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Self-organized wireless networks based on unmanned aerial vehicles (UAVs) are receiving increasing attention due to their flexible deployment opportunities. Typically, UAVs are used as supplementary access points to existing ground infrastructure to extend network coverage and capacity. Therefore, finding the optimal position for each UAV is a crucial task that can be resolved by multiobjective optimization. In this paper, a self-organizing UAV swarm model is introduced as an evolutionary type of heuristic that can be adjusted in real time based on the network’s key performance indicators (KPIs). The exogenous dynamics of the wireless communication system are reflected by an end-user mobility model based on Ornstein-Uhlenbeck processes. To achieve versatility in the positioning of UAVs, a model of virtual forces is adopted to enable control of the movement of each UAV via a collection of persistent and mutable parameters of the system. Evolution is controlled by eleven strategic versions of parametric mutations, including a neutral benchmark process. The simulation results reveal that the suggested solution allows for a substantial increase in the KPI efficiency metrics relative to those of the nonmutated system. The results also addressed the presence of the Pareto front, where it is not possible to improve the selected KPIs without negatively affecting others. While existing research that focuses on evolutionary methods usually considers offline approaches, this work uses an online evolutionary approach that is able to solve the presented problem by taking the dynamic nature of the environment into account.

Recently, unmanned aerial vehicle (UAV) remote sensing has been widely used in emergency scenarios; the operating mode has transitioned from one UAV to multiple UAVs. However, the current multiple-UAV remote sensing mode is characterized by high labor costs and limited operational capabilities; meanwhile, there is no suitable UAV swarm scheduling method that can be applied to remote sensing in emergency scenarios. To solve these problems, this study proposes a UAV swarm scheduling method. Firstly, the tasks were formulated and decomposed according to the data requirements and the maximum flight range of a UAV; then, the task sets were decomposed according to the maximum flight range of the UAV swarm to form task subsets; finally, aiming at the shortest total flight range of the task subsets and to balance the flight ranges of each UAV, taking the complete execution of the tasks as the constraint, the task allocation model was constructed, and the model was solved via a particle swarm optimization algorithm to obtain the UAV swarm scheduling scheme. Compared with the direct allocation method and the manual scheduling methods, the results show that the proposed method has high usability and efficiency. [ABSTRACT FROM AUTHOR]

Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of 94 % in the case of 1000 nodes compared with existing methods with the same level of global energy consumption. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms can be applied for the pre-sorting step of radar signal sorting. It can effectively dilute the pulse stream and prevent the dense pulse stream from interfering pulse repetition interval (PRI) extraction. However, the pre-sorting deviation will cause interference and missing pulses during the main sorting process. To solve this problem, we deploy the unmanned aerial vehicle (UAV) swarm to monitor reconnaissance areas and put forward a novel deep transfer learning based signal sorting method. The UAV swarm can collect the pulses from different time and spatial domains, and interference and missing pulses in main sorting processing can be relieved dramatically. In our model, we pre-train our model with the data collected from multiple source areas, which corresponds to different areas detected by different parts of UAV swarms. Then we fine-tune our model with the data of the target area. The experimental results prove that the signal sorting accuracy of methods based on deep transfer learning, i.e., YOLO-MobileNet, F-RCNN and cascade RCNN, are higher than that of the baseline methods. In addition, the signal sorting accuracy of traditional methods based on deep learning can be greatly improved with the help of transfer learning. • The reconnaissance architecture is constructed by UAV swarms. • Deep transfer learning is used to improve the sorting accuracy. • Experiment results verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment. [ABSTRACT FROM AUTHOR]

Seeking to give unmanned aerial vehicles (UAVs) a higher level of autonomous control, this study uses edge computing systems to replace the ground control station (GCS) commonly used to control UAVs. Since the GCS belongs to the central control architecture, the edge computing system of the distributed architecture can give drones more flexibility in dealing with changing environmental conditions, allowing them to autonomously and instantly plan their flight path, fly in formation, or even avoid obstacles. Broadcast communications are used to realize UAV-to-UAV communications, thus allocating tasks among a swarm of UAVs and ensuring that each individual UAV collaborates as an integrated member of the group. The dynamic path programming problem for UAV swarm missions uses a two-phase tabu search with a 2-Opt exchange method and an A* search as the path programming algorithm. Distance is taken as a cost function for path programming. The turning points of no-fly zones are then increased and expanded based on drone fleet coverage, thus preventing drones from entering prohibited areas. Unlike previous work, which mostly considers only single no-fly zones, this approach accounts for multiple restricted areas, ensuring that a UAV swarm can complete its assigned task without violating no-fly zones. A drone encountering an obstacle while traveling along the route set by the algorithm will update the map information in real time, allowing for instant recharting of the optimal path to the goal as a reverse search using the D* Lite algorithm. [ABSTRACT FROM AUTHOR]

Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods. [ABSTRACT FROM AUTHOR]

The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we study the self-healing problem of unmanned aerial vehicle (UAV) swarm network (USNET) that is required to quickly rebuild the communication connectivity under unpredictable external destructions (UEDs). Firstly, to cope with the one-off UEDs, we propose a graph convolutional neural network (GCN) that can find the recovery topology of the USNET in an on-line manner. Secondly, to cope with general UEDs, we develop a GCN based trajectory planning algorithm that can make UAVs rebuild the communication connectivity during the self-healing process. We also design a meta learning scheme to facilitate the on-line executions of the GCN. Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs. The simulation results also show that the meta learning scheme can not only enhance the performance of the GCN but also reduce the time complexity of the on-line executions. [ABSTRACT FROM AUTHOR]

In this paper, we study the multi-task cooperation problem for unmanned aerial vehicle (UAV) swarms, where the UAV energy consumption is taken into consideration during location scheduling and task implementation. One task may need the cooperation of several UAVs with specific capabilities. To avoid unreasonable task allocation, we quantify the mission properties of UAVs and task areas. We comprehensively consider the overlapping and complementary relationship of the UAV's task types, so that UAVs can form corresponding collective execution tasks according to the task attributes. Based on the coalition game theory, we model the distributed task assignment problem of UAVs as a coalition formation game (CFG). We propose a task allocation algorithm, and then prove that it can achieve the joint optimization of energy and task completion by decision-making of UAVs in finite iterations. With the equilibrium properties of coalition formation in UAV networks, we further optimize the position of UAVs to minimize the network energy consumption. Simulation results verify that the proposed method can reduce the flight loss with high task completion degree.

UAV swarm are often subjected to random interference or malicious attacks during the execution of their tasks, resulting in UAV failure or communication interruption. When the UAV swarm is out of interference or the repair command is executed, the performance of the UAV swarm will be restored to a certain extent. However, how to measure the changes of UAV swarm’s performance during this process will be very important, and it is also crucial to determine whether the UAVs can continue to perform its mission. Based on this motivation, we propose a resilience assessment framework for UAV swarm considering load balancing after UAV swarm suffer from disturbances. We analyze the effects of different topologies and different parameters on the resilience of UAV swarm. The study found that attack intensity is the most important factor affecting UAV swarm performance. As the attack intensity increases, the performance of the UAV swarm decreases rapidly. At the same time, topology also has a very important impact on UAV swarm resilience.

Aiming at anti Unmanned Aerial Vehicle (UAV) swarm, this paper studies the detection and suppression mechanisms of emergence in cooperative flight. Cooperative fly is one of the critical operations for UAV swarm in both military and civilian utilities, which allows individual UAVs to distributed adjust their velocity to head for a common destination as well as avoid a collision. This process is viewed as the emergence of complex systems. An emergence detection algorithm based on double thresholds is proposed. It simultaneously monitors the cooperative flight process and system connectivity to accurately identify the occurrence, achievement, or failure of cooperative fly, which provides a solid prerequisite for the suppression mechanism. For suppression, in-band radio interference is designed under the constraint of average power, and the effect is modeled from the perspective of degrading the communication performance of the target system. It is found that low-intensity continuous interference can effectively delay the cooperative fly process and has better concealment, while medium-intensity continuous interference can rapidly stop that process. Based on the above analysis, for the first time, two countermeasures for the UAV swarm’s cooperative fly are designed for the operation intent of delay and disruption of the target UAC swarms, respectively. Simulation results show the effectiveness of the countermeasures.

An unmanned aerial vehicle (UAV) swarm has broad application prospects, in which scheduling is one of the key technologies determining the completion of tasks. A market-based approach is an effective way to schedule UAVs distributively and quickly, meeting the real-time requirements of swarm scheduling without a centre. In this paper, a double-loop framework is designed to enhance the performance of scheduling, where a new task removal method in the outer loop and a local redundant auction method in the inner loop are proposed to improve the optimization of scheduling and reduce iterations. Furthermore, a deadlock detection mechanism is introduced to avoid endless loops and the scheduling with the lowest local cost will be adopted to exit the cycle. Extensive Monte Carlo experiments show that the iterations required by the proposed method are less than the two representative algorithms consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, and the number of allocated tasks is increased. In addition, through the deadlock avoidance mechanism, PI can completely converge as the method in this paper.

