Search Results (20)

Intelligent Connected Vehicles (ICVs) impose stringent requirements on real-time computational services. However, limited onboard resources and the high latency of remote cloud servers restrict traditional solutions. Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC), which deploys computing and storage resources at the network edge, offers a promising solution. In UAV-assisted vehicular networks, jointly optimizing content and service caching, computation offloading, and UAV trajectories to maximize system performance is a critical challenge. This requires balancing system energy consumption and resource allocation fairness while maximizing cache hit rate and minimizing task latency. To this end, we introduce system efficiency as a unified metric, aiming to maximize overall system performance through joint optimization. This metric comprehensively considers cache hit rate, task computation latency, system energy consumption, and resource allocation fairness. The problem involves discrete decisions (caching, offloading) and continuous variables (UAV trajectories), exhibiting high dynamism and non-convexity, making it challenging for traditional optimization methods. Concurrently, existing multi-agent deep reinforcement learning (MADRL) methods often encounter training instability and convergence issues in such dynamic and non-stationary environments. To address these challenges, this paper proposes a MADRL-based joint optimization approach. We precisely model the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and adopt the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, which follows the Centralized Training Decentralized Execution (CTDE) paradigm. Our method aims to maximize system efficiency by achieving a judicious balance among multiple performance metrics, such as cache hit rate, task delay, energy consumption, and fairness. Simulation results demonstrate that, compared to various representative baseline methods, the proposed MAPPO algorithm exhibits significant superiority in achieving higher cumulative rewards and an approximately 82% cache hit rate. Full article

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

With the rapid increase in the number of vehicles and the growing demand for low-latency and reliable communication, traditional vehicular network architectures face numerous challenges. Unmanned Aerial Vehicle (UAV)-assisted vehicular networks provide an innovative solution for real-time data transmission and efficient cross-domain communication, significantly enhancing resource allocation efficiency and traffic safety. However, these networks also raise privacy and security concerns. Traditional symmetric key and Public Key Infrastructure (PKI)-based authentication schemes suffer from issues such as key management, certificate verification, and data leakage risks. While blockchain technology has been explored to address these problems, it still suffers from inefficiencies and high computational overhead. This paper proposes a UAV-assisted vehicular network architecture that leverages UAVs as trusted intermediaries for cross-domain authentication, effectively reducing authentication delays and improving scalability. Through ProVerif security proofs and detailed theoretical analysis, the proposed scheme is demonstrated to meet the security requirements of vehicular networks and withstand a broader range of attacks. Performance evaluation results show that the proposed scheme achieves at least a 20% reduction in computational and communication overhead compared to existing schemes, highlighting its significant advantages. Additionally, the average consensus time for the proposed scheme is at least 40% lower than existing schemes. The novelty of the proposed scheme lies in the integration of UAVs as trusted intermediaries with blockchain technology, addressing key management and privacy issues, and providing an efficient and secure solution for high-density vehicular networks. Full article

The rapid evolution of smart cities relies heavily on advancements in wireless communication systems and extensive IoT networks. This paper offers a comprehensive review of the critical role and future potential of integrating unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance Internet of Vehicles (IoV) systems within beyond-fifth-generation (B5G) and sixth-generation (6G) networks. We explore the combination of quasi-optical millimeter-wave (mmWave) signals with UAV-enabled, RIS-assisted networks and their applications in urban environments. This review covers essential areas such as channel modeling and position-aware beamforming in dynamic networks, including UAVs and IoVs. Moreover, we investigate UAV navigation and control, emphasizing the development of obstacle-free trajectory designs in dense urban areas while meeting kinodynamic and motion constraints. The emerging potential of RIS-equipped UAVs (RISeUAVs) is highlighted, along with their role in supporting IoVs and in mobile edge computing. Optimization techniques, including convex programming methods and machine learning, are explored to tackle complex challenges, with an emphasis on studying computational complexity and feasibility for real-time operations. Additionally, this review highlights the integrated localization and communication strategies to enhance UAV and autonomous ground vehicle operations. This tutorial-style overview offers insights into the technical challenges and innovative solutions of the next-generation wireless networks in smart cities, with a focus on vehicular communications. Finally, future research directions are outlined. Full article

The rapid development of the Internet of Vehicles (IoV) and intelligent transportation systems has led to increased demand for real-time data processing and computation in vehicular networks. To address these needs, this paper proposes a task offloading framework for UAV-assisted Vehicular Edge Computing (VEC) systems, which considers the high mobility of vehicles and the limited coverage and computational capacities of drones. We introduce the Mobility-Aware Vehicular Task Offloading (MAVTO) algorithm, designed to optimize task offloading decisions, manage resource allocation, and predict vehicle positions for seamless offloading. MAVTO leverages container-based virtualization for efficient computation, offering flexibility in resource allocation in multiple offload modes: direct, predictive, and hybrid. Extensive experiments using real-world vehicular data demonstrate that the MAVTO algorithm significantly outperforms other methods in terms of task completion success rate, especially under varying task data volumes and deadlines. Full article

