Evaluation of IEEE 802.11n and IEEE 802.11p based on Vehicle to Vehicle Communications

This Journal is an extension of work originally presented in conference on Human System Interaction (HSI), although the current standard for vehicular communication is the 802.11p, those devices are still very expensive. To achieve our goal we simulated the wave propagation using Win Pro Solutions which include Proman, Wallman software and field test of 802.11n.


Introduction
This section we focus in the properties of propagation of electromagnetic waves, second section related works, third section simulation of the standard that we want to compare and in the final section the results.
Radio propagation is the behavior of electromagnetic waves in the radio spectrum when propagating, from one point to another. Like light waves, radio waves are affected by the phenomena of reflection, refraction, diffraction, absorption, polarization and dispersion and interference. Understanding the effects of different conditions on the propagation of the radio has many practical applications such as: frequency selection, power, type of modulation, type of antenna, among others. To simulate how electromagnetic waves propagate the WinProp software was used, this software includes a set of tools for simulation in the domain of wireless propagation and radio network planning. To solve this problem, the Friis equation was used to calculate the power received from an antenna, when it is transmitted from another antenna, as follows: Where Pr is the power at the receiving antenna, Pt is the transmitted power, Gt is the gain in the transmitted antenna, Gr is the gain in the received antenna, R is the distance from the transmitted antenna to the received antenna, λ is the wavelength. This is the equation used by the PROMAN software to calculate the behavior of the propagation of the different wireless systems.
in a controlled or not environment. To do this, the author uses the Network Simulator (NS2). In that sense, Authors in [10] proposed to use the optimized network engineering tools (OPNET) simulator to introduce a modeling and solution of a large scale vehicle communication problem.
The main difference of our journal is that in most of the cases investigation for vehicular communication where based using 802.11a standard that is no longer being used. We also focus our investigation in the possibility to use 802.11n for V2V because the 802.11p standard hardware are too expensive.

Simulation Analysis
To mesure the cause and effect between the urban environment and the V2V/V2I communication model a performance evaluation between both IEEE 802.11n and IEEE 802.11p standards were developed. The statistical measurement analysis was conducted at the Causeway Islands, Amador, Panamá, see Figure 1. To do that a database of the urban scenario was created using WinProp software. The 3D model of the urban environment was edited and generated using WallMan software, while to predict the propagation the ProMan software was used. The electrical specifications of the Raspberry Pi that has been used in the simulation process are shown in Table I and Table II. Note: FCC ID Web [11]

Image Reference
To get the reference image to be used in the IEEE 802.11n and IEEE 802.22p simulation task the satellite image of the following places located in Panama City were used: Amador and Via España. Google Earth Maps was used to get the satellite imagery and geospatial information of the selected areas. The information was added to Wallman Sofware as follows:  Height in meters. Building, trees and objects are limited to a height of 732 meters.  Width and Length in meters. This was done by subtracting the UTM coordinates, in case of the width from the East to the West, to extract the length the coordinates from the North to the South were subtracted.  Finally, the process was repeated for all those areas within the image were the propagation model should be computed.

Scenery Recreation
In this section we proceed to create the database that will be used in the Proman to determine the propagation of the following way.
 To define the environment information, the indoor database and draw option was selected. It is important to emphasize that to perform the simulation with different urban scenarios the indoor database option should be used. Using this option, objects such as trees, bulk, building, as well as other objects are added into the database.  Due to the absence of 3D maps information of the urban area, the option draw was used to describe the 3D environment data.  Then the catalog material properties for different frequency bands of materials [12].  Once the catalog is imported into the database, the georeferenced image was created by specifying units in meters. This process can also be done by using UTM coordinates (X,Y), however it will cause some difficulties viewing images because pixels are not correctly mapped into the simulator.  Finally, database information of buildings and other objects were added.

Simulation Process
To analyze the effect of the 3D urban environment under the propagation model used in the V2V/V2I communication systems a simulation was developed as follows:  Firstly, select the connection to the database that it was created in the previous section. To do that, in the scenario section the indoor database was selected while in the indoor 3D section the database previously created was the selected one.  Secondly, the propagation point was added. Here in the simulation option choose the time variant.  Thirdly, the type of transmitter site and antenna were selected considering an omni-directional type which is the Raspberry Pi 3 antenna.  Fourthly, the Raspberry PI 3 transmitter parameters were defined. This task was done using channel 1, the frequency is 2.41GHz and the transmission power is 8 dBm and the antenna 1.5 dBi. The parameters of the WIFI module BCM43438 since both are from the same manufacture, Raspberry does not provide enough information about it.  Finally, at this step the propagation can be computed.
Here, the tasks consist in verifying the radiated power. To do that the test involves measuring the power intensity receive in a notebook using a Raspberry Pi 3 as a transmitter. This process starts by taken measures at every certain distance by measuring the power intensity received in a laptop with a WiFi analyzer. Figure  3 shows the tests from 0.5 to 100 meters that were made to the Raspberry Pi 3. Table III shows the results of this task.

