## I Introduction

Besides artificial intelligence

[Xiang.2020], the fleet management of mobile machines is the principal research direction of the internet of things in the fields of mobile working machinery. Currently, the mobile machines are distributed sparsely in the working site and working at low transport speed to avoid a collision. With the vehicle-to-everything(V2X), we envision that the mobile machines can work more densely with each other and transport the material much quicker since the collision is impossible with sensible communication. The most challenging and research-worthy use case can be described as the task of repairing the highway. During the road is repaired, congestion of traffic is usually expected. According to the study from Triantis, traffic congestion causes significant economic losses [Triantis.2011]. Apparently, by investing more machines with the help of V2X technology in a particular site can surely improve the working productivity, so that the economy lost due to the congestion can be diminished. However, the challenging thing is the congested passenger cars also occupy the channel load; thus, the possibility of the packet collision as well as the packet loss will increase. In this paper, we first evaluate the performance of the IEEE 802.11p standard for varying node density rates by means of simulations using [Henderson.2008]. After that, we propose an analytical model based on the simulation results for mobile machines to predict the mean delay and probability of package loss of the transmit since the simulation model is computationally expensive.## Ii Why we use the IEEE 802.11p?

Despite the fact that LTE has a series of advantages, we would like to adopt the IEEE 802.11p as our first version for connected mobile machines due to the following reasons. First of all, to fully make the advantages of C-V2X, mobile machines need a base station nearby, which varies from 10m until 10km[Siomina.2006]. However, for the fleet of mobile machines that are working far away from urban, they might fail to find a base station nearby. Moreover, the usage of 802.11p is free of charge. Different from the cellular network which the users must pay for the service from the network operators, the 5.9 GHz band is a free but licensed spectrum [Jiang.2008]. In addition, IEEE 802.11p is well designed for the vehicle industry so that no additional modification is needed for vehicle onboard ECU [Bazzi.2017]. Thus, the compatibility of IEEE 802.11p is better for the mobile machines designed without the consideration of V2X. Usually, mobile machines drive at a relatively lower speed, and the communication between cars and mobile machines is not essential; thus, the lack of ability to deal with vehicle mobility by IEEE 802.11p can be ignored, based on the analysis of Alasmary’s study [Alasmary.2012]. Although there have no consensus about which wireless technology is the more promising technology, scientists from both sides agree that the combination of LTE and 802.11p have a certain improvement in performance compared to if only one technology is used [Bazzi.2017, Chen.2016, Mir.2014, Viriyasitavat.2015]. Thus, we would like to use IEEE 802.11p as the communication technology for our initial version fleet management. Even though the passenger car industry adopts cellular technology in the future, the idea of using IEEE 802.11p for mobile machines is still sensible, because the congestion of the channel is consequently alleviated.

## Iii Modelling

Mecklenbräuker has shown the common scenarios in their paper [Mecklenbrauker.2011]. Unfortunately, for mobile machines that have the task to repair the highway, the scenario does not belong to these common scenarios. Firstly, there has usually no buildings around the working site, but the traffic is congested. Secondly, instead evaluate the communication among all the participants in the ad-hoc network, only communication among mobile machines is essential.

### Iii-a Propagation model

In [Stoffers.201287201289], a comparative analysis between different propagation models is performed. Based on Stoffer’s study, there is no best model for all cases, and the users should select the model depending on the concrete environment. Because we are mainly interested in MAC performance and the highway is more similar to an urban scenario, we used a log-distance path loss model proposed by [Erceg.1999]. It is denoted as

(1) |

where PL (d0) is defined as the path loss at the reference distance(d0), and PL(d0) = 46.6777dB. n refers to the path loss distance exponent varying from the propagation environment, and n = 3.

Since the single factor that influences receive power is the distance from the transmitter, in the following simulations, the dynamic mobility model is not applied to vehicles. Still, the relative positions of the vehicles are randomly initialized.

