Unmanned Aerial Vehicles Networks Research Articles

Multi-Objective Optimization Approaches for Physical Layer Secure Communications Based on Collaborative Beamforming in UAV Networks

Unmanned aerial vehicle (UAV) communications and networks are promising technologies in the forthcoming 5G/6G wireless communications. However, they have challenges for realizing secure communications. In this paper, we consider to construct a virtual antenna array consists UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground. To achieve a better secure performance, the UAV elements can fly to optimal positions with optimal excitation current weights for performing CB transmissions. However, this leads to extra motion energy consumption. We formulate a physical layer secure communication multi-objective optimization problem (MOP) of UAV networks to simultaneously improve the total secrecy rates, total maximum sidelobe level (SLL) and total motion energy consumption of UAVs by jointly optimizing the positions and excitation current weights of UAVs, and the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated MOP, we propose an improved multi-objective dragonfly algorithm with chaotic solution initialization and hybrid solution update operators (IMODACH) and a parallel-IMODACH (P-IMODACH) to solve the problem. Simulation results verify that the proposed approaches can effectively solve the formulated MOP and it has better performance than some other benchmark algorithms and approaches. Moreover, some unexpected circumstances are considered and discussed.

A Task-Driven Sequential Overlapping Coalition Formation Game for Resource Allocation in Heterogeneous UAV Networks

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

Energy Efficiency Optimization of IRS-Assisted UAV Networks Based on Statistical Channels

In this letter, intelligent reflecting surface (IRS) is adopted to assist unmanned aerial vehicle (UAV) to achieve energy-efficient transmission. Specifically, the energy efficiency is maximized by jointly optimizing the UAV trajectory, transmit power and IRS phase shifts based on the statistical channel state information (CSI), since the instantaneous CSI is difficult to obtain due to the mobility of UAV and the passive structure of IRS. The non-convex problem is decomposed into two sub-problems of the trajectory and transmit power optimization, and the phase-shift optimization. To deal with the non-convexity of the first one, the successive convex approximation and Dinkelbach’s algorithm are utilized. For the second one, a closed-form solution is derived directly through equivalent transformations. Then, the two sub-problems are solved iteratively to obtain an effective solution to the original problem. Simulation results are presented to validate the effectiveness of the proposed scheme.

Joint Location and Beamforming Optimization for STAR-RIS Aided NOMA-UAV Networks

This work investigates the potential of combining unmanned aerial vehicle (UAV) and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in wireless networks. In particular, the signals from UAV can be reflected and transmitted through STAR-RIS to the users on both sides of the surface to provide full-space coverage. We formulate a sum-rate maximization problem in STAR-RIS aided non-orthogonal multiple access UAV networks, where the location and power allocation of UAV, and the passive reflection/transmission beamforming of STAR-RIS are jointly optimized, subject to the quality-of-service requirement. Furthermore, we transform the non-convex problem into a convex one and propose an efficient algorithm to obtain the suboptimal solution to the original problem. Simulation results verify the superiority of the proposed scheme compared to other benchmarks.

Characterization of the Global Bias Problem in Aerial Federated Learning

Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of aerial FL in large-scale UAV networks. To this end, the paper proposes a channel-aware distribution and aggregation scheme to enforce equal contribution from all devices in the FL training as a means to resolve the global bias problem. We demonstrate the convergence of the proposed method by experimenting with the MNIST dataset and show its superiority compared to existing methods. The obtained results enable system parameter tuning to relieve the impact of the aerial channel deficiency on the FL convergence rate.

A Cross-Layer, Mobility, and Congestion-Aware Routing Protocol for UAV Networks

A Cross-Layer, Mobility, and Congestion-Aware Routing Protocol for UAV Networks

ULS-PBFT: An ultra-low storage overhead PBFT consensus for blockchain

Since the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism can avoid the performance bottleneck of blockchain systems caused by Proof of Work (PoW), it has been widely used in many scenarios. However, in the blockchain system, each node is required to back up all transactions and block data of the system, which will waste a lot of storage resources. It is difficult to apply to scenarios with limited storage resources such as unmanned aerial vehicle networks and smart security protection; thus, it is often used in small-scale networks. In order to deploy PBFT-based blockchain systems in large-scale network scenarios, we propose an ultra-low storage overhead PBFT consensus (ULS-PBFT), which groups nodes hierarchically to limit the storage overhead within the group. In this paper, we first propose an optimal double-layer PBFT consensus from the perspective of minimizing the storage overhead, and prove that this consensus can significantly reduce the storage overhead. In addition, we also investigate the superiority of ULS-PBFT in terms of communication overhead while setting the security threshold in the presence of the possibility of Byzantine nodes. The simulation results demonstrate the advantages of ULS-PBFT. Then, we extend such grouping idea to the blockchain system with X-layer PBFT and analyze its storage and communication overhead. Finally, the node grouping strategy of double-layer PBFT is studied for four application scenarios when the performance of storage overhead, communication overhead, and security are considered comprehensively.

