Aerial Vehicle Networks Research Articles

LDM-Enhanced Computation and Communication Codesign in Unmanned Aerial Vehicle Networks

LDM-Enhanced Computation and Communication Codesign in Unmanned Aerial Vehicle Networks

Corrections to “A Hierarchical Unmanned Aerial Vehicle Network Intrusion Detection and Response Approach Based on Immune Vaccine Distribution”

Corrections to “A Hierarchical Unmanned Aerial Vehicle Network Intrusion Detection and Response Approach Based on Immune Vaccine Distribution”

Performance evaluation for Q-learning based anycast routing protocol in unmanned aerial vehicle networks with multiple base stations

Unmanned Aerial Vehicle (UAV) networks can be used for data transmission in emergency scenarios, relaying data from ground users to base stations (BSs). While UAV networks collaborating with multi-BSs can significantly enhance performance, existing UAV routing protocols predominantly focus on unicast routing and often neglect critical aspects such as base station discovery. In addition, the high mobility of UAVs and rapid changes in network topology also pose great challenges for existing multi-BS routing protocols to maintain efficient data transmission. Aiming at the above problems, this paper abstracts the routing of multi-base station UAV networks as anycast routing for dynamic networks and proposes a distributed anycast routing protocol called QARP to improve the data transmission performance. In QARP, base stations can be discovered automatically and parameters of Q-learning are dynamically adjusted to improve the efficiency of data transmission. The Link Duration Estimation is used to influence routing decision and dynamically adjust the hello message interval. A multiple base stations transmission value function is designed to indicate the performance of data transmission and is used to calculate the reward and update Q-table. The experimental results show that the QARP proposed in this paper outperforms existing multi-BS routing and Q-learning based routing protocols in terms of delay, packet delivery ratio and throughput in single base station and multiple base stations scenarios.

BAKAS-UAV: A Secure Blockchain-Assisted Authentication and Key Agreement Scheme for Unmanned Aerial Vehicles Networks

BAKAS-UAV: A Secure Blockchain-Assisted Authentication and Key Agreement Scheme for Unmanned Aerial Vehicles Networks

Energy optimization and age of information enhancement in multi‐UAV networks using deep reinforcement learning

AbstractThis letter introduces an innovative approach for minimizing energy consumption in multi‐unmanned aerial vehicles (multi‐UAV) networks using deep reinforcement learning, with a focus on optimizing the age of information (AoI) in disaster environments. A hierarchical UAV deployment strategy that facilitates cooperative trajectory planning, ensuring timely data collection and transmission while minimizing energy consumption is proposed. By formulating the inter‐UAV network path planning problem as a Markov decision process, a deep Q‐network (DQN) strategy is applied to enable real‐time decision making that accounts for dynamic environmental changes, obstacles, and UAV battery constraints. The extensive simulation results, conducted in both rural and urban scenarios, demonstrate the effectiveness of employing a memory access approach within the DQN framework, significantly reducing energy consumption up to 33.25% in rural settings and 74.20% in urban environments compared to non‐memory approaches. By integrating AoI considerations with energy‐efficient UAV control, this work offers a robust solution for maintaining fresh data in critical applications, such as disaster response, where ground‐based communication infrastructures are compromised. The use of replay memory approach, particularly the online history approach, proves crucial in adapting to changing conditions and optimizing UAV operations for both data freshness and energy consumption.

Cloud-Edge Collaborative Optimization Based on Distributed UAV Network

With the continuous development of mobile communication technology, edge intelligence has received widespread attention from academia. However, when enabling edge intelligence in Unmanned Aerial Vehicle (UAV) networks where drones serve as edge devices, the problem of insufficient computing power often arises due to limited storage and computing resources. In order to solve the problem of insufficient UAV computing power, this paper proposes a distributed cloud-edge collaborative optimization algorithm (DCECOA). The core idea of the DCECOA is to make full use of the local data of edge devices (i.e., UAVs) to optimize the neural network model more efficiently and achieve model volume compression. Compared with the traditional Taylor evaluation criterion, this algorithm consumes less resources on the communication uplink. The neural network model compressed by the proposed optimization algorithm can achieve higher performance under the same compression rate.

