Unmanned Aerial Vehicles Networks Research Articles

UAVs-Aided Delay-Tolerant Blockchain Secure Offline Transactions in Post-Disaster Vehicular Networks

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, unmanned aerial vehicles (UAVs) with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes.

Resource Allocation for the Backhaul of NOMA-Based Cellular UAV Network

A cellular unmanned-aerial-vehicle (UAV)-enabled system is studied in this research, where multiple UAVs work cooperatively to upload the collected sensory data to the base station (BS). The UAVs form a two-tier network with two kinds of communication modes, i.e., UAV-to-Network (U2N) and UAV-to-UAV (UAV) communications. We investigate the communication resource allocation for UAVs to maximize the aggregate energy efficiency of the system. The formulated problem is mixed-integer and NP-hard. To make the formulated problem tractable, an efficient matching-based algorithm is proposed to complete the transmission mode selection and subchannel allocation, respectively. Then the transmission power is optimized based on game theory. The simulation results reveal that the uplink energy efficiency can be improved with the proposed scheme, compared with the maximum power strategy.

MEC-Enabled Fine-Grained Task Offloading for UAV Networks in Urban Environments

In recent years, with the continuous development of information technology, the amount of data generated and hosted by cloud service platforms in urban environments is unprecedented. Mobile edge computing (MEC) is combined with UAV networks to better realize the ability to provide nearby services to a large number of terminal devices in cities. Unmanned aerial vehicles (UAVs) are highly maneuverable and inexpensive and are good carriers for carrying MEC platforms. In UAV edge networks, we usually face the problem of fine-grained task offloading based on relevant features of urban environments. We need to address high energy consumption and task processing delays to help achieve urban sustainability goals. Therefore, we combine the software definition network (SDN) technology and, on this basis, we propose two task offloading strategies based on an improved EFO intelligent algorithm for different user scales. At the same time, we run the proposed offloading system in the UAV sensor. The experiment shows that, compared with the traditional strategy, the unloading efficiency of the proposed method can be improved by about 10%.

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.

Networks of UAVs of Low Complexity for Time-Critical Localization

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance, such as tracking the position of a malicious user. Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence. The UAV's intelligence includes the UAV's capability to process information and make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. This article overviews some existing signal processing techniques for distributed estimation and autonomous navigation of UAVs of low complexity. To this end, we show some of the needs of the UAVs for running efficient localization operations for time-limited missions, performed either as a team or individually. Furthermore, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.

Reinforcement Learning-Based Dynamic Anti-Jamming Power Control in UAV Networks: An Effective Jamming Signal Strength Based Approach

Unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communication is vulnerable to malicious jamming due to the broadcast nature of wireless communications. In this letter, an anti-jamming power control framework with an unknown jamming model and unknown transmission power is proposed. In particular, the probability density function (PDF) of the effective jamming signal strength (EJSS) is first estimated via kernel density estimation (KDE). Then, utilizing the EJSS, a deep deterministic policy gradient (DDPG) based framework is proposed to acquire the power control strategy in real time. Moreover, a trajectory design scheme based on K-means++ is proposed to track the location of users. The simulation results show that the proposed framework yields an improved sum rate and energy efficiency over the reference schemes.

Modulating Retroreflector Based Free Space Optical Link for UAV-to-Ground Communications

Weight reduction and low power consumption are key requirements in the next generation of unmanned aerial vehicle (UAV) networks. Employing modulating retro-reflector (MRR)-based free space optical (FSO) technology is an innovative technique for UAV-to-ground communication in order to reduce the payload weight and power consumption of UAVs which leads to increased maneuverability and flight time of UAV. In this paper, we consider an MRR-based FSO system for UAV-to-ground communication. We will show that the performance of the considered system is very sensitive to tracking errors. Therefore, to assess the benefits of MRR-based UAV deployment for FSO communications, the MRR-based UAV FSO channel is characterized by taking into account tracking system errors along with UAV’s orientation fluctuations, link length, UAV’s height, optical beam divergence angle, effective area of MRR, atmospheric turbulence and optical channel loss in the double-pass channels. To enable effective performance analysis, tractable and closed-form expressions are derived for probability density function of end-to-end signal to noise ratio, outage probability and bit error rate of the considered system under both weak-to-moderate and moderate-to-strong atmospheric turbulence conditions. The accuracy of the analytical expressions is verified by extensive simulations. Analytical results are then used to study the relationship between the optimal system design and tracking system errors.

