UAV Nodes Research Articles

Sea-Based UAV Network Resource Allocation Method Based on an Attention Mechanism

As humans continue to exploit the ocean, the number of UAV nodes at sea and the demand for their services are increasing. Given the dynamic nature of marine environments, traditional resource allocation methods lead to inefficient service transmission and ping-pong effects. This study enhances the alignment between network resources and node services by introducing an attention mechanism and double deep Q-learning (DDQN) algorithm that optimizes the service-access strategy, curbs action outputs, and improves service-node compatibility, thereby constituting a novel method for UAV network resource allocation in marine environments. A selective suppression module minimizes the variability in action outputs, effectively mitigating the ping-pong effect, and an attention-aware module is designed to strengthen node-service compatibility, thereby significantly enhancing service transmission efficiency. Simulation results indicate that the proposed method boosts the number of completed services compared with the DDQN, soft actor–critic (SAC), and deep deterministic policy gradient (DDPG) algorithms and increases the total value of completed services.

UAV-enabled software defined data collection from an adaptive WSN

AbstractUnmanned aerial vehicle (UAV)-based data gathering from wireless sensor networks is one of the recent research topics that has currently attracted research interest. One of the challenges for the UAV-aided WSN data collection efforts is to design an energy-efficient UAV/drone communication with arbitrarily dispersed ground sensors by improving the ground network structure. This paper aims to develop a technique titled UAV Fuzzy Travel Path' that supports UAV smooth path design and enables ground network topology shifting. A comprehensive UAV-based data collection model is proposed to enable dynamic orchestration/re-orchestration of wireless ground sensors to jointly improve network performance and UAV path fluidity. This provides a more flexible ground network framework that can be restructured based on network demands and UAV optimal paths, effectively allowing for a software-defined network concept. The main contribution of this work is the implementation of the software-defined wireless sensor network on the ground network that adaptably supports the movement of the UAV and enhances the communication network’s energy efficiency with a proposed latency analytical analysis via network orchestration/re-orchestration phases. The main significance of this research is in offering a flexible span for UAV path design than being fixed in one strict route for data gathering purposes. Four various simulation tools are employed for modelling and performance evaluation, namely MATLAB, CupCarbon, Contiki-Cooja and Mission Planner. The proposed software-defined ground network system demonstrates encouraging results in terms of network performance metrics including energy consumption of UAV versus ground sensor nodes energy usage, packet delivery rate, and the communication time of the ground orchestrated or/and re-orchestrated network.

PUF-based dynamic secret-key strategy with hierarchical blockchain for UAV swarm authentication

The evolving UAV swarm flight environment exacerbates the difficulty of secure message delivery within the swarm. Physical Unclonable Functions (PUFs) have the potential to provide lightweight physical identities to UAV nodes. Nevertheless, the explicit storage of a private key within each UAV device invariably results in privacy leakage arising from physical hijacking. Therefore, to accomplish unified management and decentralized storage of identity informations for UAVs, as well as to simultaneously realize the dynamic updating of private key, we propose a PUF-based Dynamic Secret-key strategy with Hierarchical Blockchain (PDSHB) for UAV swarm authentication. Specifically, the master blockchain ensures that all UAVs’ registration information is tamper-proof, while the dynamic sub-blockchain synchronizes the UAV information within the swarm. Meanwhile, we have developed a more reliable and efficient PUF-based dynamic secret-key strategy that eliminates the need to store any secret information within UAVs, effectively mitigating the risk of key leakage resulting from physical attacks. Protocol security is demonstrated through formal security proofs, informal security analysis, and AVISPA security checking. Performance experiments based on Raspberry Pi 4B and FPGA-based F450 UAVs also show that the proposed protocol can deal with various security threats of dynamic swarms of UAVs and reduce computational and communication overheads during authentication.

A quality of service‐aware routing protocol for FANETs

SummaryFlying ad hoc networks (FANETs) are a class of highly dynamic mobile ad hoc networks in which flying UAVs coordinate among themselves for delivering the data to the applications. Data routing in FANETs has been a challenge due to the inherent mobile and unpredictable movement of UAV nodes. This paper proposes a quality of service‐aware routing protocol (QARP) for FANETs which incorporates hybrid nature‐inspired heuristics to identify optimized routes to the destination UAVs. The routing mechanism operates in two phases. In the first phase, the neighbour discovery initializes the adapted firefly algorithm to identify nodes with least packet delivery delay to the destination. In the second phase, the Gaussian quantum‐behaved particle swarm optimization meta‐heuristic algorithm is utilized to identify the most optimal nodes to create a route for data delivery to destination. The proposed protocol has been tested to perform better in terms of delay, routing overhead, throughput and packet delivery ratio in comparison to MA‐DP‐AODV, MDA‐AODV and MDRMA routing protocols.