A blockchain-assisted lightweight UAV network authentication mechanism via covert communication

The increasing importance of terminal privacy in the Unmanned Aerial Vehicle (UAV) network has led to a growing recognition of the crucial role of authentication technology in UAV network security. However, traditional authentication approaches are vulnerable due to the transmission of identity information between UAVs and cryptographic paradigm management centers over a public channel. These vulnerabilities include brute-force attacks, single point of failure, and information leakage. Blockchain, as a decentralized distributed ledger with blockchain storage, tamper-proof, secure, and trustworthy features, can solve problems such as single-point-of-failure and trust issues, while the hidden communication in the physical layer can effectively resist information leakage and violent attacks. In this paper, we propose a lightweight UAV network authentication mechanism that leverages blockchain and covert communication, where the identity information is transmitted as covert tags carried by normal modulated signals. In addition, a weight-based Practical Byzantine Fault-Tolerant (wPBFT) consensus protocol is devised, where the weights are determined by the channel states of UAVs and the outcomes of past authentication scenarios. Simulation results demonstrate that the proposed mechanism outperforms traditional benchmarks in terms of security and robustness, particularly under conditions of low Signal-to-Noise Ratio (SNR) and short tag length.

Flavors of the Next Generation of Unmanned Aerial Vehicles Networks

Flavors of the Next Generation of Unmanned Aerial Vehicles Networks

A model for preventing radio interference in the communication system of unmanned aerial vehicles

Problem statement. The communication network of unmanned aerial vehicles (UAVs) has proven to be a promising model capable of operating independently and as a repeater to increase coverage and communication efficiency. However, densely distributed ground base stations with common communication frequencies inevitably generate interference within the transmission channel. The influence of interference can be effectively eliminated by implementing optical communication in free space in the communication network of an unmanned aerial vehicle. Goal. Development of a modal for effective elimination of radio interference for unmanned aerial vehicles through the use of a mixed millimeter-wave radio frequency communication system. Results. The simulation results show that the proposed network significantly increases the probability of troubleshooting and achieves an increase in the signal-to-noise ratio by approximately 4.6 dB at an average frequency of character errors, especially in scenarios where radio frequency lines are subject to severe interference or unfavorable weather conditions, unlike a fully radio frequency communication network. Practical significance. Improved communication reliability for UAV systems with an effective signal-to-noise ratio reduces the likelihood of packet loss and the bit error rate (BER) of transmitted data.

Vertical Switching Algorithm for Unmanned Aerial Vehicle in Power Grid Heterogeneous Communication Networks

The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.

Addressing environmental stochasticity in reconfigurable intelligent surface aided unmanned aerial vehicle networks: Multi-task deep reinforcement learning based optimization for physical layer security

Deep Reinforcement Learning (DRL) is a powerful tool for optimizing communications in reconfigurable intelligent surface (RIS)-assisted millimeter-wave unmanned aerial vehicle (UAV) systems, particularly for ensuring physical layer security by dynamically maximizing signal strength for legitimate users while minimizing leakage towards eavesdroppers. However, existing approaches often overfit the DRL agent to a non-stochastic environment, where the trajectories of users and eavesdroppers are fixed. This results in a single-task DRL agent that cannot generalize beyond the training environment/task. In response, this study introduces a novel multi-task DRL framework to address this limitation. Firstly, recognizing that not all tasks share the same solution, we proposed a reward-driven clustering method to group similar tasks based on their reward scores. This enables the training of a shared multi-task DRL agent for each cluster. Each cluster can be treated as an expertise, and the cluster-specific multi-task DRL agents are considered the 'experts'. Meanwhile, we leverage federated learning (FL) to speed up the training of the shared multi-task DRL agent. By combining these two techniques, our reward-driven clustered FL autonomously groups similar tasks together rapidly. Simulation results demonstrate a reduction in the number of agents from 50 to 10 specialized 'experts', while achieving a 10X speedup compared to conventional single-task DRL training. Furthermore, the study presents a mixture of experts (MoE) model that unifies these 'experts' into a single model. The MoE is trained to select the most suitable 'expert' when encountering new tasks. Our MoE exhibits robustness by effectively generalizing to 50 novel tasks without fine-tuning.

A Survey on Reputation Systems for UAV Networks

The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

IRS Integrated UAV Communication to Enhance Physical Layer Security: A Deep Reinforcement Learning approach

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

Novel Hybrid Deep Optimized Machine Learning Model for Energy Efficient Cluster Selection in Unmanned Aerial Vehicle Networks

Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches.

Novel Hybrid Deep Optimized Machine Learning Model for Energy Efficient Cluster Selection in Unmanned Aerial Vehicle Networks

Unmanned Aerial Vehicles (UAVs) have grown into a more powerful type of data transmission due to this rapid progress of evolution of wireless communication technology. In addition, UAVs have been proven to be effective in a variety of applications, including intelligent transport, disaster risk management, surveillance, and environmental monitoring. When UAVs are deployed randomly, however, they can effectively accomplish challenging tasks because of the UAVs’ has low battery capacity, quick mobility, and dynamic in nature orientation. Due to this reason, a new technique must be designed for an optimal energy efficient UAV clustering as well as data routing protocols. In this work proposes a new hybrid model of Emperor penguin-based Generalized Approximate Reasoning Based Intelligent Control (EP-GARIC) cluster-based network topology. Furthermore, the optimal routing function is achieved by the proposed Artificial Jellyfish Optimization (AJO). The implementation of this research is carried out using Network Simulator (NS2). The simulation results displays the effective performance of the suggested approach in terms of reduced energy consumption, improved packet delivery ratio, reduced loss, and so on over compared to the conventional approaches