The rapid expansion of the Industrial Internet-of-Things (IIoT) has spurred significant research interest due to the growth of security-aware, vehicular, and time-sensitive applications. Unmanned aerial vehicles (UAVs) are widely deployed within wireless communication systems to establish rapid and reliable links between users and devices, attributed to their high flexibility and maneuverability. Leveraging UAVs provides a promising solution to enhance communication system performance and effectiveness while overcoming the unprecedented challenges of stringent spectrum limitations and demanding data traffic. However, due to the dramatic increase in the number of vehicles and devices in the industrial wireless networks and limitations on UAVs’ battery storage and computing resources, the adoption of energy-efficient techniques is essential to ensure sustainable system implementation and to prolong the lifetime of the network. This paper provides a comprehensive review of various disruptive methodologies for addressing energy-efficient issues in UAV-assisted industrial wireless networks. We begin by introducing the background of recent research areas from different aspects, including security-enhanced industrial networks, industrial vehicular networks, machine learning for industrial communications, and time-sensitive networks. Our review identifies key challenges from an energy efficiency perspective and evaluates relevant techniques, including resource allocation, UAV trajectory design and wireless power transfer (WPT), across various applications and scenarios. This paper thoroughly discusses the features, strengths, weaknesses, and potential of existing works. Finally, we highlight open research issues and gaps and present promising potential directions for future investigation. Full article

As Autonomous Vehicles continue to advance and Intelligent Transportation Systems are implemented globally, vehicular ad hoc networks (VANETs) are increasingly becoming a part of the Internet, creating the Internet of Vehicles (IoV). In an IoV framework, vehicles communicate with each other, roadside units (RSUs), and the surrounding infrastructure, leveraging edge, fog, and cloud computing for diverse tasks. These networks must support dynamic vehicular mobility and meet strict Quality of Service (QoS) requirements, such as ultra-low latency and high throughput. Terrestrial wireless networks often fail to satisfy these needs, which has led to the integration of Unmanned Aerial Vehicles (UAVs) into IoV systems. UAV transceivers provide superior line-of-sight (LOS) connections with vehicles, offering better connectivity than ground-based RSUs and serving as mobile RSUs (mRSUs). UAVs improve IoV performance in several ways, but traditional optimization methods are inadequate for dynamic vehicular environments. As a result, recent studies have been incorporating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into UAV-assisted IoV systems to enhance network performance, particularly in complex areas like resource allocation, routing, and mobility management. This survey paper reviews the latest AI/ML research in UAV-IoV networks, with a focus on resource and trajectory management and routing. It analyzes different AI techniques, their training features, and architectures from various studies; addresses the limitations of AI methods, including the demand for computational resources, availability of real-world data, and the complexity of AI models in UAV-IoV contexts; and considers future research directions in UAV-IoV. Full article

With the rapid development of the Internet of Things, the Internet of Vehicles (IoV) has quickly drawn considerable attention from the public. The cooperative unmanned aerial vehicles (UAVs)-assisted vehicular networks, as a part of IoV, has become an emerging research spot. Due to the significant limitations of the application and service of a single UAV-assisted vehicular networks, efforts have been put into studying the use of multiple UAVs to assist effective vehicular networks. However, simply increasing the number of UAVs can lead to difficulties in information exchange and collisions caused by external interference, thereby affecting the security of the entire cooperation and networking. To address the above problems, multiple UAV cooperative formation is increasingly receiving attention. UAV cooperative formation can not only save energy loss but also achieve synchronous cooperative motion through information communication between UAVs, prevent collisions and other problems between UAVs, and improve task execution efficiency. A multi-UAVs cooperation method based on arithmetic optimization is proposed in this work. Firstly, a complete mechanical model of unmanned maneuvering was obtained by combining acceleration limitations. Secondly, based on the arithmetic sine and cosine optimization algorithm, the mathematical optimizer was used to accelerate the function transfer. Sine and cosine strategies were introduced to achieve a global search and enhance local optimization capabilities. Finally, in obtaining the precise position and direction of multi-UAVs to assist networking, the cooperation method was formed by designing the reference controller through the consistency algorithm. Experimental studies were carried out for the multi-UAVs’ cooperation with the particle model, combined with the quadratic programming problem-solving technique. The results show that the proposed quadrotor dynamic model provides basic data for cooperation position adjusting, and our simplification in the model can reduce the amount of calculations for the feedback and the parameter changes during the cooperation. Moreover, combined with a reference controller, the UAVs achieve the predetermined cooperation by offering improved navigation speed, task execution efficiency, and cooperation accuracy. Our proposed multi-UAVs cooperation method can improve the quality of service significantly on the UAV-assisted vehicular networks. Full article

In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to efficiently connect the trapped vehicle to the base station is the challenge facing the emergency vehicular network. To address this challenge, this study proposes a UAV-assisted multi-objective and multi-hop ad hoc network (UMMVN) that can be used as an emergency vehicular network. Firstly, it presents an integrated design of a search system to find a trapped vehicle, the communication relay, and the networking, which significantly decreases the UAV’s networking time cost. Secondly, it presents a multi-objective search for a trapped vehicle and navigates UAVs along multiple paths to different objectives. Thirdly, it presents an optimal branching node strategy that allows the adequate use of the overlapping paths to multiple targets, which decreases the networking cost within the limited communication and searching range. The numerical experiments illustrate that the UMMVN performs better than other state-of-the-art networking methods. Full article

