) in this paper to deploy the UAV network. To trade off communication efficiency and lifetime, the selection of prime and backup cluster heads is designed by synthetically considering communication quality and remaining energy of the UAV. Furthermore, a survivability analysis method based on Markov process (SAM-MP) is constructed to analyze the survivability performance of the proposed UAV network based on DWCA-DCH when the UAV node suffers from energy exhausting or accidents. The simulation results show that the survivability and stability of the UAV cluster ad hoc network based on DWCA-DCH proposed in this paper is improved by about 35% compared with the single cluster head network.

Research on the damage effectiveness of AHEAD against the LSS UAV clusters

With the expansion of unmanned aerial vehicle(UAV) applications and increased power, the status of unmanned aerial vehicles in the military field has continued to improve. How to effectively defend against UAV attacks has become a key research issue in various countries. The damage assessment model of the AHEAD in the literature [17] does not consider the systematical error of anti-aircraft artillery, and ignores the dispersion error of the AHEAD explosion-points in the direction of the vertical interception plane, which leads to the inaccuracy of the calculated damage probability. In this paper, the model is modified, the systematical error and the dispersion error of the opening point in the direction of the vertical interception plane are added to the original model, and then the improved model is used to analyze the damage effectiveness of the AHEAD against two typical LSS UAVs. It shows that the AHEAD has good damage efficiency to LSS UAVs. To a certain extent, it has the ability to counter the LSS UAV clusters.

A Distributed Algorithm for UAV Cluster Task Assignment Based on Sensor Network and Mobile Information

Cluster formation and task processing are standard features for leveraging the performance of unmanned aerial vehicles (UAVs). As the UAV network is aided by sensors, functions such as clustering, reformation, and autonomous working are adaptively used for dense task processing. In consideration of the distributed nature of the UAV network coupled with wireless sensors, this article introduces a Rational Clustering Method (RCM) using dense task neighbor information exchange. The Rational Clustering Method (RCM) is an algorithm for dense task neighbor information exchange that can be used to cluster objects according to their shared properties. Each object’s task neighbors, and the similarities between them, are calculated using this method. Starting with the task density of its neighbors, the RCM algorithm gives each object in the dataset a weight. This information exchange process identifies a UAV units’ completing tasks and free slots. Using this information, high-slot UAVs within the communication range can be grouped as clusters. Unlike wireless sensor clusters, task allocation is performed on the basis of available slots and UAV longevity within the cluster; this prevents task incompletion/failures and delays in a densely populated UAV scenario. Cluster sustainability or dispersion is recommended when using distributed state learning. State learning transits between the pending task and UAV longevity; an intermediate state is defined for task reassignment amid immediate cluster deformation. This triple feature-based distributed method balances tasks between failures, overloading, and idle UAVs. The RCM was verified using task processing rate, completion ratio, reassignment, failures, and delay. Task processing rate was increased by 8.16% and completion ratio was increased by 10.3% with the proposed RCM-IE. Reassignment, failure, and delay were all reduced by 12.5%, 9.87%, and 11.99%, respectively, using this method.

A Task Planning Method for UAV Swarm Dynamic Reconstruction Based on a Fourth-Order Motif

The primary purpose of task allocation is to build each equipment platform and quickly complete integration planning at the actual combat speed to achieve efficient management of the entire task. In this process, higher requirements are put forward for dynamic, cooperative, and highly adaptive drone colony organization. In this paper, the scheduling problem of hybrid unmanned aerial vehicle (UAV) systems is studied under an uncertain environment. First, the system-capability-task organizational structure is defined and quantified, which lays a foundation for dynamic adjustment of the organizational structure in the future. Then, combined with the theory of flexible network and elastic network management, the model is calculated, and the linear transformation function and fuzzy theory are used to stratify and cluster the capability layers. On this basis, four motif structures are introduced for abnormal nodes in the process of dynamic adjustment, and a dynamic group reconstruction algorithm (DRA-M) is established. Finally, the time and communication load indexes are determined, and the alternative strategy is designed for the failure point. The performance of the classical scheduling algorithm is evaluated by benchmarking it under different conditions. The results show that the algorithm has a good dynamic adjustment ability in the event of a UAV swarm emergency, which is a bright light for the future study of highly adaptive UAV cluster organization.

Missing data reconstruction in attitude for quadrotor unmanned aerial vehicle based on deep regression model with different sensor failures

Forming a UAV cluster with multiple UAVs to collaborate in accomplishing tasks is the future development of battlefield. The UAV cluster can collaborate by communicating through inter-aircraft links and can quickly and accurately perform complex tasks such as path planning, collaborative reconnaissance, collaborative sensing and attack. In the process of Unmanned Aerial Vehicle (UAV) cooperative operation, obtaining the real-time attitude information of each UAV is preliminary to implement collaborative cooperation. However, in practice, due to the internal malfunction of sensors and mutual interference among UAVs, the acquired attitudes often suffer from data missing. This paper proposed a novel missing data reconstruction method for UAV’s attitude in the case of sensor failures. The attitude data of UAV is obtained through an advanced UAV simulation platform AirSim. Through fusing the temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM), a deep regression framework is established for missing data construction. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models and different combinations of data missing. The performance of our method is quantified using metrics of root-mean-square-error (RMSE), mean absolute error (MAE), determination coefficient (DC), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) score. The averaged performance values of our proposed method outperform other baseline algorithms. The results of the experiment show that the study could well-address the issue of missing data reconstruction and provide reliable attitude data.

UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net

Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is a large and complex real-time feedback system that integrates a communication and sensor network, control system, calculation, and physical process. The heterogeneous UAVs conduct complex behaviors, which requires a comprehensive description and analysis of UAV cluster modeling. The integrated modeling of the heterogeneous UAV cluster is of great significance and value to test and verify the new combat mode. In this paper, we present a novel representation framework based on the Petri nets. We used a spatial-temporal hybrid Petri net to illustrate the discrete state and continuous process of a heterogeneous UAV cluster system, and effectively achieved the fusion of a physical and computational process and interaction event modeling in the cluster system. Finally, the formal verification of UAV cluster attack mission modeling was carried out by UPPAAL, suggesting the proposed modeling method is feasible and effective.