In recent years, significant progress has been made in the multi-target tracking (MTT) of unmanned aerial vehicle (UAV) swarms. Most existing MTT approaches rely on the ideal assumption of a pre-set target trajectory. However, in practice, the trajectory of a moving target cannot be known by the UAV in advance, which poses a great challenge for realizing real-time tracking. Meanwhile, state-of-the-art multi-agent value-based methods have achieved significant progress for cooperative tasks. In contrast, multi-agent actor-critic (MAAC) methods face high variance and credit assignment issues. To address the aforementioned issues, this paper proposes a learning-based factored multi-agent soft actor-critic (FMASAC) scheme under the maximum entropy framework, where the UAV swarm is able to learn cooperative MTT in an unknown environment. This method introduces the idea of value decomposition into the MAAC setting to reduce the variance in policy updates and learn efficient credit assignment. Moreover, to further increase the detection tracking coverage of a UAV swarm, a spatial entropy reward (SER), inspired by the spatial entropy concept, is proposed in this scheme. Experiments demonstrated that the FMASAC can significantly improve the cooperative MTT performance of a UAV swarm, and it outperforms existing baselines in terms of the mean reward and tracking success rates. Additionally, the proposed scheme scales more successfully as the number of UAVs and targets increases.

Wireless ad hoc networks are the solution for providing network connectivity in challenging environments with a lack or absence of infrastructure. Data transmission in such networks typically adopts geographic routing protocols, which use geographic coordinates as addresses of network devices. However, geographic coordinates are not always obtainable, as traditional localization systems (GNSS, Wi-Fi, terrestrial infrastructure) might not be available due to signal loss. In this paper, we propose a method that assigns virtual coordinates to network nodes, which can be used as input for geographic routing protocols. The numerical results demonstrate the high topological similarity between the physical and the virtual network. Our method exhibits convergence advantages over conventional approaches and outperforms them in terms of the total number of discovered paths.

Unmanned aerial vehicle (UAV) detection has the advantages of flexible deployment and no casualties. It has become a force that cannot be ignored in the battlefield. Scientific and efficient mission planning can help improving the survival rate and mission completion rate of the UAV search in dynamic environments. Towards the mission planning problem of UAV collaborative search for multi-types of time-sensitive moving targets, a search algorithm based on hybrid layered artificial potential fields algorithm (HL-APF) was proposed. This method consists of two parts, a distributed artificial field algorithm and a centralized layered algorithm. In the improved artificial potential field (IAPF), this paper utilized a new target attraction field function which was segmented by the search distance to quickly search for dynamic targets. Moreover, in order to solve the problem of repeated search by the UAV in a short time interval, a search repulsion field generated by the UAV search path was proposed. Besides, in order to solve the unknown target search and improve the area coverage, a centralized layered scheduling algorithm controlled by the cloud server (CS) was added. CS divides the mission area into several sub-areas, and allocates UAV according to the priority function based on the search map. The CS activation mechanism can make full use of prior information, and the UAV assignment cool-down mechanism can avoid the repeated assignment of the same UAV. The simulation results show that compared with the hybrid artificial potential field and ant colony optimization and IAPF, HL-APF can significantly improve the number of targets and mission area coverage. Moreover, comparative experiment results of CS mechanism proved the necessity of setting CS activation and cool-down for improving the search performance. Finally, it also verified the robustness of the method under the failure of some UAVs. [ABSTRACT FROM AUTHOR]

We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information. [ABSTRACT FROM AUTHOR]

An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable configuration. The proposed topology can make the swarm remain robust in spite of the large number of UAVs. Then a potential function-based controller is developed to control the UAVs in realizing autonomous configuration swarming under the consideration of mutual collision, and the stability of the controller from the individual UAV to the entire swarm system is analyzed by a Lyapunov approach. Afterwards, a yaw angle adjustment approach for the UAVs to reach consensus is developed for the multi-layer swarm, then the direction state of each UAV converges with a fast rate. Finally, simulations are performed on the large-scale UAV swarm system to demonstrate the effectiveness of the proposed scheme.

In recent years, with the continuous development of UAV technology, the application of the UAV swarm in the military has been a global focus of research. Although it can bring a series of benefits in autonomous cooperation, the traditional UAV management technology is prone to hacker attacks due to many security issues, such as a single point of failure brought by centralized management and the lack of reliable identity authentication. This paper studies the advantages and the recent advances of the blockchain in UAV swarm, proposes a blockchain-based UAV swarm identity management model (B-UIM-M), and establishes a distributed identity authentication scheme based on the distributed identity identifier (DID) under this model. Moreover, to ensure the safe transmission of UAV communication data, a secure communication architecture based on blockchain and a set of secure transmission protocols were designed, combined with cryptography. In the current military field, there is no similar application case of the UAV swarm identity management model and distributed identity authentication. The feasibility and security of the proposed scheme are proved through experiments and security analyses.