Experimental Results
The results of the comparison is presented in this section. To do that, the error between both IEEE standards were computed using MatLab. Figure 5 and Figure 6 show the expected result between the both IEEE standards, 802.11n and 802.11p respectively. In case of the simulation task it can be observed that at the same power transmission, the more distance increase, the more attenuated the signal is in the IEEE 802.11p. It should be clearly noticed that those differences are basically due to the fact that the 802.11p standards were developed for data quality while the 802.11n was designed for transmission speeds.
The results in Figure 7 have shown that for the practical results (data in blue) there is high attenuation (over 16%) compared with the simulations using the 802.11p and 802.11n standards. Since the vehicle-to-vehicle prototype system is taken into consideration to work with a low data rate, the proposed V2V prototype system is a promising strategy to deal with the problem of vehicle communication. Although there is a drawback of the proposed implementation of the V2V over the IEEE 802.11n standards, the problem of oversaturation on 2.4GHz band due to the presence of devices is compensated with the use of OFDM.  At this point it is important to mention that the 802.11p standard uses a transmission power above 13 dBm but for comparative purposes between both standards we will place the power and gain parameters of the antenna at the same value. The 802.11p standard uses more power as it aims to cover from 300 meters to 1000 meters above all to compensate for the loss in urban spaces.

Implementation:
At present there are different types of vehicle control algorithms, we will limit ourselves to determining the collision time (TTC). The condition of collision can occur when the driver in front suddenly stops or slows downs when the driver behind is going too fast. This type of collisions represents 23% of the collisions that occur on the roads.  • Microcontroller: For this task the Raspberry PI 3 reduced board computer with Linux Rasbian operating system and Python programming language will be used. This will be responsible for processing the information acquired, formatting, sending and receiving information through the network and finally send alarms or alerts to the driver about possible risks.
• Acquisition of data: This task will be performed by the serial receiver of the Global Positioning System (GPS) and the bluetooth reader of the vehicular diagnostic port (OBD II) that will allow us to access the vehicle's data bus (CAN Bus).

• Wireless communication:
The communication will be made using the 802.11n module integrated in the Raspberry Pi 3 in the 2.4GHz band. The network topology will be Ad-Hoc.
 Alerts: the alert system will be used to indicate the distance and time of collision and if the event is a rear or front collision. Figure 9 details the components to be used in the development of our prototype and the way in which everyone interacts and communicates with each other: Figure 9. V2V prototype In order to calculate and predict the collision is important to know each of the factors involves that may cause failure to our algorithm.
Latency due to the transmission of data, acquisition times and information processing are factors of vital relevance for our algorithm since when the data is received it has been some time since it was captured. Table IV of times involved to be considered in the calculation of collision time. T0: Time when the vehicle data is available in the electronic systems of the vehicle. T1: Data capture of CAN bus and time at which the GPS position information is updated. This is the real time the event was detected, and it will be the information that will be sent T2: Time taken by the message to be processed and sent to the wireless network T3: Time in which the message is received in the access layer (antenna level) in the receiver. T4: Time the decoding of the received message data and it is ready for processing T5: Time in which the processing of the received data is completed. The application can request a driver action, if applicable. T6: Time is that it takes the information processed in giving an alert the driver either sonorously or visually With these 6 times involved we can easily predict the trajectory of the vehicles involved in a possible collision. Figure 10 below shows how the information flows between the vehicles involved in a possible collision Figure 10. Processing time.

Experimental Results V2V communication
In order to verify the results of systems already in operation, the equipment was installed in two vehicles. Figure 11 and Figure  12 show the result of the GPS positions of the X-Trail vehicle and the data received from the Kicks vehicle. Based on the results obtained, we passed the data acquired by a Kalman filter to determine the accuracy of the GPS positions of the path used. Figure 13 and Figure 14 below show the real data in red and the result of the prediction using the Kalman filter.  From the previous figure and the statistical analysis, we can realize that where there is loss of data or information for the Kalman filter it is difficult to make an accurate prediction especially if this path involves curves.

Applications:
Including the final implementation we were looking for with the 802.11n propagation study Graphical material: Including simulation graphics of the propagation environment, graphs of actual tests of the V2V communication system designed, Kalman filter path graphs Tables: including table of