In this paper, we design a mathematical model for performance and reliability evaluation of the IEEE 802.11p Enhanced Distributed Channel Access (EDCA) broadcast scheme in Dedicated Short-Range Communication (DSRC) with the presence of hidden terminals. Specifically, we first introduce a more accurate semi-Markov process (SMP) model to portray the channel contention among multiple types of safety messages and their backoff behavior in DSRC based vehicular ad hoc networks (VANETs) with the influence of hidden terminals. Each type of safety message’s generation and service in an individual vehicular node is modeled leveraging a unique M/G/1/K queue. For the channel contention, the SMP model interrelates with the M/G/1/K queue via fixed-point iteration. Additionally, grounded on the solution of fixed-point iteration, we acquire the performance indices such as packet delay (PD), packet delivery rate (PDR), and packet reception rate (PRR). The new SMP model considers the IEEE 802.11p EDCA backoff counter process, unsaturated packet arrivals, limited MAC queue length, hidden terminals, Nakagami-m fading channel with distance-related path loss, and distinct transmission, carrier sensing and interference ranges. Eventually, we validate the correctness of the model through the comparison between the numerical and simulation results under different network parameters and prove that the proposed model has an advantage over the existing models in analyzing the impact of hidden terminals on PDR and PRR.

Vehicle-to-vehicle (V2V) communication empowers vehicles to share information by broadcasting basic and critical safety messages. Dedicated short-range communication (DSRC), the medium access control (MAC) layer of which utilizes the IEEE 802.11p protocol, is a promising candidate technology for vehicular communication. Safety applications usually demand safety message dissemination to be prompt and reliable. To satisfy these strict requirements, the MAC layer of vehicular safety communication tends to adopt Distributed Coordination Function (DCF) or single-class Enhanced Distributed Channel Access (EDCA) without request-to-send/clear-to-send (RTS/CTS), acknowledgment (ACK) and retransmission mechanisms as the access scheme. As far as we know, although many numerical models have been provided to understand the IEEE 802.11 DCF performance, there is no precise model that examines the performance of vehicular safety communication exploiting such an access scheme in imperfect channels with different incoming traffic loads. In this paper, we settle this problem by developing an analytical model where the impacts of various incoming traffic loads, packet length distribution, hidden terminal effects, node mobility, the MAC layer queuing system, and the faulty radio channels are all included which no one has done this before. The experimental and numerical results reveal that the constructed model can exactly forecast the vehicular network performance of packet delay, delivery rate, and reception rate under different traffic and channel circumstances.

With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%.

Platooning, which is enabled by vehicle-to-vehicle (V2V) communication, is one of the most potential frameworks in the intelligent transport system (ITS) to enhance driving safety and improve traffic capacity. In a platoon, vehicles interact with each other by broadcasting beacons via Dedicated Short Range Communication (DSRC). In this work, we explore the impact of beacon transmission rate allocation on the network utility which involves not only network performance but also traffic safety and efficiency for the vehicular ad hoc network (VANET) composing of a single platoon. An optimization problem aiming at searching for an optimal beacon transmission rate allocation for platoon management is developed based on a network utility maximization framework. Particularly, adopting a synchronized P-persistent repetition (SPR) medium access control (MAC) protocol, an optimal beacon transmission rate allocation to achieve the network utility maximization, is obtained for a platoon at a certain cruise velocity. In the simulation, the correctness of the proposed approach is validated, and its advantages over the benchmark are demonstrated by comparisons.

In this study, a new semi-Markov process (SMP)-based model is devised to evaluate the IEEE 802.11p enhanced distributed channel access (EDCA) broadcast performance for vehicular safety communication. Differing from the existing SMP analytical models, the proposed model takes the virtual collision among various prioritized access categories (ACs) inside each vehicle into consideration. Moreover, in contrast to the Markov chain-based models, our model is simpler but with approximate accuracy. Concretely, we first capture the behavior of each AC’s backoff entity using SMP. Then, the parameters of interest in the vehicular ad hoc network (VANET) such as packet transmission probability, conditional collision probability, and saturation throughput are derived. Finally, via MATLAB simulations, we demonstrate that the newly developed model achieves comparable accuracy in calculating these output parameters while its complexity and computation time is around one-tenth of that of the Markov chain-based models. Therefore, the proposed model is more suitable for real-time performance analysis of IEEE 802.11p EDCA safety communication in a freeway scenario.

In this paper, a new semi-Markov process (SMP) based model is devised to evaluate the IEEE 802.11p enhanced distributed channel access (EDCA) broadcast performance for vehicular safety communication. Differing from the existing SMP analytical models, the proposed model takes the virtual collision among various prioritized access categories (ACs) inside each vehicle into consideration. Moreover, in contrast to the Markov chain-based models, our model is simpler but with approximate accuracy. Concretely, we first capture the behavior of each AC’s backoff entity using SMP. Then, the parameters of interest in the vehicular ad hoc network (VANET) such as packet transmission probability, conditional collision probability, and saturation throughput are derived. Finally, via MATLAB simulations, we demonstrate that the newly developed model achieves comparable accuracy in calculating these output parameters while its complexity and computation time is around onetenth of that with the Markov chain-based models. Therefore, the proposed model is more suitable for real-time performance analysis of IEEE 802.11p EDCA safety communication in a freeway scenario.