Roadside unit (RSU) cloud and its vehicle-to-infrastructure (V2I) connectivity can enable various security, entertainment, and shared mobility applications for vehicles in intelligent transportation systems (ITS) through wireless communications. In this article, the deep programmability of software-defined networking (SDN) is employed to dynamically reconfigure network hosting services and their data forwarding information for effectively meeting the basic shared mobility applications’ needs in vehicle ad hoc networks (VANETs). Multipath is also enabled to forward data flow for balancing network links utilization rate and SDN is thus utilized to achieve the minimum cloud delay with the least number of hosts, which can be summarized as a mixed-integer linear programming (MILP) problem. The joint optimization (JO) algorithm is proposed and in contrast to the two single-objective algorithms which are the delay optimization (DO) algorithm and host optimization (HO) algorithm, respectively. Results show that, for the single-threading instance, the JO and DO algorithms are the same in essence. For the multithreading instance, the JO algorithm generally outperforms the two single-objective optimization algorithms, respectively, under given demands. Furthermore, results also demonstrate that the services should be deployed globally in a distributed manner rather than in the centralized manner for achieving the minimized cloud delay in designing an RSU cloud.

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments.

In order to enhance the precision of network traffic prediction for multi-area vehicle networks, this paper proposes a two-tier distributed Software-Defined Vehicular Network (SDVN) architecture equipped with multiple controllers, which is subsequently deployed to manage traffic across regions, thus minimizing communication costs and enabling seamless vehicle movement. We firstly build on control plane placement (CPP) research, focusing on deployment strategies that impact scalability, performance, and fault tolerance. It highlights the importance of hierarchical decision-making with multiple controllers handling varying traffic demands. A comprehensive comparison of various machine learning and deep learning algorithms is then conducted to evaluate their efficacy in forecasting SDVN traffic patterns, which is crucial for system efficiency. Experiments show the proposed architecture’s effectiveness in traffic prediction and management in Shenzhen’s Longhua New District. The study confirms that the SDVN system enhances traffic prediction and management, improving urban mobility and Internet of Vehicles. The paper offers a framework using advanced technologies to address challenges in traffic prediction and management in modern vehicular networks.

The rapid proliferation of heterogeneous applications, from latency-critical video delivery to bandwidth-intensive file transfers, poses increasing challenges for modern communication networks. Traditional traffic engineering approaches often fall short in meeting diverse Quality of Experience (QoE) requirements under such conditions. To overcome these limitations, this study proposes a QoE-driven distribution framework for mixed traffic in Software-Defined Networking (SDN) environments. The framework integrates flow categorization, adaptive path selection, and feedback-based optimization to dynamically allocate resources in alignment with application-level QoE metrics. By prioritizing delay-sensitive flows while ensuring efficient handling of high-volume traffic, the approach achieves balanced performance across heterogeneous service demands. In our 15-RSU Mininet tests under service number = 1 and offered demand = 10 ms, JOGAF attains max end-to-end delays of 415.74 ms, close to the 399.64 ms achieved by DOGA, while reducing the number of active hosts from 5 to 3 compared with DOGA. By contrast, HNOGA exhibits delayed growth of up to 7716.16 ms with 2 working hosts, indicating poorer suitability for latency-sensitive flows. These results indicate that JOGAF achieves near-DOGA latency with substantially lower host activation, offering a practical energy-aware alternative for mixed traffic SDN deployments. Beyond generic communication scenarios, the framework also shows strong potential in Intelligent Transportation Systems (ITS), where SDN-enabled vehicular networks require adaptive, user-centric service quality management. This work highlights the necessity of coupling classical traffic engineering concepts with SDN programmability to address the multifaceted challenges of next-generation networking. Moreover, it establishes a foundation for scalable, adaptive data distribution strategies capable of enhancing user experience while maintaining robustness across dynamic traffic environments.

The connected multi road side unit (RSU) environment can be envisioned as the RSU cloud. In this paper, the Software-Defined Networking (SDN) framework is utilized to dynamically reconfigure the RSU clouds for the mixed traffic flows with energy restrictions, which are composed of five categories of vehicles with distinctive communication demands. An environmentally sustainable SDN data dissemination method for safer and greener transportation solutions is thus proposed, aiming to achieve the lowest overall SDN cloud delay with the least working hosts and minimum energy consumption, which is a mixed integer linear programming problem (MILP). To solve the problem, Joint optimization algorithms with Finite resources (JF) in three hyperparameters versions, JF (DW = 0.3, HW = 0.7), JF (DW = 0.5, HW = 0.5) and JF (DW = 0.7, HW = 0.3), were proposed, which are in contrast with single-objective optimization algorithms, the Host Optimization (H) algorithm, and the Delay optimization (D) algorithm. Results show that JF (DW = 0.3, HW = 0.7) and JF (DW = 0.5, HW = 0.5), when compared with the D algorithm, usually had slightly larger cloud delays, but fewer working hosts and energy consumptions, which has vital significance for enhancing energy efficiency and environmental protection, and shows the superiority of JFs over the D algorithm. Meanwhile, the H algorithm had the least working hosts and fewest energy consumptions under the same conditions, but completely ignored the explosive surge of delay, which is not desirable for most cases of the SDN RSU cloud. Further analysis showed that the larger the network topology of the SDN cloud, the harder it was to find a feasible network configuration. Therefore, when designing an environmentally sustainable SDN RSU cloud for the greener future mobility of intelligent transportation systems, its size should be limited or partitioned into a relatively small topology.