Search Results (20)

Jamming devices introduce unwanted signals into the network to disrupt primary communications. The effectiveness of these jamming signals mainly depends on the number and distribution of the jammers. The impact of clustered jamming has not been investigated previously for an unmanned aerial vehicle (UAV)-assisted cellular-vehicle-to-everything (C-V2X) communications by considering multiple roads in the given region. Also, exploiting three-dimensional (3D) beam-width variations for a millimeter waveband antenna in the presence of jamming for vehicular node (V-N) links has not been evaluated, which influences the UAV-assisted C-V2X system’s performance. The novelty of this paper resides in analyzing the impact of clustered jamming for UAV-assisted C-V2X networks and quantifying the effects of fluctuating antenna 3D beam width on the V-N performance by exploiting millimeter waves. To this end, we derive the analytical expressions for coverage of a typical V-N linked with a line-of-sight (LOS) UAV, non-LOS UAV, macro base station (MBS), and recipient V-N for UAV-assisted C-V2X networks by exploiting beam-width variations in the presence of jammers. The results show network performance in terms of coverage and spectral efficiencies by setting V-Ns equal to 3 km⁻², MBSs equal to 3 km⁻², and UAVs equal to 6 km⁻². The findings indicate that the performance of millimeter waveband UAV-assisted C-V2X communications is decreased by introducing clustered jamming in the given region. Specifically, the coverage performance of the network decreases by 25.5% at −10 dB SIR threshold in the presence of clustered jammers. The performance further declines by increasing variations in the antenna 3D beam width. Therefore, network designers must focus on considering advanced counter-jamming techniques when jamming signals, along with the beam-width fluctuations, are anticipated in vehicular networks. Full article

Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput. Moreover, 6G vehicular communications comprise data-intensive applications such as augmented reality, mixed reality, virtual reality, intelligent transportation, and autonomous vehicles. Since vehicles’ sensors generate immense amount of data, the latency in processing these applications also increases, particularly when the data are not independently identically distributed (non-i.i.d.). Furthermore, when the sensors’ data are heterogeneous in size and distribution, the incoming packets demand substantial computing resources, energy efficiency at the UAV servers and intelligent mechanisms to queue the incoming packets. Due to the limited battery power and coverage range of UAV, the quality of service (QoS) requirements such as coverage rate, UAV flying time, and fairness of vehicle selection are adversely impacted. Controlling the UAV trajectory so that it serves a maximum number of vehicles while maximizing battery power usage is a potential solution to enhance QoS. This paper investigates the system performance and communication disruption between vehicles and UAV due to Doppler effect in the orthogonal time–frequency space (OTFS) modulated channel. Moreover, a low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories. The weighted total energy consumption of a UAV is minimized by jointly optimizing the transmission window ($L_w$), transmit power and UAV trajectory considering Doppler spread. The simulation results reveal that the weighted total energy consumption of the OTFS-based system decreases up to 10% when combined with federated learning to locally process the sensor data at the vehicles and communicate the processed local models to the UAV. The weighted total energy consumption of the proposed federated learning algorithm decreases by 10–15% compared with convex optimization, heuristic, and meta-heuristic algorithms. Full article

Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology. Full article

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading. Full article

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions. Full article

With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs. Full article

Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided. Full article

Jammers emit strong intentional jamming signals aiming to limit or block legitimate communications. The distribution of jammers, whether in non-clustered or clustered form, significantly influences the performance of vehicle-to-everything (V2X) networks. In addition, the fluctuations in the three-dimensional (3D) antenna beam width of unmanned aerial vehicles (UAVs) can exert a substantial impact on the network’s overall performance. This paper introduces a model for UAV-V2X (U-V2X) communications in mm-Wave bands, considering non-clustered and clustered jammers, as well as the varying 3D antenna beam width. The roads are modeled using a Poisson line process, vehicular nodes (VNs) are modeled using a 1D Poisson point process (PPP), and UAVs are modeled using a 3D PPP. The jammers are distributed in two ways: non-clustered and clustered distributions. Moreover, the fluctuations in the 3D antenna beam width follow a normal distribution. To this end, a typical node’s performance in U-V2X communications is evaluated for various network configurations, including the number of UAVs, VNs, roads, jammers, and jammer’s transmission power. The analytical expressions for the outage probability (OP) of VN to VN connection (i.e., V2V), VN to UAV connection (i.e., V2U2V), and an overall connection (i.e., U-V2X), under non-clustered and clustered jamming, along with the fluctuating antenna beam width, are derived. The results revealed that the performance of the U-V2X communications utilizing mm-Waves is significantly degraded with the non-clustered jamming in comparison with the clustered jamming. The fluctuations in the 3D beam width of the UAV antennas further compromise the network’s performance. Thus, accurate modeling of these fluctuations is crucial, particularly in the presence of non-clustered jammers. Furthermore, the system designers should focus on implementing additional anti-jamming countermeasures specifically targeting non-clustered jammers in U-V2X communications. Full article

