Safe Through mmWave in Mist (STMM): Efficient SDVN Architecture for Stable Navigation in Foggy Weather

Heavy rain worldwide, in addition to winter fog, usually causes foggy weather on highways even during the summer season. Our goal is to propose a machine learning-based protocol that ensures safe and stable network paths using software-defined networking (SDN) technology to promote safe driving in foggy weather. Our initial evaluation of the proposed scheme is accomplished by using dedicated short-range communication (DSRC) technology, with a few meters of safety distance between vehicles. Because of this distance, we ensure that vehicles can communicate with each other at high data rates and low latency in adverse weather conditions by using mmWave technology. The global network topology is controlled by the SDN main controller (MC) with the assistance of local controllers (LCs). LCs play a vital role in assisting vehicles in moving safely within their communication range by communicating with the optimal controller (OC) that is selected to minimize the burden of the MC. During low visibility ahead, these controllers send alert messages (AMs) to direct vehicles to slow down and switch lanes. We consider different scenarios to evaluate the performance of both technologies and find that mmWave performs better than DSRC with a better delivery ratio, end-to-end delay (E2ED), and overhead in comparison with reference schemes.