Improved YOLOV5-Based UAV Pavement Crack Detection

In terms of highway crack detection, the combination of UAV and deep learning network has become a powerful detection means. However, in the actual detection, in order to take into account the detection efficiency, it is necessary to ensure that the detection area is large enough, which makes the crack occupy few pixels in the image, and the image background is complex. Therefore, in this paper, DJI Mavic3 is used to establish the image data set of highway pavement cracks under complex background. And, the YOLOV5 deep learning model is improved by adding swin transformer structure and BIFPN feature pyramid. The improved YOLOV5 model achieved real-time pixel-level detection with detection accuracy of 90% and detection speed of 43.5 FPS. In terms of crack detection ability, the accuracy of the improved YOLOV5 reaches 4 pixels, and cracks of 1.2 mm can be detected in the experiment. Compared with the YOLOV7 model, the detection accuracy of the improved YOLOV5 model is increased by 15.4%. Compared with the YOLOV6 model, the calculated parameters of the improved YOLOV5 model are reduced by 59.25%. The proposed model is superior to other advanced models in crack detection.

Research on Identity Authentication Scheme for UAV Communication Network

Unmanned aerial vehicles (UAV) play a vital role in many fields, such as agricultural planting, security patrol, emergency rescue, and so on. The development and implementation of these devices have become vital in terms of reachability and usability. Unfortunately, as drones become more widely used in various fields, they become more and more vulnerable to attacks and security threats, including, but not limited to, eavesdropping, man-in-the-middle attacks, and known session key attacks. In order to deal with these attacks and security threats and meet the needs of lightweight UAV communication, a secure and efficient authentication scheme is essential. To meet the security and lightweight requirements of an identity authentication scheme in a UAV communication network, this paper proposes an identity authentication scheme sdronelig based on an elliptic curve cryptosystem. The scheme realizes the mutual authentication and session key agreement configuration between the UAV and the ground station, and the authentication and key agreement between the UAVs can be realized with the help of the control station. The sdronelig authentication scheme is based on the ECDH key exchange protocol in the elliptic curve cryptography algorithm and adopts the MAC message authentication code technology and the method of pre-calculating part of the process. Under the premise of ensuring the security of the UAV communication network, the authentication efficiency is improved, the communication overhead and communication times are reduced, and the lightweight requirement of the UAV authentication scheme is met. Additionally, a formal verification tool is used to verify the security of the sdronelig scheme under the Dolev-Yao threat model, which is suitable for UAV networks. Finally, a detailed comparative study was conducted on security features, communication overhead, the number of communications, and computational overhead. The results show that the proposed sdronelig authentication scheme not only provides superior security features but also has better or comparable overhead compared to other existing authentication schemes.

A Study of Coverage Probability-Based Energy-Efficiency Analysis for UAV-Aided THz-Enabled 6G Networks

Reliable and flexible emergency communication is a crucial challenge for search and rescue in the circumstance of disasters, specifically for the situation when base stations (BS) are no longer functioning. Unmanned aerial vehicle (UAV)-aided networking is becoming a prominent solution to establish emergency networks, which also should be energy-efficient. The Terahertz (THz) frequency-enabled communication system is also envisioned as enabling technology for Sixth-Generation (6G) communication systems. To this end, we consider a THz UAV network for our investigation. In this article, we study energy-efficiency (EE) analysis for UAV-Aided Terahertz-Enabled 6G Networks. The metric coverage probability (CP) is adopted as EE analysis given the QoS requirement. Using stochastic geometry tools, we derive a CP. Afterward, considering that CP, we study a network EE optimization method. Borrowing the idea from the existing literature, we reformulate the actual EE optimization problem into a solvable Dinkelbach nonlinear fractional programming. However, the optimization problem turns into a nondeterministic polynomial hard (NP-hard) problem. Eventually, we transform that NP-hard problem into a convex optimization problem using the ConCave-Convex procedure (CCCP), which is more tractable and feasible to find a solution for the optimization.

