Taşıt ağları için yol kenarı ünitesi yerleştirme uygulaması

Edge computing has become a prominent computing strategy when mobile devices and Internet of Things (IoT) became popular in the last decade and cloud computing could not meet the computational requirements of some of these devices/applications. What edge computing can provide different than cloud computing is low latency in communication, high quality of service, and support for high mobility. Connected and autonomous vehicles scenarios can be considered as an important application field for edge computing as these are the key requirements to implement a vehicular network. In this thesis, we aim to present a solution to one of the high level problems in vehicular networks: efficient RSU placement by addressing network coverage and computational demand. We propose an RSU placement framework for generating RSU placement models based on traffic characteristics of a target area. Our work is different from previous studies as they mostly approached this problem from communication aspect and focused on solving a coverage problem without considering computational requirements. Other research addressing computational requirements in vehicular networks propose solutions for low level challenges such as resource allocation. The proposed framework in this study can be used by infrastructure providers for designing an efficient RSU placement while building a smart city. Moreover, our work includes extending capabilities of a simulation framework designed for edge computing scenarios. Therefore, we can evaluate the performance of the generated models and validate their functionalities by running simulations on this environment.