Recently, micro-UAV swarm-based antenna arrays have been exploited as a novel solution to support sensing and communication applications. However, positional perturbations represent a challenge while deploying the micro-UAV swarm, which may cause deterioration of the UAV array spatial spectrum and limit the array aperture to coherently resolve a target. This letter studies the impacts of positional errors on the direction-of-arrival (DoA) estimation and spatial spectrum of the micro-UAV swarm-based array deployed according to the relative position-based formation, where the perturbations of one UAV may be inherited by its following UAVs. Additionally, we study the beamforming of the micro-UAV swarm-based antenna array either with and without the beamformers’ power constraint. Moreover, asymptotic performance has been examined with massive number of micro-UAVs. Several important insights are gained, and are verified through numerical simulations. [ABSTRACT FROM AUTHOR]

This paper introduces a collaborative allocation model designed for multiple UAVs and diverse targets in maritime combat situations. The model incorporates factors such as distance, angle, interception rate, and recognition rate to comprehensively represent the UAVs' overall damage advantage against targets. Given the complexity of real-world environments and real-time demands, large-scale UAV swarm missions necessitate swift and effective responses. To address this, the paper proposes a Two-Stage Greedy Auction Algorithm, enabling the rapid and efficient completion of cooperative strike tasks within large-scale UAV swarms while preventing deadlock occurrences. In the initial allocation stage, the entropy weight method is utilized to assess task advantages, ensuring a rational allocation criterion for various metrics during the strike process. Subsequently, to enhance the overall effective strike rate within all constraints, a reassignment algorithm is designed based on effective strike benefit indices and the initial assignment result. Simulation results demonstrate the algorithm's quick and stable running time in small-scale and large-scale scenarios. [ABSTRACT FROM AUTHOR]

The mobility of both unmanned aerial vehicles (UAVs) and users, often results in frequent interruptions of communication links, affecting network communication performance. This problem can be solved through reasonable UAV location deployment. This paper proposes an integrated NISAC-EKF algorithm to enhance the localization deployment capability of UAV swarms. Initially, the nonlinear autoregressive exogenous model (NARX) is trained on crowd location data in a communication dataset, and integrated sensing and communication (ISAC) techniques are used to achieve communication-aided sensing. Subsequently, the extended Kalman filter (EKF) is enhanced by introducing probabilistic switching weights into the prediction of location errors, using interactive multiple model. Finally, the sequence predicted by NARX guides the EKF inputs, and the movement direction of the ground crowd adjusts the UAV location for effective demand tracking. Simulation results demonstrated that the NISAC-EKF algorithm ensures excellent performance in terms of network communication capacity and communication rate, even in the presence of varying ground demand. [ABSTRACT FROM AUTHOR]

This paper addresses the global optimal consensus problem with time-varying objective functions and state-coupled inequality constraints. By integrating the predefined-time consensus and prediction-correction schemes, a distributed optimization protocol is proposed for the cooperative interception scenarios involving the unmanned aerial vehicle (UAV) swarm. Specifically, the δ -exact penalty method is utilized to eliminate the coupled constraints. A prediction-correction term with the penalty function is then established to track the trajectory of the time-varying optimal solution. Additionally, an edge-based predefined-time consensus term is introduced to facilitate the state consensus among UAVs. Under the undirected and connected topology, the proposed protocol ensures the predefined-time state consensus and asymptotically minimizes the sum of individual convex objective functions. Furthermore, the protocol is extended to other scenarios involving switching topology, directed detail-balanced topology, and external disturbances. Numerical simulations illustrate that the UAV swarm can form the predesigned interception formation with a predefined-time frame and effectively track the target swarm. [ABSTRACT FROM AUTHOR]

Unlike aerial base station enabled by a single unmanned aerial vehicle (UAV), aerial coordinated multiple points (CoMP) can be enabled by a UAV swarm. In this case, the management of multiple UAVs is important. This paper considers the power allocation strategy for a UAV swarm-enabled aerial network to enhance the physical layer security of the downlink transmission, where an eavesdropper moves following the trajectory of the swarm for better eavesdropping. Unlike existing works, we use only the large-scale channel state information (CSI) and maximize the secrecy throughput in a whole-trajectory-oriented manner. The overall transmission energy constraint on each UAV and the total transmission duration for all the legitimate users are considered. The non-convexity of the formulated problem is solved by using max-min optimization with iteration. Both the transmission power of desired signals and artificial noise (AN) are derived iteratively. Simulation results are presented to validate the effectiveness of our proposed power allocation algorithm and to show the advantage of aerial CoMP by using only the large-scale CSI.

This paper proposed an unmanned air vehicle (UAV) swarm task and resource dynamic assignment algorithm based on task sequence mechanism. By establishing a task sequence, each UAV strictly separates the necessary task time and synchronization waiting time. For the found new targets, each UAV quickly determines its available time period. According to the available time and task resources, an auction algorithm and consensus algorithm are used to decompose the task assignment into initial distributed assignment phase and swarm consensus phase to develop real-time conflict-free task solutions for UAV swarm. The simulation experiment verified that the algorithm could assign different tasks to UAVs in a real-time distributed manner with the limited resources of each UAV.