Path Planning for Cellular-Connected UAV: A DRL Solution With Quantum-Inspired Experience Replay

In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage of UAV’s adjustable mobility, a UAV navigation approach is formulated to achieve the aforementioned optimization goal. Conventional offline optimization techniques suffer from inefficiency in accomplishing the formulated UAV navigation task due to the practical consideration of local building distribution and directional antenna radiation pattern. Alternatively, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL)-aided solution is proposed to help the UAV find the optimal flying direction within each time slot, and thus the designed trajectory towards the destination can be generated. To help the DRL agent commit a better trade-off between sampling priority and diversity, a novel quantum-inspired experience replay (QiER) framework is proposed, via relating experienced transition’s importance to its associated quantum bit (qubit) and applying Grover iteration based amplitude amplification technique. Compared to several representative DRL-related and non-learning baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results.

Trajectory Optimization and Phase-Shift Design in IRS-Assisted UAV Network for Smart Railway

The recent trend towards the smart transportation system has spurred the development of the smart railway. However, enabling trains with seamless wireless connectivity using the roadside units (RSUs) is extremely challenging, mostly due to the lack of line of sight link. To address this issue, we propose a novel framework that uses intelligent reflecting surfaces (IRS)-enabled unmanned aerial vehicle (UAV) to provide line of sight communication to the smart railway system. First, we formulate the optimization problem where the objective is to maximize the minimum achievable rate of trains by jointly optimizing the trajectory of UAV and the phase-shift of IRS. Due to the non-convex nature of the formulated problem, it is decomposed into two subproblems: IRS phase-shift problem and UAV trajectory optimization problem. Next, a Binary Integer Linear Programming (BILP) and a Soft Actor-Critic (SAC) are constructed in order to solve our decomposed problems. Finally, comprehensive numerical results are provided in order to show the effectiveness of our proposed framework.

Spectrum and Energy Efficiency Optimization in IRS-Assisted UAV Networks

Unmanned aerial vehicles (UAVs) have been widely employed in wireless communications, and the performance can be enhanced with the assistance of intelligent reflecting surface (IRS). However, the finite energy of UAVs greatly limits the endurance and becomes a bottleneck for IRS-UAV communications. In this paper, an integrated IRS-UAV communication scheme is proposed where the IRS is mounted on the UAV to connect the base station and the ground user. We present two schemes to maximize the spectrum efficiency (SE) and the energy efficiency (EE) of the system by jointly optimizing the active beamforming, passive beamforming and UAV trajectory. First, to tackle the SE maximization problem, we divide it into three subproblems to optimize the variables iteratively. For the active and passive beamforming, the closed-form solutions can be directly derived. The suboptimal trajectory design can be obtained by utilizing the successive convex approximation. Furthermore, considering the limited on- broad energy of UAV, a scheme to maximize the EE is proposed. The optimal active beamforming and the passive beamforming can be similarly obtained. For the non-convex fractional programming of trajectory optimization, it can be solved via the Dinkelbach’s method. Numerical results demonstrate that the proposed algorithms are effective for the IRS-UAV networks to maximize the SE and EE, respectively.

Quick Multiband Spectrum Sensing for Delay-Constraint Cognitive UAV Networks

Unmanned aerial vehicles (UAVs) have gained much popularity for a wide variety of applications that do not require human operators, but also face the spectrum scarcity problem. To address this problem, a cognitive UAV network (CUAVN) framework based on cognitive radio (CR) technology is formulated in this article. On the basis of this, the sequential probability ratio test (SPRT) is used as a fundamental cooperative spectrum sensing (CSS) scheme to significantly reduce the average number of samples, but the uncertainty of its detection delay has a negative impact on the spectrum-sensing performance and the achievable throughput of CUAVNs, especially in the multiband spectrum sensing. Considering the location flexibility and uneven distribution density of UAVs, we design an intraframe CSS structure, with aim of achieving CSS between microsensing slots. Furthermore, a sequential maximal truncation (SMT) is proposed to realize a quick multiband spectrum sensing for delay-constraint CUAVNs. Compared to uniform tail truncation (UTT), simulation results show that the proposed quick multiband spectrum sensing with the help of SMT requires fewer sensing times in support of better sensing performance and achievable throughput, especially under a strict delay constraint.

The Role of Drones in Out-of-Hospital Cardiac Arrest: A Scoping Review.

