Unmanned Aerial Vehicles Networks Research Articles

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.

Secure UAV adhoc network with blockchain technology.

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency.

Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.

An efficient online/offline heterogeneous proxy signcryption for secure communication in UAV networks

The rapid growth of the Internet of Things (IoT) has led to an increased deployment of unmanned aerial vehicles (UAVs) across various sectors. However, efficiency and security issues are persistently among the primary challenges in UAV networks. In addition, significant communication delays can occur when UAVs perform remote tasks far from a command center (CC); in some cases, they may be unable to communicate with the CC. To address these challenges, in this paper, an efficient online/offline heterogeneous proxy signcryption scheme for secure communication in UAV networks (HOOPSC) is proposed. This scheme enables the CC in a certificateless cryptosystem (CLC) environment to delegate a nearby ground control station (GCS) to act as an agent, and directly send commands to the UAV within an identity-based cryptosystem (IBC) when the UAV undertakes remote tasks far from the CC. The UAV then decrypts and verifies commands for authenticity and confidentiality. In the proposed scheme, the signcryption process is split into offline and online phases, with most of the heavy computations conducted without the availability of the messages during the offline phase. Only light computations are performed in the online phase when a message is available. Moreover, a formal security proof is given in a random oracle model. Finally, a performance analysis reveals that HOOPSC outperforms existing relevant schemes, making it ideal for long-range operations in UAV networks.

Performance Analysis of Cooperative Caching and Transmission Diversity in Cache-Enabled UAV Networks

Performance Analysis of Cooperative Caching and Transmission Diversity in Cache-Enabled UAV Networks

Taas: Trust assessment as a service for secure communication of green edge-assisted UAV network

Taas: Trust assessment as a service for secure communication of green edge-assisted UAV network

Performance of UAV Networks Over the Ocean: A Perspective Towards Connected Ocean

Performance of UAV Networks Over the Ocean: A Perspective Towards Connected Ocean

UAV swarm communication reliability based on a comprehensive SINR model

Communication reliability is one of the most important factors in ensuring the safe operation of unmanned aerial vehicles (UAVs). The reliability of wireless communication for a UAV swarm is typically characterized by the signal-to-interference-and-noise ratio (SINR). However, previous work on UAV swarm communication reliability did not use a comprehensive SINR model. In this paper, we derived two novel closed-form approximations – lognormal and generalized beta prime (GBP) - of UAV swarm communication reliability based on a comprehensive SINR model. The model includes shadowing, multipath fading, the dependence of fading on external factors, which are the probability of line-of-sight (LoS) and the physical environment, as well as all possible interference within the system, which may be from the UAVs and the ground control station (GCS). For typical swarm heights between 60 – 300 m and up to 32 UAVs, comparisons of the approximate and simulated reliabilities for UAV swarms with a radius up to a critical value (which increases with the number and transmit power of the UAVs) show that only the lognormal approximation is accurate for all uplink and downlink communications, up to a critical horizontal distance from the GCS (which increases with height). Benchmarking of the lognormal approximation shows that neglecting either shadowing or multipath fading leads to high approximation errors. An example of how the lognormal approximation can be used to evaluate and maintain the communication reliability of a UAV swarm during deployment is given. Furthermore, it can be used to derive other SINR-dependent performance metrics, such as ergodic capacity and symbol error rate, which are useful in UAV network design and monitoring.

Distributed MA‐IDDPG‐OLSR based stable routing protocol for unmanned aerial vehicle ad‐hoc network

AbstractIn unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node.

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.

Autonomous UAV Networks Based on Artificial Intelligence

Autonomous UAV Networks Based on Artificial Intelligence

A congestion-aware user–cell association scheme to assist with traffic offloading in a three-tier heterogeneous network

In this work, we aim to expand the cell range in congestion areas. To achieve this goal, we integrate UAV network into macro–micro cellular network to enhance coverage. However, the gain of deploying heterogeneous nodes is limit if there are no CRE (cell range expanding)-based method to steer users towards low-power nodes. Therefore, in order to further improve system access capacity, we propose a congestion-aware UA (user–cell association) scheme to offload queued traffic of cellular network into UAV network. To verify the superiority of proposed scheme, we utilize the low-complexity SG (stochastic-geometry) methods. Then in simulation analysis phase, we obtain five primary results: (a). The SG-based methods have lower computation complexity, and the convergence of derived expressions are verified. (b). The SG-based methods do not need the exact users’ location but the users’ density, which have improved the operability and robustness in infrastructure construction of wireless network. (c). The congestion-aware UA scheme can prompt network to adjust association tier flexibly, which is very suitable for traffic unevenly-distributed regions. (d). The optimal offsets of virtual power are estimated as B2 = −4 dB for small-cell and B3 = 3 dB for drone-cell, the resulting improvement in communication capacity is 8%. (e). We suggest the deployment density of UAV sites will no need to exceed λ3 = 41 (pks/km2).

