This study addresses the challenges faced by Vehicular Ad-Hoc Networks (VANETs) in intelligent transportation systems (ITS), including high vehicle mobility and dynamic network topologies that affect connectivity and data transmission reliability. We propose an integrated approach that combines Vehicular Clouds (VCs) with IEEE 802.11bd communication technology, modeling the vehicular cloud as an M/G/m queuing system. To align with IEEE 802.11bd's priority structure, we implement a priority scheme that limits task execution by each On-Board Unit (OBU) based on priority class. Additionally, the system enhances resource utilization through task delegation considering distance coverage and priority thresholds. Performance metrics are analyzed under varying traffic conditions, demonstrating the effectiveness of priority-based resource management to meet the evolving demands of ITS.

Vehicular cloud (VC) computing which integrates cloud computing principles with data communication through vehicular ad-hoc networks (VANETs) is the foundation for intelligent transportation systems (ITS). In this article, we propose a VC system where vehicles offload task computation to other vehicles in the vicinity through a threshold-based prioritization scheme, using IEEE 802.11bd communication technology to interconnect the vehicles. We model the VC as an$M/G/m$queuing system with task priority structure aligned to the priority structure of IEEE 802.11bd communications, taking into account distance coverage and priority thresholds that aim to enhance resource utilization across vehicles. Key performance metrics are analyzed under various traffic conditions. Our results demonstrate reliable and efficient priority-based resource management in the cloud network emphasizing the potential of this approach to meet the evolving demands of ITS.

This study investigates the performance of IEEE 802.11bd channel bonding, both with and without fallback, when Next-Generation Vehicle (NGV) devices request and send multiple packets using Transmission Opportunity (TXOP) allocation in a mixed environment with legacy, non-NGV devices that do not support TXOP. To this end, we use probabilistic modeling and Markov chain analysis. Our results show that the effectiveness of TXOP depends on the priority level assigned to each NGV traffic class. Overall, low-priority traffic classes experience marginal improvements, while higher-priority classes benefit moderately from the use of TXOP.

The recent IEEE 802.11bd communication technology allows next-generation vehicle (NGV) devices to make use of wide channels using channel bonding, both with and without fallback, in addition to multiple packet transmission using transmission opportunity (TXOP) allocation. To evaluate its performance in an environment which also contains legacy or non-NGV devices that do not support TXOP allocation, we develop a detailed analytical model through probabilistic modeling and Markov chain analysis. The model includes data rate scaling with distance from the roadside unit (RSU). Performance analysis provided by the model shows that enhanced distributed channel access (EDCA), which is available for both NGV and legacy devices, provides noticeable prioritization of traffic; that TXOP allocation leads to better performance by an amount which depends on the traffic priority level; and that NGV devices that use fallback experience better performance than either legacy devices or NGV devices that do not use fallback.

In this paper, we develop a detailed analytical model of IEEE 802.11bd channel bonding with fallback using an M/G/1 queuing model and discrete time Markov chain model for both Next Generation Vehicle (NGV) devices and older legacy (non-NGV) devices. We compute a number of performance metrics and show that the channel bonding mechanism of IEEE 802.11bd provides clear advantages over existing standards.

In this paper, we investigate the performance of IEEE 802.11bd networks through a discrete Markov chain and E-limited M/G/1 queuing models. We then use the resulting model to compare the performance of Next-Generation Vehicle (NGV) that use channel bonding without fallback and legacy (non-NGV) devices through performance metrics such as successful transmission probability, medium access probability, and access delay.