Research on characterizing the high dynamic performance of distributed FANETs

Distributed Flying Ad-Hoc Networks (FANETs) have been widely used in collaborative reconnaissance, situation construction and other scenarios. Due to malicious damage, complex electromagnetic interference, and rapid movement of UAV nodes during flight, distributed FANET have high dynamic characteristics. However, most researches on the characterization of network dynamics cannot fully consider the influence of various factors, such as protocol self-interference. In this paper, by comprehensively considering the effects of node damage, channel fading, protocol self-interference and other factors, based on HCPP model, Weibull distribution model and other theories, we establish link outage probability of network model to characterize link randomness and high dynamicity. On this basis, link entropy rate and topology entropy rate model are established to characterize the high dynamic performance of distributed FANETs.

Cross-Modal Object Detection Via UAV

UAV-based object detection aims at locating and recognizing targets in aerial images, which is widely applied to traffic surveillance, disaster rescue and anomaly monitoring. However, due to expensive sensors and complicated architectures, it is unrealistic to deploy precise but heavy multi-modal object detectors into UAV nodes. To get over the dilemma, inspired by model compression and cross-modal signal processing techniques, this paper proposes a cross-modal knowledge distillation (CKD) enabled object detection paradigm, which achieves comparable detection performance with multi-modal techniques, yet requires less computational resource. On the one hand, in order to avoid transferring redundant feature knowledge, we design a Selective Feature Imitation (SFI) to selectively shorten the distance between cross-modal features according to their types. On the other hand, in order to transfer the most valuable prediction knowledge, we design an Adaptive Prediction Imitation (API). It evaluates the quality of prediction knowledge, and then adaptively adjusts the distillation intensity for cross-modal prediction. Extensive experiments on the DroneVehicle dataset have shown the performance improvement of the proposed scheme.

Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.

Coupled-Map-Lattices-Based Vulnerability Assessment of UAV Network in Interference Scenarios

On the modern information battlefield, UAV have been widely used due to the advantages of no casualties and good maneuverability. However, during the UAV swarm combat, the UAV network will be interfered by the enemy, which will damage some key UAV nodes or communication links and affect the connectivity of the entire network, thus leading to the fact that the entire network becomes more vulnerable. Therefore, it is necessary to study the network vulnerability of UAV networks in interference scenarios. In this paper, a coupled map lattice (CML) model which is a dynamic system with discrete time, discrete space, and continuous state variables is proposed to assess the vulnerability of UAV networks. The CML model integrates multiple topological indicators such as node degree, node betweenness, and node clustering coefficient, and reflects the node state change of the UAV network in the interference scenario from the topological point of view. When changing the strategy of interfering UAV nodes in different interfering scenarios, the relative network efficiency and failure proportion are used as indicators to study the change of network vulnerability. The studies show that precisely interfering important UAV nodes in a network can cause more damage to the UAV network. We also discover that as the intensity of external interference increases, the entire network will become increasingly vulnerable and the vulnerability of the network will also have different manifestations under different interfering strategies.

Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information

In this paper, we study to employ geographic information to address the blockage problem of air-to-ground links between UAV and terrestrial nodes. In particular, a UAV relay is deployed to establish communication links from a ground base station to multiple ground users. To improve communication capacity, we first model the blockage effect caused by buildings according to the three-dimensional (3-D) geographic information. Then, an optimization problem is formulated to maximize the minimum capacity among users by jointly optimizing the 3-D position and power allocation of the UAV relay, under the constraints of link capacity, maximum transmit power, and blockage. To solve this complex non-convex problem, a two-loop optimization framework is developed based on Lagrangian relaxation. The outer-loop aims to obtain proper Lagrangian multipliers to ensure the solution of the Lagrangian problem converge to the tightest upper bound on the original problem. The inner-loop solves the Lagrangian problem by applying the block coordinate descent (BCD) and successive convex approximation (SCA) techniques, where UAV 3-D positioning and power allocation are alternately optimized in each iteration. Simulation results confirm that the proposed solution significantly outperforms three benchmark schemes and achieves a performance close to the upper bound on the UAV relay system.

