Enhanced Artificial Gorilla Troops Optimizer Based Clustering Protocol for UAV-Assisted Intelligent Vehicular Network

Vehicular networks are envisioned to deliver data transmission services ubiquitously, especially in the upcoming autonomous driving era. Accordingly, the high data traffic load poses a heavy burden to the terrestrial network infrastructure. Unmanned Aerial Vehicles (UAVs) show enormous potential to assist vehicular networks in providing services. In a dense UAV-assisted vehicular network with a large number of users, spectrum sharing is leveraged for alleviating the spectrum scarcity. However, the increasing data traffic still leads to the UAV energy consumption problem. This paper considers a specific network scenario where a UAV transmits its cached content files to vehicular users over UAV-to-vehicle (U2V) links while vehicle-to-vehicle (V2V) links reuse the U2V spectrum for safety-critical message exchanges. To improve the UAV's energy efficiency while guaranteeing users' quality of service (QoS), we jointly optimize content placement, spectrum allocation, co-channel link pairing, and power control, which are the key factors affecting energy efficiency and QoS. The joint optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved by combining Hungarian and DDQN. We perform system performance evaluations, demonstrating that our approach can not only improve the UAV's energy efficiency while satisfying the users' QoS requirements but also increase the timeliness of making decisions.

An unmanned aerial vehicle (UAV), is an aircraft that does not have a human pilot and is controlled either independently by the aircraft's computers or by remote control. Because of their high mobility, FANETs are often used in UAVs because they can disrupt communications as well as network stability due to frequent topology changes. In-depth exploration and implementation of UAV network development, concentrating on diverse topologies such as star, multi-star, and network configurations, we developed the Energy-Efficient Data Transmission Policy for UAV (EEDTP-UAV) model. It emphasizes mobility, energy efficiency, UAV distance control, and road availability in the context of Flying Ad-hoc Network (FANET) technology development. Special attention is given to path optimization techniques and different link types, which are crucial for effective UAV communication. By using a relay-based energy-efficient process, off-grid user connections are optimized according to energy consumption meters, and data is collected cooperatively between the UAV cluster and the mobile synchronous node, providing equal energy usage and efficient data transmission. Due to its flexibility and adaptive nature, the proposed algorithm is well-suited for real-time UAV swarm operations that tackle dynamic path planning, and energy efficient communication methods such as airborne navigation or crowd control. Compared with existing model EEDS, DSSPCA, EEUCH, and ESRD-PDCA models with our proposed EEDTP-UAV model. By the following parameters communication delay, energy efficiency, data success rate, throughput, and routing overhead have been calculated.

Unmanned Aerial Vehicle (UAV) is adapted as a novel unit in upcoming wireless infrastructures wherein UAVs can play various roles in different applications. The UAV is utilized for navigating commands to attain surveillance. However, routing and localization are the major issues that own higher mobility and unstable links. This paper devises a novel technique for initiating secured communication in a UAV network (UAVN). At first, the simulation of the UAVN is done. After that, the data transmission is carried out amongst nodes using a routing path; hence, an optimal routing path is formed using the newly devised Tunicate Swarm Political Optimization (TSPO) algorithm. The newly devised TSPO algorithm integrates the Tunicate Swarm Algorithm and Political Optimizer. In addition, the data communication is done using an evaluation and monitoring agent. In addition, the malicious detection is done by a decision-making agent using the deep residual network (DRN). Here, the newly devised TSPO algorithm is used to train the deep residual. The input parameters considered in the DRN are round trip time, signal strength, packet delivery, packet size, and the number of incoming packets. Once a DRN classifies the attack and genuine users, the defensive agent is employed for attack mitigation. Thus, the attacks in data are mitigated by the defensive agents. The newly devised TSPO-based DRN obtained enhanced performance with the smallest delay of 0.0014 s, the highest detection rate of 0.9325%, and the highest packet delivery ratio of 11491.2%.