In this paper, cooperative attack control law analysis and design problems for unmanned aerial vehicle (UAV) swarm attack on ground-moving target with time-coordinated strategies are investigated. First, the time-coordinated control problem for a single UAV is formulated, which is the foundation of solving the problem of a single UAV arriving at the desired attack position relative to ground-moving target at a specific terminal time. Then, relative motion between each UAV and ground-moving target is considered as a finite-time time-varying tracking system problem, and the difference between expected output and system output is defined as tracking error vector. The control law is obtained by linear quadratic optimal control theory to minimize the energy cost in the whole process and the tracking error at the terminal time. Besides, time-coordinated function, which is critical to coordinate terminal time among all UAVs in the UAV swarm, is proposed to model time-coordinated strategies. Finally, numerical simulations show that the proposed control law can steer UAV swarm to arrive at the desired attack positions and achieve the time-coordinated strategies effectively.

This paper presents a blockchain enabled secure data acquisition scheme utilizing an unmanned aerial vehicle (UAV) swarm where data are collected from the Internet of Things (IoT) devices and subsequently, forwarded to the nearest server through the UAV swarm. Before initiating data acquisition, the UAV swarm shares a shared key with the IoT devices in order to maintain communications. However, prior to transmitting data, the IoT devices encrypt the data and forward it to the UAV swarm. Upon receiving the data, the UAV swarm implements a two-phase validation utilizing the π-hash bloom filter and the digital signature algorithm to validate the sender; in addition, prior to forwarding data to the nearest server, it performs encryption. However, before adding data in blockchain, consent from all validators is required. Finally, the data are stored in blockchain with the approval of validators. A security analysis is performed to demonstrate the feasibility of the proposed scheme. Finally, the effectiveness of the proposed scheme is manifested through the implementation and simulation. The security analysis and the performance results show that UAV assist the IoT devices both in terms of connectivity and energy consumption, and provides security against the threats mentioned in the paper.

Most of the recent research on distributed formation control of unmanned aerial vehicle (UAV) swarms is founded on position, distance, and displacement-based approaches; however, a very promising approach, i.e., bearing-based formation control, is still in its infancy and needs extensive research effort. In formation control problems of UAVs, Euler angles are mostly used for orientation calculation, but Euler angles are susceptible to singularities, limiting their use in practical applications. This paper proposed an effective method for time-varying velocity and orientation leader agents for distributed bearing-based formation control of quadcopter UAVs in three-dimensional space. It combines bearing-based formation control and quaternion-based attitude control using undirected graph topology between agents without the knowledge of global position and orientation. The performance validation of the control scheme was done with numerical simulations, which depicted that UAV formation achieved the desired geometric pattern, translation, scaling, and rotation in 3D space dynamically.

Missing link prediction technology (MLP) is always a hot research area in the field of complex networks, and it has been extensively utilized in UAV swarm network reconstruction recently. UAV swarm is an artificial network with strong randomness, in the face of which prediction methods based on network similarity often perform poorly. To solve those problems, this paper proposes a Multi Kernel Learning algorithm with a multi-strategy grey wolf optimizer based on time series (MSGWO-MKL-SVM). The Multiple Kernel Learning (MKL) method is adopted in this algorithm to extract the advanced features of time series, and the Support Vector Machine (SVM) algorithm is used to determine the hyperplane of threshold value in nonlinear high dimensional space. Besides that, we propose a new measurable indicator of Multiple Kernel Learning based on cluster, transforming a Multiple Kernel Learning problem into a multi-objective optimization problem. Some adaptive neighborhood strategies are used to enhance the global searching ability of grey wolf optimizer algorithm (GWO). Comparison experiments were conducted on the standard UCI datasets and the professional UAV swarm datasets. The classification accuracy of MSGWO-MKL-SVM on UCI datasets is improved by 6.2% on average, and the link prediction accuracy of MSGWO-MKL-SVM on professional UAV swarm datasets is improved by 25.9% on average.

