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As the capabilities of mobile devices and mobile communication techniques expand, vehicular ad hoc networks (VANETs) play an increasingly significant role in transportation and traffic management, particularly in Intelligent Transportation Systems (ITS). However, the rapid topology changes, high vehicle mobility, and frequent link disconnections make routing in VANETs a challenging task, necessitating the development of efficient routing protocols. Moreover, as VANET services provided in our daily lives become more extensive and situations in which they are provided simultaneously to an increasing number of users become more frequent, numerous data delivery optimization algorithms have been researched to address these issues. Alongside this, the research related to Unnamed Aerial Vehicles (UAVs) has also been utilized as a solution to solve the physical data link disconnection problem of VANETs. In this paper, we propose a routing protocol called PSUV (Particle Swarm UAV-VANET). First, we have solved the software-related issues of VANETs by measuring data transmission availability of vehicles existing in the topology and conducting topology optimization by creating a vehicle routing table, utilizing a mathematical value calculation formula that combines Particle Swarm Optimization (PSO) from Swarm Intelligence (SI) and the directional vector information of the vehicle. Furthermore, by utilizing UAVs, we address the physical disconnectivity issues that arise during data transmission in VANETs, thereby minimizing the delay and maximizing the total data rate under realistic conditions. Finally, extensive simulations are conducted to validate the superiority of the proposed protocol in various circumstances in an NS-3 simulation. Simulation results indicate that the proposed protocol solves the challenging issues in realistic VANET with significant improvement.

The development of smart vehicles such as self-driving cars has resulted in an increased demand for content consumption in vehicles. This demand for content, coupled with the improved quality of the content and the expanding resolution of the display, leads to a significant increase in mobile data traffics with larger sizes in content-centric vehicular networks (CCVNs). Since a single RSU cannot fully provide the larger size contents in its communication coverage due to its limited transmission rate, many studies on precaching contents have been actively conducted. By considering only delay-sensitive contents, previous precaching schemes immediately precache contents in the next continuous RSUs on trajectories of vehicles through paying lots of traffics for reducing delays. However, every application content in CCVNs might generally have a tolerable delay time to vehicles according to its characteristics related to service satisfaction. By considering the tolerable delay time, the already stored content on RSUs reached by vehicles within the tolerable delay time can be exploited to download the content without precaching. As a result, precaching of contents can be reduced and thus the traffics can be also reduced. To address this issue, we thus propose a Traffic Optimized Content Precaching (TOCP) scheme based on the tolerable delay time in CCVNs, which minimizes traffics consumed for delay-tolerant contents and decreases delays for delay-sensitive contents. To do this, we first provide a numerical model to judge the precaching necessity by calculating the content provision possibility. Next, we build a Delay-tolerant content Management Module (DMM) for managing the updated information and design new packet formats for reducing the movements of the large-size contents to achieve the optimization purpose of TOCP. Then, we select Optimal Precaching RSUs (OPRs) by solving our optimization problem by using ILP. Last, we provide a process for allowing only OPRs to participate in the content provision. Through simulation results conducted in various environments, we demonstrate that TOCP minimizes the traffic and caching burden while maintaining high reliability in comparison with the previous schemes.

Owing to the increasing capabilities of mobile devices and the development of mobile communication techniques in vehicular ad hoc networks (VANETs), mobile multimedia services have focused on supporting high Quality of Service (QoS) and Quality of Experience (QoE) for the subscribers in video streaming services. In VANETs, high-quality video streaming services aim to provide subscribers with safety and various infotainment applications. Due to the dynamic topology and frequent connectivity changes in moving vehicles, video streaming services require elastic and continuous vehicle information updates to present interactive real-time views of nontrivial road scenarios. The QoS and QoE are affected due to obstacles, re-location tracking, network traffic, and bandwidth factors that occur due to mobility problems and significantly influence the ability of vehicles with mobility to provide video streaming services. To achieve high QoS and QoE of video streaming services in VANETs, this paper proposes a novel protocol named PSOstreaming based on a particle swarm optimization (PSO) which is one of the mainstream and nature-inspired algorithms for swarm intelligence (SI). PSOstreaming calculates the PSQ score, which is the scoring method of the node in the topology in terms of the data communication capability measurement to analyze and optimize topology information in real-road circumstances. In addition, PSOstreaming utilizes a 3D vector mobility prediction algorithm for the mobility prediction method for the vehicles to address the characteristics of VANETs. In the topology, PSOstreaming defines Global PSO members and Local PSO members to disseminate video packets for video-streaming services to the service requester by computational equations. Experimental results indicate that PSOstreaming achieves high-quality video streaming services with a flexible response to dynamic topology changes and a high frame delivery ratio in terms of QoS and QoE.