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With the development of 5G, the Internet of Vehicles (IoV) evolves to be one important component of the Internet of Things (IoT), where vehicles and public infrastructure communicate with each other through a IEEE 802.11p EDCA mechanism to support four access categories (ACs) to access a channel. Due to the mobility of the vehicles, the network topology is time varying and thus incurs a dynamic network performance. There are many works on the stationary performance of 802.11p EDCA and some on real-time performance, but existing work does not consider real-time performance under extreme highway scenario. In this paper, we consider four ACs defined in the 802.11p EDCA mechanism to evaluate the limit of the real-time network performance in an extreme highway scenario, i.e., all vehicles keep the minimum safety distance between each other. The performance of the model has been demonstrated through simulations. It is found that some ACs can meet real-time requirements while others cannot in the extreme scenario.

Nowadays, IoT networks are deployed to collect various information from surrounding systems through the real-time interaction with environment [

With the development of IoV, effectiveness and safety have become the key factors considered by an intelligent transportation system (ITS) [

One of the characteristics of the IoV is dynamic topology changes. For example, when vehicles are moving on the highway, the movement of the vehicles and the drivers’ decision will cause the network topology to change (not always in a stationary state). Some analytical models have been put forward in existing performance modeling studies of the 802.11p EDCA mechanism in IoV [

As far as we know, most existing works only studied the stationary performance of the 802.11p EDCA mechanism, but the vehicular network is in a high-speed environment, and thus the number of vehicles in the carrier sensing range of each vehicle is changing all the time, which cause the performance of the 802.11p EDCA mechanism to be changed in real time. Therefore, the traditional analysis methods were not suitable for the real situations. In [

The rest of this paper is organized as follows. In Section

In this section, we first review the existing works for the performance analysis of the 802.11 distributed coordination function (DCF) mechanism, which is the basis of the 802.11p EDCA mechanism; next, we review the related works on the performance analysis of the 802.11p EDCA mechanism; and finally, the works about the real-time performance analysis of the 802.11p EDCA mechanism is reviewed.

There are lots of existing modeling approaches to analyze the performance of the 802.11 DCF mechanism. In [

In addition to those works, there are some studies about the analysis of the 802.11p EDCA mechanism. In [

As described above, researches on stationary performance are mature. There are few works study on the real-time performance analysis of the IEEE 802.11p EDCA mechanism. In [

In this section, we first describe the extreme highway scenario; then, the 802.11p EDCA mechanism is reviewed briefly.

In order to find the performance limit of the network, we consider an extreme highway scenario as shown in Figure

Network scenario in highway.

The IEEE 802.11p EDCA mechanism defines four AC queues to support different priorities of services to access a channel [_{min}, maximum backoff window CW_{max}, arbitration interframe space number AIFSN, and the retransmission limit. When a packet arrives at the AC_{m}

The backoff process is described as follows. The contention window size

The access process of 802.11p is shown in Figure

IEEE 802.11p EDCA process.

In this section, we elaborate our model to analyze the real-time performance of the 802.11p EDCA mechanism. We first construct a connectivity metric to denote the connection of vehicles in the network, and then, we develop models to derive the real-time performance of the IEEE 802.11p EDCA mechanism including the mean service time and variance for the target vehicle. The notations used in this paper are summarized in Table

Notations used in the model.

Notation | Definition |
---|---|

Abscissa of vehicle | |

Ordinate of vehicle | |

Location matrix at time | |

Connectivity between vehicle | |

Network hearing topology matrix at time | |

Number of vehicles in the transmission range of vehicle | |

Total number of vehicles | |

Maximum contention window size of AC_{m} | |

Minimum contention window size of AC_{m} | |

Contention window size of AC_{m} | |

Retransmission limit of AC_{m} | |

AIFS differentiation of AC_{m} | |

Date rate | |

Basic rate | |

Propagation delay | |

Average transmission time | |

Internal collision probability of AC_{m} | |

Total transmission probability for vehicle | |

Packet arrival probability of AC_{m} | |

Channel busy probability at time of AC_{m} | |

Stationary probability of AC_{m} | |

Internal transmission probability of AC_{m} | |

PGF of transmission time | |

PGF of the average duration that the backoff counter of AC_{m} | |

PGF of stationary probability of AC_{m} | |

Slot time | |

PGF of service time of AC_{m} | |

Mean of service time | |

Variance of service time | |

The arrival rate of AC_{m} |

There are

If the Euclidean distance of the two vehicles is less than the carrier sensing range, they are considered to be connected with each other, i.e., communicate with each other. Each vehicle can calculate a connectivity metric

