Unmanned Aerial Vehicles Networks Research Articles

Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms

The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD’s path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO’s superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC.

Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems

In aerial surveillance systems, achieving optimal object detection precision is of paramount importance for effective monitoring and reconnaissance. This article presents a novel approach to enhance object detection accuracy through the integration of Delaunay triangulation with multi-unmanned aerial vehicle (UAV) systems. The methodology involves positioning multiple UAVs at pre-specified locations using the Delaunay triangulation algorithm with performance of O (n log n). This is compared with the conventional single UAV approach at a near distance. Our findings reveal that the collaborative efforts of multiple UAVs, guided by Delaunay triangulation, significantly improves object detection accuracy, especially when compared to a single UAV operating in close proximity. This research employs advanced image processing techniques to identify objects in the area under surveillance. Results indicate a substantial enhancement in the collective surveillance capabilities of the multi-UAV system, demonstrating its efficacy in unconstrained scenarios. This research not only contributes to the optimization of aerial surveillance operations but also underscores the potential of spatially informed UAV networks for applications demanding heightened object detection accuracy. The integration of Delaunay triangulation with multi-UAV systems emerges as a promising strategy for advancing the capabilities of aerial surveillance in scenarios ranging from security and emergency response to environmental monitoring.

Energy efficient deployment of aerial base stations for mobile users in multi-hop UAV networks

Unmanned aerial vehicles (UAVs) are popularly considered as aerial base stations in a Low-Altitude Platform (LAP) to provide wireless connections to ground users in disaster and digital divide situation. Most of previous studies have investigated energy efficient UAV deployment only for the stationary ground users. Contrarily, we propose an on-line deployment algorithm for ground mobile users (GMUs) who are partially observable in the multi-UAV system. In the framework of Partially Observable Markov Decision Process (POMDP) with large state and action space, we use the Monte Carlo Tree Search (MCTS) algorithm enhanced by the Double Progressive Widening (DPW) and Deep Neural Network (DNN)-based guidance. For online learning of the DNN, simulated samples are used with Proximal Policy Optimization (PPO) that prevents excessive training for a particular belief. From experiment in urban environment, the proposed scheme shows better learning performance than the MCTS with rollout policy and faster convergence than training with environment samples. In addition, it optimizes dual objectives proportionally in a trade-off between energy saving and throughput including prediction error on GMU location.

Sensing-Assisted Beamforming and Trajectory Design for UAV-Enabled Networks

Sensing-Assisted Beamforming and Trajectory Design for UAV-Enabled Networks

Cell-Free UAV Networks With Wireless Fronthaul: Analysis and Optimization

Cell-Free UAV Networks With Wireless Fronthaul: Analysis and Optimization

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

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

Statistical A2G Coverage Characteristics in Dynamic Fixed-Wing UAV Networks

Statistical A2G Coverage Characteristics in Dynamic Fixed-Wing UAV Networks

Stable matching with evolving preference for adaptive handover in cellular-connected UAV networks

Unmanned Aerial Vehicles (UAVs) can effectively and reliably complete aerial tasks in various fields. However, the high-speed mobility of UAVs and the 6G ultra-dense heterogeneous network architecture in future will lead to frequent handover of UAVs during aerial tasks in cellular networks, increasing the potential service interruption rate. Therefore, there is an urgent requirement to address the lack of service adaptability in the overall time-space dimension. To achieve the seamless handover in highly dynamic and stochastic scenarios, we firstly transform the handover problem into a stable matching model. We then extend the stable matching of single time slot to the overall time-space dimension, and transform the dynamically changing time-space information into the preference relations evolution. By flexibly adapting the evolution of the preference lists to current network topology, we propose a Dynamic Stable Matching based Adaptive Handover (DSMAH) algorithm to find stable matching efficiently in a dynamic environment. Simulation results show that the proposed algorithm can achieve a better trade-off between communication quality, handover frequency, and convergence speed and significantly improves the stability of the cellular-connected network as compared to benchmark schemes. The proposed scheme not only effectively addresses the challenges posed by frequent handovers and ping-pong effect, but also shows its notable advantages in dynamic environments.