It is time-consuming to simulate the unmanned aerial vehicle (UAV) swarm by the Multi-Level Fast Multipole Algorithm (MLFMA). This paper proposes a method to accelerate the simulation by neglecting the coupling among UAVs and computing the scattered field by Spherical Harmonic Transform (SHT). The key point is evaluating the scattered electric field of a UAV on an eccentric sphere by changing the order of summation of the spherical harmonics and then accelerating the summation by Fast Fourier Transform (FFT). The example of a swarm of 6 UAVs shows that the bistatic Radar Cross Section (RCS) obtained by the proposed method is in good agreement with the RCS obtained by MLFMA, and the proposed method requires 4.76 times less memory and is 10.93 times faster than MLFMA. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) swarm consists of a number of autonomous and collaborative UAV nodes. The purpose of the research is to address the hypotheses: an efficient centralized resources management scheme should be adopted to meet the mission‐oriented feature of UAV swarms, while a distributed fault‐tolerant mechanism should be adopted to meet the characteristics of complex environment and address the issue of high failure rate of UAV nodes. By using an experimental modeling method, this paper proposes a management model and related mechanisms fitting for UAV swarms with the feature of "centralized decision making and distributed task execution." The model allows UAV nodes to manage their own resources autonomously and execute centralized decisions accordingly. The advantage of the model is that the master node of a UAV swarm is able to perceive all the resources of the system and the status of all (or some) of the UAVs efficiently; therefore, it provides a supportive environment for the UAV swarm application to make centralized decisions and optimize those decisions. Additionally, this paper proposes a distributed fault‐tolerant model and related mechanisms to address the issue of high failure rate of UAV nodes, as well as the system vulnerability caused by harsh environment. Finally, this study developed a prototype system designed and implemented by OMNeT++ to confirm the truth of the hypotheses. The analysis of the experimental data and simulation results of the prototype validated the feasibility and usability of the distributed fault‐tolerant mechanism and demonstrated its value for the mission‐oriented UAV swarms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively. [ABSTRACT FROM AUTHOR]

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms.

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms.

Agent-based combat modelling and simulation is an important method for UAV swarm surveillance effectiveness evaluation. The agent behaviour mechanisms and model structures in previous agent-based combat modelling and simulation studies are difficult to describe self-organized surveillance behaviours in complex mission scenarios. Taking a counter-piracy surveillance mission as an example, a combat modelling and simulation method oriented for swarm self-organized surveillance is proposed. By the hierarchical composite behaviour mechanism and the composable modular model structure, complex behaviours are described by the composition of simple behaviours, and the self-organized path planning and flight control are realized by the interaction of the pheromone map module and the path management module. Simulation experiments are conducted to demonstrate the feasibility of the proposed method, and compare the surveillance effectiveness with different path planning methods. The results show that, self-organized path planning is more adaptive to dynamic events interrupting surveillance. In large-area, long-duration and low-target-density area coverage surveillance missions, merely decreasing revisit intervals by improving path planning methods cannot significantly increase the probability of finding targets.

Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of 94% in the case of 1000 nodes compared with existing methods with the same level of global energy consumption.

Recently, unmanned aerial vehicle (UAV) remote sensing has been widely used in emergency scenarios; the operating mode has transitioned from one UAV to multiple UAVs. However, the current multiple-UAV remote sensing mode is characterized by high labor costs and limited operational capabilities; meanwhile, there is no suitable UAV swarm scheduling method that can be applied to remote sensing in emergency scenarios. To solve these problems, this study proposes a UAV swarm scheduling method. Firstly, the tasks were formulated and decomposed according to the data requirements and the maximum flight range of a UAV; then, the task sets were decomposed according to the maximum flight range of the UAV swarm to form task subsets; finally, aiming at the shortest total flight range of the task subsets and to balance the flight ranges of each UAV, taking the complete execution of the tasks as the constraint, the task allocation model was constructed, and the model was solved via a particle swarm optimization algorithm to obtain the UAV swarm scheduling scheme. Compared with the direct allocation method and the manual scheduling methods, the results show that the proposed method has high usability and efficiency.

According to different mission scenarios, the UAV swarm needs to form specific topology shapes to achieve more robust system capability. The topology shaping, which will guide the UAVs autonomously to form the desired topology shape, is considered one of the most basic procedures in the UAV swarm field operations. The traditional optimization model of UAV swarm topology shaping proposed in most studies roughly represents the energy consumption by the squared Euclidean distances from initial positions to target positions of nodes. However, in practice, UAVs flying in different directions (vertical or horizontal) usually exhibits different energy consumption even though under the same moving distance. This paper proposes a more precise energy consumption model for UAV swarm topology shaping while taking the energy consumption for a UAV flying vertically upward, vertically downward, and horizontally into account. Simulation results show that the global energy consumption of the topology shaping modeled by the proposed energy consumption model is reduced by more than 38% on average compared with that using the traditional energy consumption model. Furthermore, to further reduce the global energy consumption, a translation vector is introduced in the optimization model to obtain the optimal topology shaping position of the UAV swarm system. Newton’s method is employed to derive the translation vector which exhibits good convergence. Simulation results show that the global energy consumption of optimal topology shaping position is reduced by 9.8% on average compared with that without translation.