Large-Scale Rate-Splitting Multiple Access in Uplink UAV Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel

Unmanned aerial vehicles (UAVs) are capable of improving the performance of next generation wireless systems. However, their communication performance is prone to both channel estimation errors and potential eavesdropping. Hence, we investigate the effective network secrecy throughput (ENST) of the uplink UAV network, in which rate-splitting multiple access (RSMA) is employed by each legitimate user for secure transmission under the scenario of massive access. To maximize the ENST, the transmission rate versus power allocation relationship is formulated as a max-min optimization problem, relying on realistic imperfect channel state information (CSI) of both the legitimate users and passive eavesdroppers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eves$</tex-math></inline-formula> ). In the model considered, each user transmits a superposition of two messages to a UAV base-stations (UAV-BS), each having different transmit power and the UAV-BS adopts a successive interference cancellation (SIC) technique to decode the received messages. Given the non-convexity of the problem, it is decoupled into a pair of sub-problems. In particular, we derive a closed form expression for the optimal rate-splitting fraction of each user. Then, given the optimal rate-splitting fraction of each user, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constrainted transmit power of each user is calculated by harnessing sequential parametric convex approximation (SPCA) programming. Our simulation results confirm that the scheme conceived significantly improves the ENST compared to both the existing orthogonal and non-orthogonal benchmarks.

Dual-Band Aerial Networks for Priority-Based Traffic

Unmanned aerial vehicle networks suffer from lackluster performance due to line-of-sight issues as well as resource scarcity. Network slicing, multi-band transmission, and numerology show great potential in mitigating such limitations. In this work, we propose the use of the sub-6 GHz and mmWave bands, the latter of which requires careful consideration of line-of-sight, with numerology for aerial network slicing to provision time-critical services and broadband access. Accordingly, we formulate a user admission control policy to regulate band access after which we formulate a joint resource block and power allocation problem, a mixed-integer non-linear programming problem, to minimize the quality-of-service gap of the throughput-dependent broadband users as a best-effort service and meet the time-critical service requirements. We propose a low-complexity algorithm, PREDICT, to tackle the formulated problem and present extensive simulation results to validate the advantages of our scheme.

Phase and 3-D Placement Optimization for Rate Enhancement in RIS-Assisted UAV Networks

In this letter, we aim at improving the system sum-rate performance of an assisted aerial base station (ABS) network by considering use of reconfigurable intelligent surface (RIS). We jointly optimize the RIS phase shifts and ABS altitudes using the proposed conjugate gradient and particle swarm optimization (CG-PSO) scheme. Moreover, we divide the problem into sub-problems and solve them using an alternative optimization method. First, an optimal RIS phase shift is computed using the conjugate gradient (CG) under the fixed ABS altitude and varying transmit power. Finally, we find the optimal ABS altitude using particle swarm optimization (PSO), which results in improved sum rate under the optimized RIS phase shift. The results show that the CG-PSO scheme significantly improves the system sum rate compared to the baseline schemes.

DATA-FedAVG: Delay-Aware Truncated Accuracy-Based Federated Averaging for Intrusion Detection in UAV Network

DATA-FedAVG: Delay-Aware Truncated Accuracy-Based Federated Averaging for Intrusion Detection in UAV Network

THE PROGRAM FOR ASSESSING THE CONNECTIVITY OF NODES OF WIRELESS EPISODIC NETWORKS UNDER THE CONDITION OF USING UAVS

Background. Based on analytical mathematical models, the duration of connectivity of mobile subscribers (nodes) (MS) of a wireless episodic network (WEN, consisting of MS and UAV) was investigated in conditions of direct radio visibility and considering the relaying.&#x0D; Objective. The purpose of the work is to find methodological approaches to ensure the connectivity of WSN nodes, which is a necessary condition for obtaining information from WSN in the absence of communication infrastructure.Methods. Simulation modelling based on MAPLE 14 software package and analytical calculation methods are used.&#x0D; Results. It is shown that the duration of connectivity is directly proportional to the size of the coverage area and inversely proportional to the movement speed of nodes. The mobility nature (scenario) of nodes also affects the duration of connectivity. The simulation of the nodes' movement was carried out under 4 scenarios: "march", "incoherent", "random wandering in the field" and "random wandering in the city". The largest values of the connectivity duration correspond to the third scenario, and the smallest - to the second (with a fixed radius of the coverage area and the movement speed of nodes). Thus, the average connectivity duration of the UAV-pedestrian connection in the event of an "incoherent" will be about 36 minutes, and the UAV-car connection - about 5 minutes.&#x0D; Conclusions. The system and functional parameters of the networks, which were obtained as a result of the research, will form the basis of the initial data and limitations of the mathematical model, and will also make it possible to determine the initial placement of the UAV network at the planning stage.