### Iii-B Cooperative Awareness Message(CAM)’s generation model

Venel presented that CAMs are generated at a rate in a range of 2 to 20 packets /second corresponding to multiple factors such as driver’s reaction time and vehicle speed [Vinel.2009]. Thereby, we apply a mean value from them, namely 10 packets/ second(10 Hz). In addition, the length of a packet varies from different applications in real-world vehicular communications. In the following simulations, packet length is set to be 450 bytes, which ensures the necessary information for the safety-related application. Since the generation rate and CAM length are constant throughout the simulation, the channel load is only depended on the number of nodes in the scenario.

### Iii-C Cdma/ca

CSMA/CA algorithm is specified in IEEE 802.11 is to schedule transmissions over a single channel by differing the access attempt with a random back-off time.

Because Physical Layer Convergence Protocol(PLCP) header is modulated with Binary Phase Shift Keying(BPSK) [Tse.2004] and the payload is transmitted in the form of Quadrature Phase Shift Keying(QPSK) modulation, two range are expected: transmission range and sensing range.

Since the primary emphasis of this paper is on the congestion control algorithms at MAC layer and CAM length is constant, the term delay in the following part will always refer to the back-off time between the time point that a node request for channel access and the packet is forwarded from the MAC layer to the PHY layer, neglecting the transmission time depending on packet length and propagation time depending on distance. Table I contains the vital parameters setting that we use.

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

TxPower | 17 | dBm |

Packet length | 450 | Bytes |

Packet generation rate | 10 | Hz |

Channel width | 10 | MHz |

Data rate (BPSK) | 3 | Mbps |

Data rate (QPSK) | 6 | Mbps |

CWmin | 15 | - |

AIFSN | 7 | - |

Time Slot | 13 | µs |

SIFS | 32 | µs |

EIFS | 120 | µs |

In short, the scenario we analyzed is a working site on the highway where the communication performance among mobile machines under the interference from cars nearby.

## Iv Empirical model for fast estimation of ad-hoc network performance

Although can simulate the V2X performance regarding the delay and the probability of lost packet, we still need a quick estimation method, so that onboard ECU can obtain V2X performance in real-time and evaluate the plausibility of V2X data. Therefore, we build an empirical model to fast estimate the network performance based on the results from . Since the contention behavior due to CSMA/CA in corresponding ranges should follow the same roles, which highly depend on the number of neighbors, we introduce the analytical model as follows.

### Iv-a LuT generation

For each Cluster, e.g., the area within transmission range and the area between transmission and sensing range, we generate a Lookup-Table(LuT) in advance, which contains a set of crucial performance indicators in relationship with varying number of neighbors. To reduce the effect of randomness, we average the indicators from a large number of CAM transmissions.

To generate LuT for 1 cluster, we execute the following simulations. The neighbors are located at the same position with 60 meters away from the transmitter. The number of neighbors varies from 5 to 200, with a step of 5 in each scenario. Furthermore, for each of the 40 scenarios, 5 simulations are conducted, in which every single node schedules 1000 transmissions. The same simulations are executed for the 2. LuT, only the neighbors are 140m away from the transmitter.

Four metrics of the transmitter are measured, as shown in Fig.1, e.g. collisions probability(), packet delay probability(), packet loss probability(), and mean delay(). The term collision indicates the access attempt occurs during the duration, in which another node is transmitting. Moreover, the access attempt can also be differed due to the on-going AIFS, which follows the previous transmission, even though the channel is idle. Therefore, the percentage of delayed packets is slightly higher than the percentage of collisions. The metrics packet delay probability and mean delay indicate how probable the packet would be delayed due to an access contention, and once delay occurs, what would be the average duration.

