Sensor networks face many problems that do not arise in other types of networks. Power constraints, limited hardware, decreased reliability, and a typically higher density and number of nodes than found in conventional networks are just a small portion of the problems that have to be considered when developing protocols for use in sensor networks. Simulation is often used to test new protocols that are being developed, as well as to compare old protocols. However, there is always a danger when using simulation in testing: the results are not necessarily going to be accurate or representative. To help overcome this, it is important to possess knowledge of the simulation tools available, along with their associated strengths and weaknesses. The goal of this paper is to aid developers in the selection of an appropriate simulation tool.

Self-driving vehicles have the potential to revolutionize urban mobility by providing sustainable, safe, and convenient transportability. In recent years several companies have identified automation as their major area of research and also are investing a huge amount of their financial resources in automating vehicles. This is the period of time where autonomous vehicles are very close to being capable of transporting us to destinations without the aid of drivers in the very near future. In the current generation, the main focus is to make vehicles more automated to provide a better driving experience. These vehicles are designed to drive without or with little human assistance by sensing it's the environment. This can be achieved by a combination of sensors and processing the data with the help of computer vision technology and machine learning. The vehicle autonomy needs to be conducted with care, keeping in mind the challenges that can be faced during the process. Recognizing the traffic signals, understanding the signs, identifying the lane markings are some of the basic functions that it needs to perform. After gathering all this information, the next task is to understand the predefined protocols and follow them without any fault. This problem can be solved stepwise using some functions from image processing and computer vision technology such as Haar transform, perspective mapping, perspective transformation, canny edge detection, and histogram equalization. This solution is further enhanced by including machine learning, which improves performance with experience, making it more reliable. It should be noted that, although the vehicles promoted by the companies ensure 80% reliability, we are not yet ready to completely adapt to the idea of automated vehicles. This paper hence focuses on the negative of current ideology and makes it reliable enough to pave a way for its immediate implementation. In this paper, the authors have used a microcontroller and a microprocessor, to Arduino uno is used as a microcontroller and Raspberry pi B+ model is used as the microprocessor. To detect the lanes the authors have used image processing using a library called OpenCV. For detecting the traffic signs the authors have used supervised machine learning technique, to capture the images authors have used raspberry pi version 2 cam, using cascade training to classify the positive images from the negative images.

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