Efficient content distribution is a critical challenge in vehicular networks (VANETs). This is due to the characteristics of vehicular networks, such as high mobility, dynamic topologies, short-lived links and intermittent connectivity between vehicles. Recently, information-centric networking (ICN) has been proposed to VANET scenarios for improving content delivery of infotainmentapplications. However, ICN in VANETs suffers from the Interest transmission broadcast problem, which results in a waste of resources and diminishes the performance of VANETs' applications. In this paper, we propose the link stability-based Interest forwarding for content request (LISIC) protocol, in order to tackle the Interest broadcast storm problem during a content search in information-centric VANETs. The proposed protocol controls Interesttransmission by prioritizing neighboring vehicles with more stable links with the current sender. Simulation results show that the proposed protocol improves the content delivery rate by 40% while decreases the Interest packet transmissions by 26%, in scenario of a low number of content producers in the network.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs' trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

Unmanned aerial vehicle is one of the main announced use cases of 5G/IMT2020, which is expected to have various applications in many fields. These devices have limited capabilities in terms of energy and processing. Due to the complex structure of unmanned aerial vehicle networks and the high mobility constraints, design of efficient routing protocol, for supporting such network, is a challenge. Thus, efficient routing of data among unmanned aerial vehicles between source and destination is an important issue in designing unmanned aerial vehicle networks. Proactive routing protocols are one of the main categories of routing protocols developed for mobile ad hoc networks and vehicular ad hoc networks. Optimized link state routing protocol is one of the most common proactive routing protocols that has been modified to support unmanned aerial vehicle networks, considering high mobility feature of the network. In this work, we propose a latency and energy-efficient proactive routing protocol for dense unmanned aerial vehicle networks, with high-density devices, based on optimized link state routing protocol algorithm, referred to as multi-objective optimized link state routing protocol. The proposed routing protocol is topology aware and can be used for low-latency and high-mobility applications. The proposed multi-objective optimized link state routing protocol routing algorithm considers all modified versions of optimized link state routing protocol and introduces a novel method for selecting multipoint relay nodes that considers the traffic load on the communication channel and the load on each unmanned aerial vehicle node. Moreover, the proposed algorithm considers the communication link stability and the energy constraints. The system is simulated over a reliable environment for various scenarios, and it is compared to the original optimized link state routing protocol and its modified versions. Simulation results indicate that the proposed protocol achieves higher efficiency in terms of latency, energy, and reliability.

Connectivity and collision constrained opportunistic routing for emergency communication using UAV

Adaptive cooperative communications for enhancing QoS in vehicular networks

Smart Access Points on the road for online gaming in vehicular networks

A Survivable Communication Game based Approach for a network of cooperative UAVs

Energy efficiency maximization in UAV communication networks with nonlinear energy harvesting

Unmanned Aerial Vehicles (UAVs) have gained great attention in Internet-of-Things (IoT) applications benefiting from the flexibility of deployment and line-of-sight (LoS) channel conditions. In this paper, we study a UAV-assisted Mobile Edge Computing (MEC) system for providing services to large-scale IoT nodes (INs). In the considered system, the UAV acts as an Aerial Base Station (ABS) that can selectively access large-scale INs to enable efficient data collection and computational offloading while ensuring data integrity. Specifically, we first derive the reconstruction error upper bound based on Graph Laplacian Regularization (GLR) as the data integrity metric. Considering that the UAV is usually limited in energy consumption, we propose an energy efficiency (EE) maximization problem that jointly optimizes the selection of INs, the scheduling of INs, the 3D flight and the computational resource allocation of the UAV, subject to constraints related to UAV motion, resources and data integrity. Due to the non-convex nature of the considered problem, a two-stage algorithm called GDA-3DNACRA is proposed, which adopts Gershgorin Disk Alignment (GDA), Convex Relaxation, and Successive Convex Approximation (SCA) for solving it efficiently. Simulation results have shown that the proposed approach can significantly improve the EE of the UAV while ensuring the data integrity.

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.

In recent years, unmanned aerial vehicles (UAVs) have gained popularity for various applications and services in both the military and civilian domains. Multiple UAVs can carry out complex tasks efficiently when they are organized as an ad hoc network, where wireless communication is essential for cooperation and collaboration between UAVs and the ground station. Due to rapid mobility and highly dynamic topology, designing a routing protocol for UAV networks is a challenging task. As the number of UAVs increases, a hierarchical routing called clustering is necessarily required to provide scalability because clustering schemes ensure the basic level of system performance such as throughput, end-to-end delay, and energy efficiency. For approximately a half-decade, several survey articles have been reported on topology-based routing and position-based routing for UAV networks. To the best of the authors’ knowledge, however, there is no survey on cluster-based routing in the literature. In this paper, cluster-based routing protocols for UAV networks are extensively surveyed and qualitatively compared in terms of outstanding features, characteristics, competitive advantages, and limitations. Furthermore, open research issues and challenges on cluster-based routing are discussed.