In the current digital era, where Unmanned Aerial Vehicles (UAVs), Artificial intelligence (AI), and Internet of Everything (IoE) can be well integrated, more global connectivity and automated solutions can be witnessed. This paper aims to develop an intelligent cellular structure design for a UAV wireless communication topology using an AI framework. The proposed AI framework includes Self Organizing Maps (SOMs) and an NN fitting tool that can be simulated using the Graphical User Interface (GUI) toolbox in MATLAB. The proposed framework is validated in a proof-of-concept scenario, where various parameters of link budget and cellular structure design have been tuned to achieve an efficient and optimized automatic design. The obtained results show high levels of adaptable wireless communication predictions without human intervention, which is a noticeable shift from existing work in the literature. Full article

Due to globalization and advances in network technology, the Internet of Vehicles (IoV) with edge computing has gained increasingly more attention over the last few years. The technology provides a new paradigm to design interconnected distributed nodes in Unmanned Aerial Vehicle (UAV)-assisted vehicle networks for communications between vehicles in smart cities. The process hierarchy of the current UAV-assisted networks is also becoming more multifaceted as more vehicles are connected, requiring accessing and exchanging information, performing tasks, and updating information securely. This poses serious issues and limitations to centralized UAV-assisted vehicle networks, directly affecting computing-intensive tasks and data offloading. This paper bridges these gaps by providing a novel, transparent, and secure lifecycle for UAV-assisted distributed vehicle communication using blockchain hyperledger technology. A modular infrastructure for Vehicle-to-Everything (V2X) is designed and ‘B-UV2X’, a blockchain hyperledger fabric-enabled distributed permissioned network-based consortium structure, is proposed. The participating nodes of the vehicle are interconnected with others in the chain of smart cities and exchange different information such as movement, etc., preserving operational logs on the blockchain-enabled immutable ledger. This automates IoV transactions over the proposed UAV-assisted vehicle-enabled consortium network with doppler spread. Thus, for this purpose, there are four different chain codes that are designed and deployed for IoV registration, adding new transactions, updating the ledger, monitoring resource management, and customized multi-consensus of proof-of-work. For lightweight IoV authentication, B-UV2X uses a two-way verification method with the defined hyperledger fabric consensus mechanism. Transaction protection from acquisition to deliverance and storage uses the NuCypher threshold proxy re-encryption mechanism. Simulation results for the proposed B-UV2X show a reduction in network consumption by 12.17% compared to a centralized network system, an increase in security features of up to 9.76%, and a reduction of 7.93% in the computational load for computed log storage. Full article

Connectivity between ground vehicles can be utilized and expanded to include aerial vehicles for coordinated missions. Using Vehicle-to-Everything (V2X) communication technologies, a communication link can be established between Connected and Autonomous vehicles (CAVs) and Unmanned Aerial vehicles (UAVs). Hardware implementation and testing of a ground-to-air communication link are crucial for real-life applications. In this paper, the V2X communication and coordinated mission of a CAV & UAV are presented. Four methods were utilized to establish communication between the hardware and software components, namely Dedicated Short Range communication (DSRC), User Datagram Protocol (UDP), 4G internet-based WebSocket and Transmission Control Protocol (TCP). These communication links were used together for a real-life use case scenario called Quick Clear demonstration. In this scenario, the first aim was to send the accident location information from the CAV to the UAV through DSRC communication. On the UAV side, the wired connection between the DSRC modem and Raspberry Pi companion computer was established through UDP to get the accident location from CAV to the companion computer. Raspberry Pi first connected to a traffic contingency management system (CMP) through TCP to send CAV and UAV location, as well as the accident location, information to the CMP. Raspberry Pi also utilized WebSocket communication to connect to a web server to send photos that were taken by the camera that was mounted on the UAV. The Quick Clear demonstration scenario was tested for both a stationary test and dynamic flight cases. The latency results show satisfactory performance in the data transfer speed between test components with UDP having the least latency. The package drop percentage analysis shows that the DSRC communication showed the best performance among the four methods studied here. All in all, the outcome of this experimentation study shows that this communication structure can be utilized for real-life scenarios for successful implementation. Full article

Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0. Full article

A new era of the fifth-generation (5G) networks is realized to satisfy user demands on higher data rate and massive connectivity for information sharing and utilization. The vertical applications such as vehicle-to-everything (V2X) communications, industrial automation, smart factory, smart farm and smart cities require ultra-fast communications and wide service range. Coverage extension is a key issue to support the required demands on higher performance, but requires an additional deployment of base or relay stations. Therefore, an efficient solution needs to be cost-effective and easy, in order to deploy more stations. An unmanned aerial vehicle (UAV) has been considered as a candidate to overcome these issues because it is much more cost-effective than the ground stations and does not require network or cell replanning, thereby enhancing the network coverage without additional excessive deployment procedures of the existing networks. UAVs will play important roles in 5G and beyond networks assisting as macro base stations, relay stations, small cells, or a moving aggregator. The performance of UAV wireless networks highly depends on the position or the trajectory of UAVs and the resource managements of entire networks. Recently, there have been extensive studies on performance analysis, UAV deployment, UAV trajectory and resource management of UAV wireless networks to achieve the required demands on performance. This paper surveys research conducted for the UAV deployment and trajectory to construct UAV wireless networks for the coverage extension, the throughput improvement and the resource management for different use cases and scenarios, so as to encourage further studies in this area. Full article

A UAV-assisted cellular network can provide ubiquitous links to everything and it is considered to be one of the key technologies for 6G wireless networks. In this paper, we consider an uplink wireless network with a macrobase station (MBS) and cellular users. However, the coverage equality of edge users cannot be guaranteed in scenarios where data service is dense. Specifically, a novel topology of the UAV-assisted wireless network is considered. UAVs are deployed upon the cell edge to serve edge users with poor communication quality. To avoid larger interference caused by users and UAVs in the overlapping area, the locations of these UAVs are modeled as a homogeneous Poisson point process (HPPP) under the Poisson cluster distance constraint (PCDC). In addition, we assume that edge users cluster around each UAV and model their locations as Poisson cluster processes (PCPs). Initially, the Laplace transforms of intra-cluster interference, inter-cluster interference, and other interference are derived. Subsequently, coverage probability and area spectrum efficiency are derived for UAVs and MBS using tools from stochastic geometry. Moreover, the energy efficiency of the system is obtained. Simulation results are examined to validate the accuracy of theoretical analysis and provide insights into the effects of the system parameters as well as useful guidelines for practical system design. Full article

In a fifth generation (5G) vehicular network architecture, several point of access (PoA) types, including both road side units (RSUs) and aerial relay nodes (ARNs), can be leveraged to undertake the service of an increasing number of vehicular users. In such an architecture, the application of efficient resource allocation schemes is indispensable. In this direction, this paper describes a network slicing scheme for 5G vehicular networks that aims to optimize the performance of modern network services. The proposed architecture consists of ground RSUs and unmanned aerial vehicles (UAVs) acting as ARNs enabling the communication between ground vehicular nodes and providing additional communication resources. Both RSUs and ARNs implement the LTE vehicle-to-everything (LTE-V2X) technology, while the position of each ARN is optimized by applying a fuzzy multi-attribute decision-making (fuzzy MADM) technique. With regard to the proposed network architecture, each RSU maintains a local virtual resource pool (LVRP) which contains local RBs (LRBs) and shared RBs (SRBs), while an SDN controller maintains a virtual resource pool (VRP), where the SRBs of the RSUs are stored. In addition, each ARN maintains its own resource blocks (RBs). For users connected to the RSUs, if the remaining RBs of the current RSU can satisfy the predefined threshold value, the LRBs of the RSU are allocated to user services. On the contrary, if the remaining RBs of the current RSU cannot satisfy the threshold, extra RBs from the VRP are allocated to user services. Similarly, for users connected to ARNs, the satisfaction grade of each user service is monitored considering both the QoS and the signal-to-noise plus interference (SINR) factors. If the satisfaction grade is higher than the predefined threshold value, the service requirements can be satisfied by the remaining RBs of the ARN. On the contrary, if the estimated satisfaction grade is lower than the predefined threshold value, the ARN borrows extra RBs from the LVRP of the corresponding RSU to achieve the required satisfaction grade. Performance evaluation shows that the suggested method optimizes the resource allocation and improves the performance of the offered services in terms of throughput, packet transfer delay, jitter and packet loss ratio, since the use of ARNs that obtain optimal positions improves the channel conditions observed from each vehicular user. Full article