Lightweight Mutual Authentication Scheme Enabled by Stateless Blockchain for UAV Networks

The UAV network composed of resource-constrained lightweight UAV swarms can efficiently accomplish mission with time critical requirements in dynamic and complex environments. However, the trusted authentication of network nodes poses a huge challenge due to its own resource constraints, the lack of trusted centralized support, frequent joining or departure of UAVs to or from the network, and the presence of cyber-attacks. In this paper, we propose a stateless blockchain based on triple aggregatable subvector commitment and present a dynamic proof of trust authorization consensus mechanism with a periodic random selection of authorized nodes to guarantee the trustworthiness of mutual authentication of UAV nodes. Our proposed triple vector authentication solution solves several of the challenges mentioned above very well. The extensive experiments demonstrate that our blockchain-based authentication scheme enjoins significant advantages over the four schemes currently available for UAV network authentication in terms of single authentication latency, speed of energy consumption, average computational cost, and end-to-end latency.

Secure transmission in satellite-UAV integrated system against eavesdropping and jamming: A two-level stackelberg game model

Aiming at the physical layer security (PLS) secure transmission existing in the information backhaul link of the satellite-UAV integrated (SUI) network, a two-layer Stackelberg game model (TSGM) that can resist full-duplex (FD) eavesdropping and jamming attacks is proposed. The confrontation relationship between the UAV network and the attacker is established as the first layer Stackelberg game. The source UAV adjusts its own transmission power strategy according to the attacker's jamming strategy to resist malicious jamming attacks. The internal competition and cooperation relationship in UAV network is modeled as the second layer Stackelberg game, and the optimal cooperative UAV transmits jamming signal to the attacker to resist malicious eavesdropping attacks. Aiming at the "selfishness" of UAV nodes, a price incentive mechanism is established to encourage UAV to actively participate in cooperation, so as to maximize the advantages of cooperative communication. For the proposed TSGM, we construct the utility function and analyze the closed equilibrium solution of the game model, and design a three-stage optimal response iterative (TORI) algorithm to solve the game equilibrium. The simulation results show that the proposed TSGM can effectively increase the utility of the source UAV and improve the enthusiasm of cooperation compared with other power control models.

ATS-LIA: A lightweight mutual authentication based on adaptive trust strategy in flying ad-hoc networks.

With the rapid development of wireless communication and edge computing, UAV-assisted networking technology has great significance in many application scenarios such as traffic forecasting, emergency rescue, military reconnaissance. However, due to dynamic topology changes of Flying Ad-hoc Networks (FANET), frequent identity authentication is easy to cause the instability of communications between UAV nodes, which makes FANET face serious identity security threats. Therefore, it is an inevitable trend to build a secure and reliable FANET. In this paper, we propose a lightweight mutual identity authentication scheme based on adaptive trust strategy for Flying Ad-hoc Networks (ATS-LIA), which selects the UAV with the highest trust value from the UAV swarm to authenticate with the ground control station (GCS). While ensuring the communication security, we reduce the energy consumption of UAV to the greatest extent, and reduce the frequent identity authentication between UAV and GCS. Through the security game verification under the random oracle model, it is proved that the proposed method can effectively resist some attacks, effectively reduce the computational overhead, and ensure the communication security of FANET. The results show that compared with the existing schemes, the proposed ATS-LIA scheme has lower computational overhead.

LB-OPAR: Load balanced optimized predictive and adaptive routing for cooperative UAV networks

Cooperative ad-hoc UAV networks have been turning into the primary solution set for situations where establishing a communication infrastructure is not feasible. Search-and-rescue after a disaster and intelligence, surveillance, and reconnaissance (ISR) are two examples where the UAV nodes need to send their collected data cooperatively into a central decision maker unit. Recently proposed SDN-based solutions show incredible performance in managing different aspects of such networks. Alas, the routing problem for the highly dynamic UAV networks has not been addressed adequately. An optimal, reliable, and adaptive routing algorithm compatible with the SDN design and highly dynamic nature of such networks is required to improve the network performance. This paper proposes a load-balanced optimized predictive and adaptive routing (LB-OPAR), an SDN-based routing solution for cooperative UAV networks. LB-OPAR is the extension of our recently published routing algorithm (OPAR) that balances the network load and optimizes the network performance in terms of throughput, success rate, and flow completion time (FCT). We analytically model the routing problem in highly dynamic UAV network and propose a lightweight algorithmic solution to find the optimal solution with O(|E|2) time complexity where |E| is the total number of network links. We exhaustively evaluate the proposed algorithm’s performance using ns-3 network simulator. Results show that LB-OPAR outperforms the benchmark algorithms by 20% in FCT, by 30% in flow success rate on average, and up to 400% in throughput.11DISTRIBUTION STATEMENT A: Approved for Public Release; distribution unlimited AFRL-2021-1606 on 25 May 2021. Other requests shall be referred to AFRL/RIT 525 Brooks Rd Rome, NY 13441.

