A study of mobility models for UAV Communication Networks

With the rapid development of information technology and the increasing application of UAV in various fields, the security problems of unmanned aerial vehicle (UAV) communication network have become increasingly prominent. It has become an important scientific challenge to design a routing protocol that can provide efficient and reliable node to node packet transmission. In this paper, an efficient Digital Signature algorithm based on the elliptic curve cryptosystem is applied to routing protocol, and an improved security method suitable for on-demand routing protocol is proposed. The UAV communication network was simulated through the NS2 simulation platform, and the execution efficiency and safety of the improved routing protocol were analyzed. In the simulation experiment, the routing protocols of ad-hoc on demand distance vector (AODV), security ad-hoc on demand distance vector (SAODV), and improved security ad-hoc on demand distance vector (ISAODV) are compared in terms of the performance indicators of packet delivery rate, throughput, and end-to-end delay under normal conditions and when attacked by malicious nodes. The simulation results show that the improved routing protocol can effectively improve the security of the UAV communication network.

Topology tracking of dynamic UAV wireless networks

The Internet of Things (IoT) can be supported by multiple isolated sensor networks (MISN) when the connectivity of IoT is constrained by obstacles. Mobile nodes have been used as relays to connect partitioned networks. However, mobile nodes have the problem of not being able to move or moving slowly when encountering obstacles. In order to tackle this issue, three types of Unmanned Aerial Vehicle (UAV) swarm modes are proposed, and UAV swarm assisted connectivity enhancement algorithms (UsCE) are designed. A UAV swarm with high degree of freedom and flexibility provides a new way in IoT for solving the above connectivity problem. Our target is to find an optimal solution that minimizes the number of UAVs in the swarm and maximizes the connection time of MISN. We divide the working modes of a UAV swarm into hovering and flying. Firstly, the ground sink nodes are classified by a MISN's sink node classification algorithm to generate hovering points for the UAV swarm. Secondly, the results are optimized and adjusted by a minimum UAV swarm hovering connection algorithm to obtain an optimal solution under the hovering mode. Finally, we achieve an optimal connectivity when the UAV swarm works in flying mode through the UAV swarm flight connectivity algorithm and compare it with two previously proposed algorithms. Simulation results show that the complexity of the algorithms is low, the connection time of MISN increases significantly, and the number of UAVs is small. An optimal UAV swarm assisted connectivity enhancement scheme for MISN of different scales is derived.

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

Unmanned aerial vehicle (UAV) swarms offer a resource-effective and time-efficient method of collecting and analyzing data for a range of applications. The paper presents a cutting-edge collective artificial intelligence-powered self-organizing UAV swarm mission planning and preparing platform designed to enhance task performance with a fleet of UAVs and facilitate terrain monitoring. Users can create requests easily from the QT interface due to the cloud-based multi-user platform's interactive capabilities, which enable smooth user collaboration and real-time video viewing for group study of dynamic landscape imagery. Optimizing the behavior and performance of UAV swarm navigation maps, the UAV map configurator makes it easier to create and modify them. Additionally, the QT service layer guarantees safe data transfer to cloud servers, and the parameter gossip system encourages coordination and communication among swarm members. This integrated data sets significant parameters such as the number of swarm participants, starting relative coordinates, and statuses (imager and/or strike), and drives the establishment of critical swarm and target jobs. The server uses complex algorithms, such as the research road graph, which is based on the rotor-router model, and the complete information exchange graph, which uses the gossip/broadcast model, to carry out these tasks. These algorithms complement each other in the server environment, allowing the UAV swarm to plan and coordinate tasks more effectively. In addition, the platform provides the smooth transfer of the created goal tasks to each member of the swarms’ memory, benefiting decision-making skills and swarm performance as an entire. The QT-based user interface also provides the capability to modify the specific member task type of UAVs. This implies that UAVs can be used for specific area consideration, information gathering, transport, or, in the case of a military use, as an enemy position attacker. A server environment and QT-based interface can be used as a data sharing transponder to accomplish all of these functional changes. The task categories within the mentioned swarm shift when alterations occur in the initial transfer graph of UAVs, necessitating the inclusion of new locations for completing the modified tasks. Additionally, ensuring a unified primary administrative domain and coordinated data transmission among users across multiple platforms are crucial aspects of the suggested method. This domain ensures secure connections and offers additional security features.

