| Abstract [eng] |
Vehicular Ad-hoc Networks are analogous to Mobile ad-hoc networks but are dedicated to vehicles. There are dedicated short range communication system which have potential of transferring information between various vehicles and road-side infrastructure. They are otherwise called Intelligent Transport Networks which comes under the Intelligent Transport Systems (ITS) otherwise can also be called as Internet of vehicles. These are one-hop and multi-hop communication systems which have applications in various fields. In this study the fast-fading effects which is caused by the speedy changes in phase and amplitude of the received signal in the receiver was research. Currently, VANET network radio signal prediction models such as log-normal shadowing, Nakagami fading, Rayleigh, Rician fading model, etc., does not include the fast-fading effects. In this work, these models were analysed, and a log-normal model was chosen to create a mathematical model that also estimates rapid inhibition. The validation of the developed mathematical model was initially performed on the basis of two scientific papers and later verified and corrected in experiments. The experiments were performed according to three scenarios using a Wi-Fi access point and signal measurement with the help of InSSIDer. InSSIDer is a Wi-Fi network scanner software that graphically and digitally displays signal strength over time. A 2.45 GHz mobile phone public Internet access point with a transmission power of 10 dBm was used as the signal source. The received signal strength obtained from the InSSIDer is used to calculate the pathloss, shadowing and the fast-fading effects. The accuracy is primarily checked by optimising the signal source in the open area and then by placing the signal inside the car after which there is a distortion in the signal strength obtained is observed, the error rate was calculated and the gaussian normal distribution was used on the path loss and the shadowing effect with respect to the pathloss was plotted. The error rate in the measured values were observed at a maximum of 10 % with an average of 3.05% out of the 60 readings performed for the accuracy score experimentally. This error is observed as the experimental error. The model accuracy of 96.95% taking in account the average of the error rate is achieved. Such good accuracy of the model was also confirmed by experiments under three scenarios. It has been found that at short distances up to 50 m, signal fading is affected by both slow and fast fading, and at distances greater than 50 m, slow fading is predominant. So, the model is concluded as such: 𝐿𝑝 = { 𝐿𝑝 (𝑑0 ) + 10𝑛𝑙𝑔 ( 𝑑 𝑑0 ) + 𝑁𝜎 (0, 𝜎) + 𝑁𝐹 (𝛼, 𝜎), 𝑖𝑓 𝑑 ≤ 50 𝑚 𝐿𝑝 (𝑑0 ) + 10𝑛𝑙𝑔 ( 𝑑 𝑑0 ) + 2𝑁𝜎 (0, 𝜎) 𝑖𝑓 𝑑 > 50 𝑚 . |