An adaptive routing protocol in flying ad hoc networks

Nowadays, ad hoc networks play a vital role in wireless communication. It is a temporary network to relay the data between nodes. Ad hoc network is divided into different types such as MANET, VANET & FANET. FANET is a combination of MANET and VANET. In FANET, routing is a big challenging issue due to unique characteristics like dynamic topology, frequent changes of link quality, mobility of UAV nodes, etc. Reliability of routes is also a real challenge due to the very high mobility in FANET. In this research work, we have introduced an adaptive routing protocol, PF-WGTR - A predicted future weight-based routing scheme for FANETs that considers the node’s existing and upcoming values of assured parameters to determine the reliable routing path. This proposed routing protocol assigns weight to every node in the network by calculating the node’s future. Depending on the predicted future weight of every node in a network, the communicating nodes can establish a reliable path that persists for a long time. This adaptive, future prediction-based routing scheme ensures better data delivery with minimum overhead and optimized energy consumption in all conditions compared with the existing routing protocols. The NS-2 simulator is used to compare the proposed routing protocol with previous protocols in terms of FANET parameters. Finally, the simulation results show better performance than the existing works.

Delay performance of priority-queue equipped UAV-based mobile relay networks: Exploring the impact of trajectories

UAV-based mobile relay communication systems have an important position in future wireless networks, such as Beyond 5G and 6G, which provide ubiquitous network services. Along this line, UAV-based single-hop relay systems have numerous applications in emergency situations, such as data ferry and assisting post-disaster areas. Although there have been numerous works on optimizing trajectories and other communication resources on such systems, there are few studies on delay performance at the granular packet level. In addition to having homogeneous traffic, such a system can be equipped with diverse traffic with different QoS requirements, owing to the emergence of various applications in today’s communication networks. Based on the traffic requirements, packets with high-priority traffic should be served ahead of those with low-priority ones. To this end, we aim to analytically study the average end-to-end packet delay and buffer overflow performance of such a system at the granular packet level. However, packet-level queuing analysis of heterogeneous traffic is not as straightforward as that of homogeneous traffic. While considering different traffic requirements, we establish a priority queuing model for the source and the UAV nodes based on the stochastic process. Then, on the basis of the established queuing model, the average end-to-end packet delay and buffer overflow probability of the system under different trajectories are studied. Through extensive numerical simulations, we verified the accuracy and effectiveness of the proposed analytical model. The results show that the average packet delay and packet drop probability of high-priority packets under all trajectories are significantly better compared to low-priority packets. UAV trajectory has a great impact on network performance, and an appropriate mobile trajectory that takes full use of channel capacity is superior to a static one.

Performance Evaluation of Different ToS Using Heterogeneous Communication Interfaces in FANETs

The use of different types-of-services (ToS), such as voice, data, and video, has become increasingly present in the execution of applications involving networks composed of multiple UAVs. These applications usually require the UAVs to share different ToS in a dynamic and ad-hoc manner, such that they can support the execution of cooperative/collaborative tasks. The use of heterogeneous communication has showed gains in maintaining the connection among highly mobile nodes, while increasing the reliable transmission of data, as is necessary in MANETS, VANETs and, more recently, FANETs. The aim of this paper is to present a performance evaluation of a heterogeneous interface manager (IM), which applies a heuristic to choose the best among several single- and multi-band wireless communication interfaces, including IEEE 802.11n, IEEE 802.11p, IEEE 802.11ac, and IEEE 802.11ax. Simulated scenarios with three, five, and eight UAV nodes are developed by integrating NS-3 and Gazebo simulation tools. The IM performance is analyzed by applying different numbers of interfaces and comparing with interfaces applied homogeneously by defining two set of results, in terms of application and MAC and PHY metrics, respectively. Finally, we also evaluate the associated performance, considering voice, data, and video streaming ToS. The results indicate that the combination of different interfaces has a very powerful effect on maintaining or increasing the communication intensity.