In recent years, the concept of UAV (Unmanned Aerial Vehicle) swarm has been proposed and developed, which effectively solves the shortcomings of relatively small payload and relatively weak information perception processing capability in a single UAV operation. The research and application of UAV swarm technology has become an important direction of the development of UAV technology. UAV swarm not only can effectively improve the load capacity and information processing capacity through close cooperation between single UAV, but also has a high "self-healing" ability and strong robustness. However, due to the limited computing resources and complex external environment of UAVs, UAV swarm are vulnerable to forgery attacks, man-in-the-middle attacks and reply attacks. It is necessary to establish an authentication scheme to ensure the legitimate and reliable identity of UAVs for data exchange and sharing. However, traditional authentication schemes based on username, password or dynamic key have lower security levels, while RSA authentication requires a long session key and cannot meet the lightweight requirements in an UAV swarm. Based on the analysis and summary of existing authentication technologies and the special requirements of UAV swarm for cyber security, this paper puts forward an authentication scheme based on Zero Trust, achieves rapid authentication of UAV swarm in data exchange and sharing, and strengthens its ability to respond to cyber attacks.

Introduction: Unmanned aerial vehicle (UAV) swarms are increasingly employed in civilian and military applications. Efficient path planning is critical for navigating dynamic environments, yet challenges such as collision avoidance, energy optimization, and multiagent coordination hinder deployment. Methods: A systematic literature review (SLR) was conducted to classify UAV swarm pathplanning algorithms into four categories: sampling-based, graph-based, biologically inspired, and mathematical model-based approaches. The review adhered to established SLR guidelines, employing systematic database searches and predefined inclusion and exclusion criteria. Each algorithm was evaluated in terms of computational efficiency, adaptability, and application suitability. Results: Biologically inspired algorithms demonstrated high adaptability and flexibility, whereas graph-based approaches were effective in structured environments. Sampling-based methods proved scalable in high-dimensional spaces, and mathematical models emphasized optimization. Persistent challenges include real-time adaptability, energy efficiency, and operations in interactive environments. Discussion: The comparative analysis highlights the trade-offs inherent in each approach and underscores the unresolved challenges that limit practical implementation. Addressing these gaps is essential to advance the reliability and applicability of UAV swarms in real-world scenarios. Conclusion: This review systematically evaluates UAV swarm path-planning algorithms, identifying their respective advantages, limitations, and application contexts. The findings provide a reference framework to inform future research directions aimed at enhancing UAV swarm autonomy and operational efficiency.

An unmanned aerial vehicle communication Networks (UAVCN) is a group of UAVs (unmanned aerial vehicles) that is able to communicate and trigger information to each other for a particular mission. This network node is power limited. However, the routing mechanism should be improved which extend the lifetime and performance of UAV nodes. This manuscript intends to introduce a concept of a cross-layer method for efficient power and routing for UAVCN to increase communication performance. The cross-layer methodology integrates the physical, link and network layer. An efficient power optimized link state routing (EP-OLSR) protocol is proposed which is an extension to optimized link state routing (OLSR) protocol. The proposed routing protocol evaluated by developing scenarios of UAVCN in OPNET modeler 14.5. The simulation is carried out by implementing OLSR and EP-OLSR using different network parameters and compared the routing protocols performance. The results showed that the EP-OLSR maximizes network performance efficiency as compared to OLSR. It minimizes the delay and maximizes the throughput and increases the network performance.

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

The resilience of unmanned aerial vehicle (UAV) swarm systems describes the capability to adjust its behavior to maintain its essential functions when malfunction or attack occurs. Recent studies have modeled the UAV swarm as a network to analyze its resilience, where each UAV is represented as a node, and each link denotes information exchange between UAVs. However, the current networking method is vulnerable to malicious attacks and does not consider the individual load of one UAV is limited. In this paper, we proposed a resilient UAV swarm networking model by incorporating the effect of the limitation of the bandwidth. Under continuous malicious attacks, this proposed model could enhance the resilience of the swarm systems by rewiring the nodes. Moreover, a case study is conducted in which a UAV swarm is to perform a search and rescue task. Experimental results show that the proposed model is more resilient to malicious attacks than some existing models, e.g., the BA model, ER model, and the balanced individual communication load.

In this paper, we present a novel design for unmanned aerial vehicle (UAV) communication networks with wireless backhaul, where an active reconfigurable intelligent surface (ARIS) is deployed to improve connections between a UAV and multiple users, while mitigating channel impairments in complex environments. The proposed design aims to maximize the achievable sum rate of all networks by jointly optimizing UAV placement; resource management strategies; transmit power allocation; and ARIS reflection coefficients, subject to backhaul constraints and power budget limitations in the ARIS system. The resulting optimization problem is highly non-convex, posing significant challenges. To tackle this, we decompose the problem into three interrelated sub-problems and apply inner approximation (IA) techniques to handle the non-convexities within each sub-problem. Moreover, a comprehensive alternating optimization framework is proposed to implement an iterative solution for the sub-problems. Simulation results demonstrate that the proposed algorithm achieves approximately 59% improvement in the average sum rate, substantially enhancing overall network reliability compared to existing benchmark schemes.

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars’ precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars’ positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results.