With the continuous development of artificial intelligence, swarm control and other technologies, the application of Unmanned Aerial Vehicles (UAVs) in the battlefield is more and more extensive, and the UAV swarm is increasingly playing a prominent role in the future of warfare. How tasks are assigned in the dynamic and complex battlefield environment is very important. This paper proposes a task assignment model and its objective function based on dynamic information convergence. In order to resolve this multidimensional function, the Wolf Pack Algorithm (WPA) is selected as the alternative optimization algorithm. This is because its functional optimization of high-dimensional complex problems is better than other intelligent algorithms, and the fact that it is more suitable for UAV swarm task allocation scenarios. Based on the traditional WPA algorithm, this paper proposes a Multi-discrete Wolf Pack Algorithm (MDWPA) to solve the UAV task assignment problem in a complex environment through the discretization of wandering, calling, sieging behavior, and new individual supplement. Through Orthogonal Experiment Design (OED) and analysis of variance, the results show that MDWPA performs with better accuracy, robustness, and convergence rate and can effectively solve the task assignment problem of UAVs in a complex dynamic environment.

UAV-swarm cooperative situation perception consensus (SPC), a core part of swarm cooperative situation awareness (SA) consensus, directly influences whether a swarm could gain information superiority under complex mission environments. The related studies of evaluation indices and methods for UAV swarm cooperative SPC are not sufficient and are not very suitable for complex mission environments, e.g., battlefield or combat simulation environment, this paper systematically analyzes the connotation of swarm cooperative SPC, establishes the evaluation indices via information quality evaluation theory and proposes evaluation method of swarm cooperative SPC based on three-parameter interval number and Heronian mean (HM) operator. The proposed method includes developing a new method to represent multi-time evaluation indices by three-parameter interval number, proposing a variable weight strategy to obtain index weight and aggregating index information by three-parameter interval number weighted HM operator. The simulation results show that the established evaluation attributes can reasonably analyze the swarm cooperative SPC and the proposed approach can effectively deal with the uncertainty of situation information, the HM operator is superior to multiplicative synthesis in representing the correlations among attributes, compared to the evaluation method based on combined weights, the proposed approach has a better performance in the discrimination, which is more beneficial to the comparison of swarm cooperative SPC in different conditions.

Affected by the rescue environment and the unmanned aerial vehicle(UAV) communication technology, communication among UAVs is greatly restricted. How to do rescue mission planning for UAV swarms underlimited communication has become a difficult problem. This paper proposes a motif-based mission planning model which generates amiss ion planning strategy as a task priority execution order is inputted. The choice of mission planning strategy turns to be the choice of a task priority execution order which is a many-objective optimization problem. Permutation and combination of task priority execution order contribute to the generation of mass feasible task planning strategies. The increase in the number of tasks leads to the explosion of the amount of calculation. We enhance preference-inspired co-evolutionary algorithm with goal vectors (PICEA-g) by using the K-means clustering method in the step of offspring selection to select out next-generation goal vectors. The proposed algorithm, calledk-PICEA-g is applied in the optimization processminimizing, simultaneously, the mission completion time, the total number of changed connections, and the averagenumber of used UAVs through changing the priority execution order oftasks. As an example of application, we apply the improvedPICEA-g to optimize the execution order of a set of tasks,achieving a set of non-dominated solutions from which the decisionmaker can select the most adequate one. By experiments, thefeasibility and effectiveness of this algorithm are validated.

To perform air missions with an unmanned aerial vehicle (UAV) swarm is a significant trend in warfare. The task assignment among the UAV swarm is one of the key issues in such missions. This paper proposes PSO-GA-DWPA (discrete wolf pack algorithm with the principles of particle swarm optimization and genetic algorithm) to solve the task assignment of a UAV swarm with fast convergence speed. The PSO-GA-DWPA is confirmed with three different ground-attack scenarios by experiments. The comparative results show that the improved algorithm not only converges faster than the original WPA and PSO, but it also exhibits excellent search quality in high-dimensional space. [ABSTRACT FROM AUTHOR]

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Swarm unmanned aerial vehicles (UAVs) is an approach to the coordination of multiple UAVs as a system, which has huge advantages on mission capabilities, such as cooperative search, border surveillance, and situation awareness. For better swarm coordination, a robust inter-UAV network, especially in ad hoc mode, is needed. Due to the dynamic nature of UAV networks owing to mobility and topology change, adapting routing quickly to topology changes is one of the key components of UAV networks. Based on investigating the relationship between the swarm formation control and the network topology, we propose a proactive topology-aware scheme to track the network topology change. Through simulations on Qualnet and real-world experiments with five quadrotors, the results confirmed that the proposed scheme reduces the average delay and improves routing performance significantly. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) swarms are widely applied for service provisioning in many domains such as topographic mapping and traffic monitoring. These UAV applications are complicated as they demand not only huge computational resources but also strict latency requirements. However, due to the limited onboard resources of UAVs as well as various dynamics of applications and environments, it's challenging to obtain a feasible computation offloading policy in online environments. To cope with these problems, we propose an online algorithm for UAV swarms to jointly optimize the computation offloading and multi-hop routing scheduling in the Edge-Cloud environment. First, we propose a UAV-Edge-Cloud computing model to meet the requirements of latency and computing capability. We then formulate a joint optimization of workflow assignment and multi-hop routing scheduling in order to minimize the computation cost and latency. To tackle this NP-hard problem, an algorithm based on the Markov approximation technique is designed to obtain near-optimal solutions. Furthermore, to achieve high long-term performance in online environments, we exploit Lyapunov optimization to control the migration cost caused by the dynamics of application and environment. Finally, extensive simulation results demonstrate that our approach achieves a long-term high-efficiency and stable performance in an online environment for UAV swarms with respect to computation offloading and traffic routing. [ABSTRACT FROM AUTHOR]