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

Communication architecture and operations for SDR-enabled UAVs network in disaster-stressed areas

The article presents a collaboration and communication architecture for disaster stressed areas where communication losses restrain disaster response and perplex rescue operations. In these areas, the fragile access networks such as cellular or Wi-Fi systems become congested or inaccessible, requiring a reliable emergency communication infrastructure. The proposed architecture involves Unmanned Aerial Vehicles (UAVs), equipped with wireless technologies i.e., 3G/4G/5G/Wi-Fi systems, and manifestly Software Defined Radios (SDRs), enabling self-forming, self-organizing, and self-healing capabilities. The architecture uses a Hybrid Connectivity Module (HCM) which empowers cooperative communication capacities and abridge the technologies to achieve reliable communication link between UAVs, mobile-to-UAV, and UAV-to-mobile towards Ground Control Station (GCS) in catastrophic areas. The seamless connectivity via next generation SDR waveforms and designed algorithms ensure an implementation of versatile multi-user UAVs network. This enables the formation of a backbone network assisting ground devices to communicate GCS. For cooperative communication, the architecture provides a MAC-centric cross-layer protocol which offers efficient multihop routing between UAVs. The data (i.e., text, images, or voice) is routed to devices or GCS via UAVs, which can notify rescue services reliably and help to trace the catastrophic zones. Furthermore, the protocol helps to allocate network resources by creating virtual sub-nets for simultaneous interference-free data transmissions which maximizes the network throughput. The experimental results show the data latency far better than any other existing ad hoc routing protocol (e.g., OLSR or AODV) based designs which usually add delays in the emergency response operations.

On performance analysis of non-orthogonal multiple access downlink for cellular-connected unmanned aerial vehicle relaying assisted vehicle-to-everything system

This paper presents the unmanned aerial vehicle (UAV) relays’ assisted vehicle-to-everything (V2X) network to implement the internet of things (IoT) systems with improvement in the coverage area. Such a network benefits from many advantages of the non-orthogonal multiple access (NOMA) scheme. We have implemented a decode-and-forward (DF) scheme for these UAVs. Then, we characterize the channels as Nakagami-m fading to evaluate the performance of the system. We derive closed-form expressions of outage probability (OP), ergodic capacity (EC), and throughput. The results show that the performance of the system depends on the transmitted signal-to-noise ratio (SNR) at the base station and the heights of the UAV relays. Target rate and power allocation factors are two main parameters that can be adjusted to achieve better performance. The results also compare to the system without UAV and OMA technique that shows the advantages of deploying UAV-assisted NOMA. Therefore, the design of NOMA for UAV relay-assisted V2X systems provides sufficient demand. The simulation results verified the effectiveness of the proposed UAV network and the precision of the theoretical analysis.

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

AbstractUnmanned aerial vehicles (UAVs) are becoming a popular research topic in applications that do not require human intervention. A variety of applications and devices coexist in the environment where UAVs operate, resulting in a serious spectrum shortage. Therefore, cognitive radio (CR) is a promising solution for opportunistic access to underutilized spectrum bands by the primary user (PU) through cooperative spectrum sensing (CSS) technique. However, the flexible location of UAVs makes CSS inefficient and even difficult to be implemented. In view of this, a cognitive UAV network model consisting of a pair of UAVs which follows a circular flight trajectory to participate in CSS is proposed in a spectrum sensing frame structure. According to the local energy detection, we further propose an optimization problem about the stopping time in a quickest detection paradigm and conduct out Bayesian detection method with feedback to minimize the sensing delay and the false alarm probability by optimizing the stopping time. Moreover, we theoretically derive the optimal threshold pair and prove the optimal stopping time by means of Markov process. At last, a series of numerical simulations are shown to corroborate the proposed Bayesian detection method with feedback, in terms of the false alarm probability, the sensing delay, and achievable throughput. In contrast to the classic Neyman‐Pearson and Bayesian detection methods, the advantage of Bayesian detection method with feedback sensing is presented.

Delay Analysis of UAV Networks with Hybrid Wireless and Power Line Communication

Delay Analysis of UAV Networks with Hybrid Wireless and Power Line Communication

A Survey on UAV Network for Secure Communication and Attack Detection: A focus on Q-learning, Blockchain, IRS and mmWave Technologies

A Survey on UAV Network for Secure Communication and Attack Detection: A focus on Q-learning, Blockchain, IRS and mmWave Technologies

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.