With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme. Full article

A UAV-assisted caching strategy considering content cache pricing in vehicular networks is proposed to address the problem of high communication load and high backhaul link overhead in vehicular networks. Consider a traffic scenario consisting of a content provider (CP), a network operator (NO), and multiple mobile users, where the NO has a set of cache-enabled roadside units (RSUs) and an unmanned aerial vehicle (UAV). The CP leases some popular contents to the NO for its benefit and the NO places this leased content in its RSU’s local cache to save expensive backhaul transmission overhead and latency. However, both NO and CP are selfish and their interests conflict with each other because they have opposing expectations for content pricing. In order to take into account the interests of both, this paper defines the utilities of CP and MNO and uses the Stackelberg game framework to model the competition between the two entities, where CP acts as a leader and sets the rental price of the content and NO acts as a follower responding to CP’s actions. An iteration-based dynamic programming algorithm is also designed to find the Stackelberg equilibrium. Meanwhile, a caching-capable UAV is introduced into the vehicular network and, based on this, a Dijkstra-based path planning algorithm is designed to further increase the total utility of NO by optimizing the trajectory of the UAV. The simulation results show that the strategy in this paper can reasonably allocate the benefits of CP and NO, reduce the average request delay, and increase the utility of NO; for example, we reduced the request latency for vehicle users by 27% and increased the total utility of NO by 13%. Full article

The increasing demands of several emergent services brought new communication problems to vehicular networks (VNs). It is predicted that the transmission system assimilated with unmanned aerial vehicles (UAVs) fulfills the requirement of next-generation vehicular network. Because of its higher flexible mobility, the UAV-aided vehicular network brings transformative and far-reaching benefits with extremely high data rates; considerably improved security and reliability; massive and hyper-fast wireless access; much greener, smarter, and longer 3D communications coverage. The clustering technique in UAV-aided VN is a difficult process because of the limited energy of UAVs, higher mobility, unstable links, and dynamic topology. Therefore, this study introduced an Enhanced Artificial Gorilla Troops Optimizer–based Clustering Protocol for a UAV-Assisted Intelligent Vehicular Network (EAGTOC-UIVN). The goal of the EAGTOC-UIVN technique lies in the clustering of the nodes in UAV-based VN to achieve maximum lifetime and energy efficiency. In the presented EAGTOC-UIVN technique, the EAGTO algorithm was primarily designed by the use of the circle chaotic mapping technique. Moreover, the EAGTOC-UIVN technique computes a fitness function with the inclusion of multiple parameters. To depict the improved performance of the EAGTOC-UIVN technique, a widespread simulation analysis was performed. The comparison study demonstrated the enhancements of the EAGTOC-UIVN technique over other recent approaches. Full article

Mobile edge computing (MEC) represents an enabling technology for prospective Internet of Vehicles (IoV) networks. However, the complex vehicular propagation environment may hinder computation offloading. To this end, this paper proposes a novel computation offloading framework for IoV and presents an unmanned aerial vehicle (UAV)-aided network architecture. It is considered that the connected vehicles in a IoV ecosystem should fully offload latency-critical computation-intensive tasks to road side units (RSUs) that integrate MEC functionalities. In this regard, a UAV is deployed to serve as an aerial RSU (ARSU) and also operate as an aerial relay to offload part of the tasks to a ground RSU (GRSU). In order to further enhance the end-to-end communication during data offloading, the proposed architecture relies on reconfigurable intelligent surface (RIS) units consisting of arrays of reflecting elements. In particular, a dual-RIS configuration is presented, where each RIS unit serves its nearby network nodes. Since perfect phase estimation or high-precision configuration of the reflection phases is impractical in highly mobile IoV environments, data offloading via RIS units with phase errors is considered. As the efficient energy management of resource-constrained electric vehicles and battery-enabled RSUs is of outmost importance, this paper proposes an optimization approach that intends to minimize the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power constraints, timeslot scheduling, and task allocation. Extensive numerical calculations are carried out to verify the efficacy of the optimized dual-RIS-assisted wireless transmission. Full article

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay. Full article

In this paper, we investigate the relay selection problem for the unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs). For the considered network, we first model and analyze the link quality of service (LQoS) from the source node (SN) to the neighbor node and the node forward capacity (NFC) from the neighbor node to the destination node (DN). Then, the relay selection problem is formulated as a multi-objective optimization problem by jointly considering the LQoS and the NFC. Afterward, we decompose the problem into two subproblems and propose a relay selection protocol with the storage-carry-forward (SCF) method. Moreover, we define a utility function with the node encounter frequency (NEF) and the message time-to-live (TTL) taken into account, based on which a redundant copy-deleting approach is devised. Furthermore, we analyze the security of the designed protocol. Finally, the simulation results demonstrate that the proposed relay selection protocol can improve the message delivery ratio, reduce the average end-to-end delay, and limit the overhead. Full article