This paper analyzes the wireless connectivity of an Unmanned Aerial Vehicle (UAV) swarm in the presence of (un)intentional external interference from the ground. Different from existing studies, the swarm UAVs fly independently around a given three-dimensional (3D) location and are all within a 3D spherical space. Closed-form bounds are delivered for the average outage probability of a UAV from its nearest neighbor in the swarm, and the density of the swarm, which allows the swarm to operate uninterruptedly in the presence of the interference. Our analysis involves closed-form approximations of the instantaneous outage probability of a UAV from its nearest neighbor by using the first-order Marcum $Q$ -function and the zero-th order modified Bessel function of the first kind. The analysis also involves applying Jensen's inequality to the instantaneous outage probability to bound the average outage probability and the density of the swarm. Corroborated by simulations, our analysis is accurate, and useful to evaluate the impact of external interference on the connectivity of UAV swarms. Interesting insights are shed on the connectivity and coverage of the UAV swarm. [ABSTRACT FROM AUTHOR]

For the rising hazard of pirate attacks, unmanned aerial vehicle (UAV) swarm monitoring is a promising countermeasure. Previous monitoring methods have deficiencies in either adaptivity to dynamic events or simple but effective path coordination mechanisms, and they are inapplicable to the large-area, low-targetdensity, and long-duration persistent counter-piracy monitoring. This article proposes a self-organized UAV swarm counter-piracy monitoring method. Based on the pheromonemap, this method is characterized by (1) a reservation mechanism for anticipatory path coordination and (2) a ship-adaptive mechanism for adapting to merchant ship distributions. A heuristic depth-first branch and bound search algorithm is designed for solving individual path planning. Simulation experiments are conducted to study the optimal number of plan steps and adaptivity scaling factor for different numbers of UAVs. Results show that merely decreasing revisit intervals cannot effectively reduce pirate attacks. Without the ship-adaptive mechanism, the proposed method reduces up to 87.2%, 43.2%, and 5.5% of revisit intervals compared to the LèvyWalk method, the sweep method, and the baseline self-organized method, respectively, but cannot reduce pirate attacks; while with the ship-adaptive mechanism, the proposed method can reduce pirate attacks by up to 6.7% compared to the best of the baseline methods. [ABSTRACT FROM AUTHOR]

To maximize the advantages of being low-cost, highly mobile, and having a high flexibility, aerial recovery technology is important for unmanned aerial vehicle (UAV) swarms. In particular, the operation mode of "launch-recovery-relaunch" will greatly improve the efficiency of a UAV swarm. However, it is difficult to realize large-scale aerial recovery of UAV swarms because this process involves complex multi-UAV recovery scheduling, path planning, rendezvous, and acquisition problems. In this study, the recovery problem of a UAV swarm by a mother aircraft has been investigated. To solve the problem, a recovery planning framework is proposed to establish the coupling mechanism between the scheduling and path planning of a multi-UAV aerial recovery. A genetic algorithm is employed to realize efficient and precise scheduling. A homotopic path planning approach is proposed to cover the paths with an expected length for long-range aerial recovery missions. Simulations in representative scenarios validate the effectiveness of the recovery planning framework and the proposed methods. It can be concluded that the recovery planning framework can achieve a high performance in dealing with the aerial recovery problem. [ABSTRACT FROM AUTHOR]

Over the past decades, Unmanned Air Vehicles (UAVs) have achieved outstanding performance in military, commercial and civilian applications. UAVs are increasingly appearing in the form of swarms or formations to meet higher mission requirements. Communication plays an important role in UAV swarm control and coordination. The communication architecture defines how information is exchanged between UAVs or between UAVs and the central control center. Routing protocols help provide reliable end-to-end data transmission. Therefore, it is particularly important to design UAV swarm communication architectures and routing protocols with high performance and stability. This review article details four communication architectures including the advantages and disadvantages. Applicable scenarios are also discussed. In addition, a systematic overview and feasibility research of routing protocols are presented in this paper. To spur further research, the open research issues of UAV swarm communication architectures and routing protocols are also investigated. [ABSTRACT FROM AUTHOR]