Joint User Association and Power Control in UAV Network: A Graph Theoretic Approach

Unmanned aerial vehicles (UAVs) have recently been widely employed as effective wireless platforms for aiding users in various situations, particularly in hard-to-reach scenarios like post-disaster relief efforts. This study employs multiple UAVs to cover users in overlapping locations, necessitating the optimization of UAV-user association to maximize the spectral and energy efficiency of the UAV network. Hence, a connected bipartite graph is formed between UAVs and users using graph theory to accomplish this goal. Then, a maximum weighted matching-based maximum flow (MwMaxFlow) optimization approach is proposed to achieve the maximum data rate given users’ demands and the UAVs’ maximum capacities. Additionally, power control is applied using the M-matrix theory to optimize users’ transmit powers and improve their energy efficiency. The proposed strategy is evaluated and compared with other benchmark schemes through numerical simulations. The simulation outcomes indicate that the proposed approach balances spectral efficiency and energy consumption, rendering it suitable for various UAV wireless applications, including emergency response, surveillance, and post-disaster management.

A secure and efficient UAV network defense strategy: Convergence of blockchain and deep learning

Unmanned Aerial Vehicles (UAVs) are highly versatile and efficient tools utilized across diverse industries for data collection. However, they are vulnerable to wireless communication and data exchange risks, including unauthorized access, data theft, and network attacks. To address these problems, we introduce a secure and reliable UAV network service architecture that incorporates blockchain and deep learning to provide more secure and efficient network services for UAVs. We propose a UAV cluster identity management module by combining blockchain, encryption algorithms, and digital signatures to enhance the security of UAV communication data transmission. Then, based on machine learning, deep learning, and malicious process detection technology, we propose a real-time secure situational awareness system for UAV cluster terminal devices to enhance the security of the operating environment for UAVs. Finally, we propose a data-trustworthy interconnection platform based on blockchain, smart contracts, and consensus algorithms to realize secure and efficient sharing and transmission of terminal data. The results of the experiments demonstrate the feasibility and effectiveness of our UAV network service architecture.

Cyber attacks on UAV networks: A comprehensive survey

New technologies are constantly emerging in the modern world and changing the way we live our everyday lives. Although technology has many useful applications, there are various ways it can potentially be abused. Unmanned aerial vehicles (UAVs) are one of the most rapidly developing technologies, with potentially far-reaching consequences. A new focus on UAV applications has fueled rising concerns with regards to security, specifically around networked UAVs. UAVs may be managed from a remote place with relative ease. Essential operations involving the use of military tactics and weapons involve employing them in a variety of situations, such as reconnaissance, surveillance, and offensive, defensive, and civilian capacities. Message insertion, message manipulation, jamming, and GPS spoofing are the most commonly used cyber-attacks against these systems. Ensuring the security of electronics and communications in systems that employ several UAVs is of utmost importance to guarantee their safety and dependability in military and civilian activities. Many technological methods have been developed over the past decade for securing UAVs from cyber-attacks. This paper attempts to summaries the problems that can arise with unmanned aerial vehicles (UAVs), cyber-attacks, and the countermeasures used to protect against them. This is the first paper that details all of the past cyber-attacks on unmanned aerial vehicles (UAVs).