Drones may be able to deliver automated external defibrillators (AEDs) directly to bystanders of out-of-hospital cardiac arrest (OHCA) events, improving survival outcomes by facilitating early defibrillation. We aimed to provide an overview of the available literature on the role and impact of drones in AED delivery in OHCA. We conducted this scoping review using the PRISMA-ScR and Arksey and O’Malley framework, and systematically searched five bibliographical databases (Medline, EMBASE, Cochrane CENTRAL, PsychInfo and Scopus) from inception until 28 February 2022. After excluding duplicate articles, title/abstract screening followed by full text review was conducted by three independent authors. Data from the included articles were abstracted and analysed, with a focus on potential time savings of drone networks in delivering AEDs in OHCA, and factors that influence its implementation. Out of the 26 included studies, 23 conducted simulations or physical trials to optimise drone network configuration and evaluate time savings from drone delivery of AEDs, compared to the current emergency medical services (EMS), along with 1 prospective trial conducted in Sweden and 2 qualitative studies. Improvements in response times varied across the studies, with greater time savings in rural areas. However, emergency call to AED attachment time was not reduced in the sole prospective study and a South Korean study that accounted for weather and topography. With growing interest in drones and their potential use in AED delivery spurring new research in the field, our included studies demonstrate the potential advantages of unmanned aerial vehicle (UAV) network implementation in controlled environments to deliver AEDs faster than current EMS. However, for these time savings to translate to reduced times to defibrillation and improvement in OHCA outcomes, careful evaluation and addressing of real-world delays, challenges, and barriers to drone use in AED delivery is required.

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET.

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes.

Connecting flying backhauls of unmanned aerial vehicles to enhance vehicular networks with fixed 5G NR infrastructure

This paper investigates moving networks of Unmanned Aerial Vehicles to extend connectivity and guarantee data rates in the 5G by analysing possible hovering locations based on limitations such as flight time and coverage. The authors provide analytic bounds on the requirements in terms of connectivity extension for vehicular networks served by fixed Enhanced Mobile BroadBand infrastructure, where both vehicular networks and infrastructures are modelled using stochastic and fractal geometry as a model for urban environment. The authors prove that assuming n mobile nodes (distributed according to a hyperfractal distribution of dimension dF) and an average of ρ Next Generation NodeB (gNBs), distributed like a hyperfractal of dimension dr if ρ = nθ with θ > dr/4 and letting n tending to infinity (to reflect megalopolis cities), then the average fraction of mobile nodes not covered by a gNB tends to zero like O n − d F − 2 d r 2 θ − d r 2 $O\left({n}^{-\frac{\left({d}_{F}-2\right)}{{d}_{r}}\left(2\theta -\frac{{d}_{r}}{2}\right)}\right)$ . Interestingly, the authors prove that the average number of drones, needed to connect each mobile node not covered by gNBs, is comparable to the number of isolated mobile nodes. The authors complete the characterisation by proving that when θ < dr/4 the proportion of covered mobile nodes tends to zero. The authors offer insights on the placement of the ‘garage of drones’, the home location of these nomadic infrastructure nodes, to minimise the ‘flight-to-coverage time’. The authors provide a fast procedure to select the relays that will be garages (and store drones) in order to minimise the number of garages and minimise the delay. The authors’ analytical results are confirmed using simulations in Matlab.

A Lightweight Authentication Scheme for a Network of Unmanned Aerial Vehicles (UAVs) by Using Physical Unclonable Functions

A network of agents constituted of multiple unmanned aerial vehicles (UAVs) is emerging as a promising technology with myriad applications in the military, public, and civil domains. UAVs’ power, memory, and size constraints, ultra-mobile nature, and non-trusted operational environments make them susceptible to various attacks, including physical capturing and cloning attacks. A robust and resilient security protocol should be lightweight and resource-efficient in addition to providing protection against physical and tampering threats. This paper proposes an authentication protocol for a UAV-based multi-agent system robust against various threats and adversaries, including strong resistance against cloning and physical attacks. The proposed protocol is based on a physical unclonable function (PUF), a well-known hardware security primitive that is utilized for low-cost authentication and cryptographic key generation. The analysis of the proposed approach shows that it provides strong protection against various attacks, including tampering and cloning, and exhibits scalability and energy efficiency.

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.