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity. Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput.

Hedge transfer learning routing for dynamic searching and reconnoitering applications in 3D multimedia FANETs.

With the fast development of unmanned aerial vehicles (UAVs) and the user increasing demand of UAV video transmission, UAV video service is widely used in dynamic searching and reconnoitering applications. Video transmissions not only consider the complexity and instability of 3D UAV network topology but also ensure reliable quality of service (QoS) in flying ad hoc networks (FANETs). We propose hedge transfer learning routing (HTLR) for dynamic searching and reconnoitering applications to address this problem. Compared with the previous transfer learning framework, HTRL has the following innovations. First, hedge principle is introduced into transfer learning. Online model is continuously trained on the basis of offline model, and their weight factors are adjusted in real-time by transfer learning, so as to adapt to the complex 3D FANETs. Secondly, distributed multi-hop link state scheme is used to estimate multi-hop link states in the whole network, thus enhancing the stability of transmission links. Among them, we propose the multiplication rule of multi-hop link states, which is a new idea to evaluate link states. Finally, we use packet delivery rate (PDR) and energy efficiency rate (EER) as two main evaluation metrics. In the same NS3 experimental scenario, the PDR of HTLR is at least 5.11% higher and the EER is at least 1.17 lower than compared protocols. Besides, we use Wilcoxon test to compare HTLR with the simplified version of HTLR without hedge transfer learning (N-HTLR). The results show that HTRL is superior to N-HTRL, effectively ensuring QoS.

Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning

Since unmanned aerial vehicles (UAVs), such as drones, are used in various fields due to their high utilization and agile mobility, technologies to deal with multiple UAVs are becoming more important. There are many advantages to using multiple drones in a swarm, but, at the same time, each drone requires a strong connection to some or all of the other drones. This paper presents a superior approach for the UAV network’s routing system without wasting memory and computing power. We design a routing system called the geolocation ad hoc network (GLAN) using geolocation information, and we build an adaptive GLAN (AGLAN) system that applies reinforcement learning to adapt to the changing environment. Furthermore, we increase the learning speed by applying a pseudo-attention function to the existing reinforcement learning. We evaluate the proposed system against traditional routing algorithms.

Cooperative Multitask Planning Strategies for Integrated RF Systems Aboard UAVs

Limited by the load capacity of UAVs, it is difficult for an integrated radio frequency (RF) system aboard a single platform to have both wide-area and comprehensive battlefield sensing capabilities. One possible approach to solve this dilemma is to use multiple UAVs to perceive the scene cooperatively and simultaneously. To this end, this paper mainly discusses the cooperative task planning strategies facing cooperative UAVs with integrated RF systems when performing several tasks simultaneously. First, considering the complexity of the planning problem, the physical model for UAV formation cooperation is discussed. Then, based on the irregular and ad hoc characteristics of cooperative UAV networks, the essential compositions for UAVs cooperation are formulated that includes input information and planning constraints as well as evaluation indicators. Furthermore, to solve the given task planning problem, four new planning strategies are targeted designed for different planning purposes. Finally, a simulated cooperative UAV multitask planning scenario including cooperative detection, cooperative localization, and jamming is designed. Simulation results verify the effectiveness of these strategies as well as their advantages, disadvantages, and the multiscenario adaptability of each strategy.

Energy Constrained Sum-Rate Maximization in IRS-Assisted UAV Networks With Imperfect Channel Information

The focus of this work is maximizing the sum-rate of wireless unmanned aerial vehicle (UAV) networks with intelligent reflecting surfaces (IRS) in the presence of system practical limitations. More specifically, we consider that the phase compensation at the IRS is imperfect due to various factors such as device imperfections and channel estimation errors. Moreover, we consider that the IRS elements have limited switching frequency, which limits the possibility of being allocated to different UAVs over consecutive time slots when time-division multiple access (TDMA) is considered. To this end, we formulate an optimization problem where the objective is to maximize the network sum-rate subject to total energy and quality-of-service (QoS) constraints by optimizing the number of IRS elements and power allocated to each UAV. To solve the optimization problem, a low-complexity heuristic algorithm is proposed based on the quality of the estimated phase for each IRS element. The proposed approach is compared to benchmark techniques such as the uniform allocation process and genetic algorithm. The obtained results show that a significant sum-rate improvement of up to 45% can be gained using the proposed algorithm.