In our scenario, vehicles drive on the same lane with the same velocity and drive on different lanes with different velocities. Since the intervehicle distance is related with the velocity of vehicles, the intervehicle distances of vehicles are the same on the same lane and are different on different lanes. Therefore, the connection of the vehicles in the network is changed in real time due to the different velocities and intervehicle distances on different lanes, thus causing the matrix

In this section, we regard access process of the 802.11p EDCA mechanism as the service process and derive the real-time performance of the IEEE 802.11p EDCA mechanism including the mean service time and variance for the target vehicle

We first derive _{H}_{H}

Since the channel may be idle or busy when the backoff counter is decremented by one,

The probability_{m}_{m}

The packet will be transmitted when the backoff counter becomes 0; the internal transmission probability for AC_{m}

According to the transition probability of the Markov chain, the probability can be calculated as

Denote _{m}

As mentioned in the system model, an internal collision occurs when there are more than two ACs in a vehicle transmitting at the same time. In this case, the AC with the highest priority is transmitted successfully. Let

Since the packet transmission is considered to be successful only when there is no internal collision, and the transmission probability of a vehicle is the sum of four ACs, the transmission probability of a vehicle is calculated as

By now, we have found all the relationships between

Assign initial values to four

Bring

Combining relations Equations (

Setting an error bound

Calculating the mean and variance of service time according to Equations (

In this section, we evaluate the network performance in the extreme highway scenario with four lanes. The simulation is conducted on MATLAB R2018a. The distance between contiguous vehicles is set to be the minimum distance according to the 4-second rule. As the speed range of the American highway is from 20 m/s to 30 m/s, we set the speeds of the four lanes to be 20 m/s, 23 m/s, 20 m/s, and 30 m/s, respectively. The total length of the highway is 3000 m and the transmission range is 300 m, the values of 802.11p parameters and scenario description are shown in Table

Parameter values.

Parameters | Value |
---|---|

Highway length | 3000 m |

Lane width | 3.5 m |

Transmission range | 300 m |

Speed range | 20 m/s~30 m/s |

Vehicle length | 3 m |

Header length of physical layer (PHY_{H} | 48 bits |

Header length of MAC layer (MAC_{H} | 112 bits |

Packet length | 200 bits |

Basic rate ( | 1 Mbps |

Date rate ( | 6 Mbps |

Propagation delay ( | 2 |

SIFS | 32 |

Slot time | 13 |

3 | |

7 | |

15 | |

15 | |

7 | |

15 | |

1023 | |

1023 | |

2 | |

3 | |

6 | |

9 |

In order to ensure that the target vehicle is always within the scope of the scenario, we take the fifth vehicle in the second lane as the target vehicle. Each vehicle has four ACs to broadcast packets. Since the vehicle speed of each lane is different, the relative position of the vehicle is time varying, thus causing other vehicles to move in/out of the transmission range of the target vehicle. In this case, the number of vehicles in the carrier sensing range of the target vehicle may change in real time.

As shown in Figure

Number of vehicles.

Figures _{0}, AC_{1}, and AC_{2} is less than 0.01 s, which is the minimum delay to ensure safety in IoV [_{0}, AC_{1}, and AC_{2} can meet the real-time requirements, while AC_{3} cannot meet the requirements.

Mean of service time. (a) Mean of service time of AC_{0}. (b) Mean of service time of AC_{1}. (c) Mean of service time of AC_{2}. (d) Mean of service time of AC_{3}.

Standard deviation of service time. (a) Standard deviation of AC_{0}. (b) Standard deviation of AC_{1}. (c) Standard deviation AC_{2}. (d) Standard deviation AC_{3}.

In this paper, we considered four ACs and proposed models to investigate the limit of the real-time performance of the 802.11p EDCA mechanism in the extreme highway scenario. Specifically, we model the real-time network between vehicles, study the connection between vehicles, calculate the real-time number of vehicles within the carrier sensing range, and then calculate the real-time performance metrics of the 802.11p EDCA mechanism including mean and variance of service time. The simulation result is employed to demonstrate that AC_{0}, AC_{1}, and AC_{2} are able to meet the real-time requirement in the extreme highway scenario but AC_{3} cannot. In the future work, we will study the real-time performance modeling of the 802.11p EDCA mechanism in other scenarios.

The data used to support the findings of this study are included within the article.

The authors declare no conflicts of interest.

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61701197 and 61540063, in part by the Yunnan Natural Science Foundation of China under Grant Nos. 2016FD058 and 2018FD055, in part by the 111 Project under Grant No. B12018, and in part by the Jiangsu Laboratory of Lake Environment Remote Sensing Technologies Open Fund under Grant No. JSLERS-2020-001.