An Information-Entropy-Based Hierarchical Serialization Allocation Method for UAV Tracking in 6G Networks

Unmanned aerial vehicles (UAVs) play an important role in future 6G networks, which can be used to assist cellular networks in setting up temporary networks to provide communication services when network access demand is intense. It is critical to design a UAV tracking method with high efficiency and high precision under active sensor radiation control to build a reliable network of UAVs. Multisensor cooperative multitarget tracking for UAVs with high accuracy is an alternative solution to meet the performance requirements of 6G. In this paper, an information-entropy-based multisensor to multitarget allocation method under low-radiation control is proposed for UAV tracking. Specifically, the knowledge-map-based demand assessment of integrated track detection is first present; and then, fuzzy-decision-tree-based generation of sensor management plan is given; the next, the basic linear programming model for multitarget to multi-UAV allocation is utilized based on cooperative tracking under radiation control; finally, extensive experiments are conduct, and the experimental results show that the proposed method has a good performance.

Covert Communication With Energy Replenishment Constraints in UAV Networks

In this paper, we investigate the possibility and condition of exploiting wireless-powered signals as a cover for conducting covert communications in unmanned aerial vehicle (UAV) networks. Specifically, we aim to design the hovering altitude of a UAV and its prior transmission probability to strike a balance between energy replenishment and covert communications. We derive the necessary condition for guaranteeing communication covertness subject to an energy replenishment constraint, under which the UAV’s optimal hovering and the prior transmission probability are determined. Our simulation shows that varying the prior transmission probability can significantly improve the achievable communication covertness under the energy replenishment constraint, relative to a baseline scheme with a fixed prior transmission probability.

Two-layered blockchain-based admission control for secure UAV networks

The frequent replacement requirement of UAVs for recharging outputs an extreme number of messaging for admission control of end-users. There are many studies that try to optimize the network capacity in an energy-efficient manner. However, they do not consider the security of data and control channels, which is the urgent requirement of 5G. Blockchain handles secure systems. However, the high numbered transactions in blockchain may cause bottlenecks while considering computational delay and throughput of end-user. In UAVs, a high percentage of battery is consumed for computational tasks instead of communication tasks. Therefore, to handle security by considering the computational needs, this paper proposes a novel two-layered blockchain-based admission control in UAV networks. It has two layers named as side-chain for UAVs and off-chain for end-users building in a decentralized manner. In the former one, the data bearer of end-users is protected by the side-chain of each transaction which is signed by admitted end-users and UAVs. Here, each transaction includes a unique transaction id, nonce value, IMSI of the user, RNTI of UAV, signed data, and hash value. In the latter one, the off-chain is built by signing admission contracts between users in a decentralized manner and the Merkle tree is constructed for the off-chain where the root hash is only stored in a side-chain. Then, the number of transactions that should be validated is extremely decreased. Moreover, PoS is used for the validation of transactions where the validator is selected randomly instead of the competition requirement as in PoW. A novel blockchain-based admission control algorithm isolates readmission control of end-users to 5G core-side during UAV replacement. According to performance evaluation, the proposed approach serves end-users by 64.2% better QoS than the conventional one during UAV replacement. Moreover, it keeps edge delay in acceptable range by using Merkle tree and PoS.

Packet Routing in Dynamic Multi-Hop UAV Relay Network: A Multi-Agent Learning Approach

The multi-hop unmanned aerial vehicle (UAV) network can serve as data relays where ground users (GUs) do not have reliable direct connections to the base station (BS). Existing works mainly focus on simple dual-hop system. In this paper, we investigate the packet routing problem in a multi-hop UAV relay network to minimize the data transmission time and enhance the network throughput. However, the dynamic network topology due to UAV mobility makes the packet routing challenging since the limited communication range of each UAV leads to volatile wireless connection. Moreover, the line-of-sight communication links may cause strong interference among UAVs. Towards this end, we propose a novel multi-agent deep reinforcement learning based algorithm, named as multi-agent QMIX (MAQMIX) to: 1) design proper UAVs’ trajectories to serve the moving GUs while maintaining the network connection; 2) allocate frequency resource properly among UAVs to alleviate the impact of interference; and 3) choose a proper next hop UAV for each data packet to reduce the transmission time and probability of network congestion. The proposed MAQMIX has two novel training mechanisms, i.e., intra-UAV and inter-UAV training mechanisms, which can tackle the large action space issue and coordinate the training among UAVs in the multi-hop UAV relay network. Simulation results demonstrate that the MAQMIX outperforms baseline schemes in terms of the network congestion avoidance, throughput, and transmission time.