FedRDR: Federated Reinforcement Distillation-Based Routing Algorithm in UAV-Assisted Networks for Communication Infrastructure Failures

Traditional Internet of Things (IoT) networks have limited coverage and may experience failures due to natural disasters affecting critical IoT devices, making it difficult for them to provide communication services. Therefore, how to establish network communication service more efficiently in the presence of fault points is the problem we solve in this paper. To address this issue, this study constructs a hierarchical multi-domain data transmission architecture for an emergency network with unmanned aerial vehicles (UAVs) employed as core communication devices. This architecture expands the functionality of UAVs as key network devices and provides a theoretical basis for their feasibility as intelligent network controllers and switches. Firstly, the UAV controllers perceive the network status and learn the spatio-temporal characteristics of air-to-ground network links. Secondly, a routing algorithm within the domain based on federated reinforcement distillation (FedRDR) is developed, which enhances the generalization capability of the routing decision model by increasing the training data samples. Simulation experiments are conducted, and the results show that the average communication data size between each domain controller and the server is approximately 45.3 KB when using the FedRDR algorithm. Compared to the transmission of parameters through federated reinforcement learning algorithms, FedRDR reduces the transmitted parameter size by approximately 29%. Therefore, the FedRDR routing algorithm helps to facilitate knowledge transfer, accelerate the training process of intelligent agents within the domain, and reduce communication costs in resource-constrained scenarios for UAV networks and has practical value.

Identifying a Malicious Node in a UAV Network

Identifying a Malicious Node in a UAV Network

Toward Facilitating Power Efficient URLLC Systems in UAV Networks Under Jittering

Toward Facilitating Power Efficient URLLC Systems in UAV Networks Under Jittering

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.

Joint Trajectory Design and Power Allocation in NOMA-Based UAV Networks

Joint Trajectory Design and Power Allocation in NOMA-Based UAV Networks

Cooperative anti-jamming and interference mitigation for UAV networks: A local altruistic game approach

Cooperative anti-jamming and interference mitigation for UAV networks: A local altruistic game approach

Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach

Flying Energy Sources (FESs) have been proven highly effective in transferring energy to battery-powered Unmanned Aerial Vehicles (UAVs). Such wireless transfer techniques in the literature were able to extend the flight duration of UAVs; however, they overlooked the fair distribution of energy among UAVs, which is of utmost importance for supporting diverse application demands. In this work, we quantify the urgency level of a UAV following its application responsibility, energy charging, and drainage rates and develop a framework for optimal energy transfer from FESs to UAVs in a weighted-fair way. Even though the developed framework can promote a highly balanced and fair energy distribution, its computation cannot always be done in polynomial time. Alternatively, to achieve a real-time solution, we further develop a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, namely AERIAL, to effectively model the complex interactions and dependencies among the UAVs and FESs and improve the wireless energy transfer process. The AERIAL system is implemented in the OpenAI Gym simulator platform and its performances have been compared with the state-of-the-art approaches. As high as improvements of 25.2% in fairness and 19.4% in average energy level demonstrated by the AERIAL system prove its effectiveness.

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.

Reputation-based Raft-Poa layered consensus protocol converging UAV network

Integration of blockchain technology offers a robust solution for ensuring reliable data transmission within unmanned aerial vehicle (UAV) networks. Nevertheless, it is crucial to address challenges associated with UAV networks, including their dynamic nature, high latency, limited scalability, and algorithmic inefficiencies. Hence, our proposal involves integrating UAV networks with blockchain technology to enhance their performance by optimizing the consensus algorithm based on network efficiency. We first propose a comprehensive framework for optimizing network performance based on network tomography, incorporating a systematic analysis of network performance and fault avoidance strategies. To improve analysis of the core network’s real-time performance, we also propose a topology prediction method based on an effective probability matrix. Additionally, we establish a calculation method for optimally placing network monitoring points in the core network for enhanced efficiency. We then introduce an adjustable reputation-based Raft-Poa layered consensus protocol that improves consensus protocol scalability and efficiency. To tackle data and node security concerns, we further propose a trust management mechanism based on group signatures. This mechanism verifies the authenticity of node data and identifies malicious nodes. Finally, we validate the protocol’s effectiveness and compare the algorithm’s effectiveness with MMP, Raft, Poa, etc. The simulation results of this protocol show remarkable improvements in network communications and consensus process efficiency, and reductions in communication latency and node utilization rates.