Onboard Audio and Video Processing for Secure Detection, Localization, and Tracking in Counter-UAV Applications

Nowadays, UAVs are of fundamental importance in numerous civil applications like search and rescue and military applications like monitoring and patrolling or counter-UAV where the remote UAV nodes collect sensor data. In the last case, flying UAVs collect environmental data to be used to contrast external attacks launched by adversary drones. However, due to the limited computing resources on board of the acquisition UAVs, most of the signal processing is still performed on a ground central unit where the sensor data is sent wirelessly. This poses serious security problems from malicious entities such as cyber attacks that exploit vulnerabilities at the application level. One possibility to reduce the risk is to concentrate part of the computing onboard of the remote nodes. In this context, we propose a framework where detection of nearby drones and their localization and tracking can be performed in real-time on the small computing devices mounted on board of the drones. Background subtraction is applied to the video frames for pre-processing with the objective of an on-board UAV detection using machine-vision algorithms. For the localization and tracking of the detected UAV, multi-channel acoustic signals are instead considered and DOA estimations are obtained through the MUSIC algorithm. In this work, the proposed idea is described in detail along with some experiments and, then, methods of effective implementation are provided.

UAV-Enabled Relay Communication Under Malicious Jamming: Joint Trajectory and Transmit Power Optimization

In this letter, the UAV-enabled relay communication is investigated, where the UAV relay forwards the information received from the ground source node to the ground destination node under malicious jamming. We aim to maximize the end-to-end throughput by optimizing the trajectory of UAV and transmit power of both UAV and source node jointly. However, the formulated problem is intractable due to the non-convex objective function and constraints. Thus, with the aid of slack variables, we propose an efficient algorithm to solve it based on the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical results are provided to show that the joint design of trajectory and power control improves the throughput significantly.

A distributed fault‐tolerant mechanism for mission‐oriented unmanned aerial vehicle swarms

SummaryUnmanned aerial vehicle (UAV) swarm consists of a number of autonomous and collaborative UAV nodes. The purpose of the research is to address the hypotheses: an efficient centralized resources management scheme should be adopted to meet the mission‐oriented feature of UAV swarms, while a distributed fault‐tolerant mechanism should be adopted to meet the characteristics of complex environment and address the issue of high failure rate of UAV nodes. By using an experimental modeling method, this paper proposes a management model and related mechanisms fitting for UAV swarms with the feature of “centralized decision making and distributed task execution.” The model allows UAV nodes to manage their own resources autonomously and execute centralized decisions accordingly. The advantage of the model is that the master node of a UAV swarm is able to perceive all the resources of the system and the status of all (or some) of the UAVs efficiently; therefore, it provides a supportive environment for the UAV swarm application to make centralized decisions and optimize those decisions. Additionally, this paper proposes a distributed fault‐tolerant model and related mechanisms to address the issue of high failure rate of UAV nodes, as well as the system vulnerability caused by harsh environment. Finally, this study developed a prototype system designed and implemented by OMNeT++ to confirm the truth of the hypotheses. The analysis of the experimental data and simulation results of the prototype validated the feasibility and usability of the distributed fault‐tolerant mechanism and demonstrated its value for the mission‐oriented UAV swarms.

Implementation of FANET energy efficient AODV routing protocols for flying ad hoc networks [FEEAODV

In this technology driven world, wireless networks take part a key role in helping a society to enhance their life style.Unmanned air vehicles connected in an ad hoc manner been used in various application from civil projects to military applications. Establishing reliable, secure connection among UAV nodes in FANET is bit challenging task due to high node mobility, environmental condition under which it is operated and minimum energy capability of nodes etc. Routing protocols used in such a network should have unique features that overcome the limitation to make reliable and secured connectivity between nodes. All system units in flying UAV like transmitter, receiver equipment, control unit, information processing unit and payloads are powered by in build power sources .UAVs equipped with the limited on-board energy capability restrict the flying time, which will significantly affect the performance of a FANETs. Optimizing the energy consumption among nodes is one of the important research challenges .Optimization techniques for energy usage can be implemented at different OSI layer level. In our study we focused on OSI networks layers solution for performance improvements based on energy efficient routing techniques in flying ad hoc network environments. Routing algorithm that is used in MANETs applications can be optimized and used in FANETS as in both networks majority of the operational characteristics similar.