Outage probability of cognitive radio-NOMA assisted unmanned aerial vehicles network

Unmanned aerial vehicles have been applied and play a critical role in radio surveillance because of their flexibility, mobility, and likely line-of-sight to ground destinations. Here, UAVs provide LoS links to users in the communication shadow of obstructive buildings. This paper investigates a cognitive radio network system model between unmanned aerial vehicles to transfer information from a licensed primary device to unlicensed users in a secondary network. In which PN users rely on non-orthogonal multiple access to prevent interference with each other. The primary objective of this study is to examine the outage probability of the secondary users with perfect and imperfect successive interference cancellation applications. Finally, we derive the users’ exact closed-form expressions and use Monte Carlo simulations to validate the analytical results.

Deep reinforcement learning enhanced skeleton based pipe routing for high-throughput transmission in flying ad-hoc networks

Artificial Intelligence (AI) is a huge step towards the emergence of powerful and smart decision-makers which can optimally tune the network parameters. In this work, a novel idea of an intelligent pipe (iPipe) routing scheme for a network of unmanned aerial vehicles (UAVs) is presented by considering the availability of a centralized entity with enough computational resources. The proposed scheme deploys artificial intelligence to optimally decide on the direction of the routing pipe (i.e. the medial axis), based on the current network information and the prediction of the immediate future. To address the potential influx of input data, the application of Deep Reinforcement Learning (DRL) is proposed to enhance the throughput performance and adapt more effectively to network dynamics. The scheme benefits from the concept of flying ad hoc network (FANET) to provide nodes with geometric coordinates in order to facilitate the data forwarding mechanism. Compared to the conventional AI-based routing strategies, the proposed scheme does not require making decisions for the routing table of all the nodes. Instead, it only finds the trend as a set of geometric indices which will be included in the packet headers. However, the pipe nodes still act in a distributed manner. They build their routing tables according to the trend suggested by the controller and adjust the transmission probability of each link based on the local feedback. This reduces the complexity of the centralized solution. The simulation results show that the proposed iPipe method can outperform the other state-of-the-art SSR routing scheme, which is a distributed but low-cost pipe routing.

Networking of Internet of UAVs: Challenges and Intelligent Approaches

Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and communication tasks quickly, robustly, safely, and cost-efficiently via effective situation awareness and cooperation among UAVs. To achieve the promising benefits, the crucial IUAV networking issue should be tackled. This article argues that I-UAV networking can be classified into two categories: quality-of-service (QoS) driven networking and quality-of-experience (QoE) driven networking. Each category of networking poses emerging challenges that have severe effects on the safe and efficient accomplishment of I-UAV missions. This article elaborately analyzes these challenges and expounds on the corresponding intelligent approaches to tackle the I-UAV networking issue. Besides, considering the uplifting effect of extending the scalability of I-UAV networks through cooperating with high altitude platforms (HAPs), this article gives an overview of the integrated I-UAV and HAP network and presents the corresponding networking challenges and intelligent approaches.

Power allocation for enhancing energy efficiency in unmanned aerial vehicle networks

SummaryThe non‐orthogonal multiple access (NOMA) wireless networks are a robust multi‐access mechanism that serves multiple clients accessing the same resource block simultaneously. The fifth‐generation wireless networks offer huge efficiency in spectrum utilization, which can be exploited to deploy NOMA for LOS communication of unmanned aerial vehicles (UAVs). Previous research has extensively analyzed various aspects of NOMA‐UAV communication systems, including user access fairness, coverage, maximizing system capacity, and total energy efficiency. However, only a few researchers have focused on maximizing the EE of NOMA‐UAV wireless networks with user quality of service (QoS) constraints. This paper proposes a fuzzy logic‐based crossover‐based coati optimization algorithm for maximizing the energy efficiency of NOMA‐UAV, along with user scheduling. The main objective of this model is to offer a solution to the joint energy efficiency and user QoS scheduling problem. The fuzzy decision‐making strategy optimizes the energy efficiency (EE) of NOMA‐UAV by selecting appropriate power and time resources. Additionally, the crossover‐based coati optimization algorithm transforms the subchannel allocation problem into a two‐sided matching procedure. The efficiency of the proposed algorithm is evaluated concerning overall residual energy, number of remaining nodes, and time consumption. The experimental outcomes demonstrate the capability of the proposed model in optimizing the energy efficiency of the NOMA‐UAV network by identifying the optimal resource set in terms of time and power while satisfying the clients' QoS.