### Iv-B Performance estimation

For each on broad unit in the scenario, the number of neighbors located in each of the two Clusters are measured. The analytical result is derived from the sum of two values that are interpolated and extracted from LuTs. Furthermore, the upper limit for an analytical percentage is equal to 1. (

2) and (3) demonstrates this idea,(2) |

(3) |

where is the naive estimation of the performance of the ad hoc using the analytical model, the footnote and denote the estimation in terms of time and probability, respectively. is the node numbers inside of transmission range, is the node numbers inside of sensing range.

## V Validation and calibration

In this section, we first validate the viability of the analytical model and then introduce the correction factor to eliminate the error between the naive LuT and the realistic simulation results.

In the validation simulation, the traffic scenario is set to be a 1500m long highway with 3 lanes in each direction. 500 onboard units equipped with 802.11p devices are located static. Congested traffic due to a highway worksite is assumed. The simulation is set up with a total simulation time of 100s, in which the vehicles are randomly distributed on the road.

The delay relevant metrics are simulated and estimated among all onboard units. This is because each transmission has a different channel access time, which is independent of reception. For each onboard unit, the packet loss probability is measured on a random receiver, which is located within its’ 15m range, corresponding to two cooperating mobile machines.

Fig.2 represents the correlation coefficients for each performance metric, which evaluate the strength of the association between simulated and analytical results. For an optimum fitting, the blue dots are supposed to be correctly distributed along the diagonal line, with a correlation coefficient equal to 1. The correlation coefficients for the mean delay, packet delay probability, and packet loss probability are 0.9417, 0.9277 and 0.9167, which manifest a strong correlation and satisfied estimation ability of the analytical model.

To optimize the estimation performance of the proposed analytical model, the term correction factor () is introduced,

(4) |

where are the performance matrix from the simulation and the analytical model regarding the , ,, separately.

Obviously, our goal can be demonstrated as (5)

(5) |

where N denotes the total number of vehicles.

The is shown in the bottom right sub-figure in 2. The three curves from top to bottom indicate the for mean delay, packet delay probability and packet loss probability. The uniform color in the center area indicates that the naive analytical estimation method has stable performance and thus can be adjusted by multiplying appropriate correction factor . Among 3 metrics, packet loss probability is dramatically underestimated and needs a larger . This is because, in the Lut generation scenario, a reception is failed only due to multiple differed access attempts to access the channel simultaneously. However, in the real-time simulation, the transmissions from the hidden nodes cause interference at the receiver. Consequently, the reception is more like to corrupt due to lower SINR.

The correction factor differ in the discontinuous edge of the scenario, where hidden node problem is not obvious. In this case, we introduce another correction matrix. Tab. II records the correction factor in the middle() and the correction factor at the edge(), where the results are calculated based on (5).

middle | edge | |
---|---|---|

Mean delay | 1.0857 | 1.3048 |

Packet delay probability | 0.7516 | 0.9671 |

Packet loss probability | 2.2617 | 2.9121 |

After using the correction factors, the analytical model outputs a very similar result to the simulation model.

## Vi Conclusion

In this paper, we suggest that the IEEE 802.11p is a better solution for the first version of the fleet management of mobile working machines based on the analysis of the ad-hoc network and the cellular network. Moreover, we propose an analytical model to let mobile working machines have a real-time sense of the packet delay probabiliy, mean delay and the probability of packet loss in the ad-hoc network. That is, the machine can estimate how probable its transmission can be delayed, how long its transmission can be delayed and how many packets can be lost in real-time. Thanks to V2X technology, mobile machines can work closer and be driven faster so that the productivity of the working site can be increased dramatically. However, our results also show the applicable conditions of IEEE 802.11p on mobile machines. As the nodes increase, the ad-hoc network may overload. Therefore, in our second version, we are going to publish a V2X solution that combines the IEEE 802.11p and 5G. In that version, machines use the analytical model proposed in this paper to decide when the 5G should be applied. Due to the limit of the pages, we just introduce the core ideas and the results. To find the full implementation, you can find our code on our Github.

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