# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 1 Introduction  
Weather conditions affect driving when people are moving, especially when they need to travel in an emergency situation. Foggy weather in various countries makes it difficult for people to travel safely and reach their destinations on time due to low visibility on the roads. Often, there is no alternative that ensures people will reach their destinations safely and within time. The coordination between vehicles using a periodic exchange of messages to create a safe and stable routing path helps drivers to drive on foggy roads. The issue is that these messages need to be delivered in a shorter time frame with less overhead to prevent accidents and avoid burdening the network with additional overhead. Secondly, it is highly desirable that these messages reach their destination with high success rates. To ensure timely delivery and lower overhead, roadside units (RSUs) are viewed as an effective way to improve the performance of vehicle-to-vehicle (V2V) communication. The literature [1] has already discussed multiple routing schemes that ensure safe and stable routing paths for both V2V and vehicle-to-infrastructure (V2I) communications in clear weather. However, to implement these protocols in foggy weather, we require new technology that enhances network intelligence and offers flexibility, as fog presents itsown challenges regarding loss of signal. To ensure safe movement in adverse weather conditions, we require a software-based controller that handles queries from vehicles and instructs them accordingly. Therefore, our proposed scheme should involve a method that focuses on software-based controllers, and with the consideration of these control messages, it assures timely delivery with minimal overhead during foggy weather. For decades, software-defined networking (SDN) [2] has been considered an emerging technology that enhances vehicular network performance by reducing forwarding delay and network overhead and preventing hop-by-hop flooding during route discovery. The network is viewed globally by the main controller (MC) of SDNs. However, the routing traffic cannot be controlled by a single MC in this dynamic and challenging network. Therefore, there is a need for local controllers (LCs) that manage network traffic at a lower level than the MC. By managing routing traffic at the local level, these LCs assist the MC, sharing its burden. Hence, our priority is to choose these LCs using a decision tree classifier (DTC), since MC and RSUs (a few as LCs) form a tree-based topology in a vehicular network. These LCs also assist vehicles in finding the optimal routing path. Selecting the optimal path in a foggy environment can be a bit of a challenge, but taking advantage of vehicles that move at an almost constant speed during foggy weather as they follow each other across the low visibility road makes it possible. Vehicles typically follow the driver who is familiar with the area they are passing through at that moment. In a nutshell, vehicles move in platoons to traverse the low-visibility area. Our LCs guide these vehicles to cross any foggy areas on the road by sending periodic alert messages (PAMs). We use a dedicated short-range communication (DSRC) band for V2V and V2I communications, as it performs well in foggy weather. Moreover, as our scheme integrates SDN technology into vehicular networks, this integration is named software-defined vehicular network (SDVN) [3]. Hence, our aim is to implement SDVN during foggy weather by introducing a MC that maintains a global network view by obtaining locally collected data from LCs. These MC and LCs form the control layer of the SDN technology. They help vehicles moving in the form of a platoon by instructing them about the current visibility conditions and to reduce speed and change lanes using PAMs. The controllers communicate with each other using LTE technology to maintain stability and high coverage range, while V2V and V2I communications use DSRC technology. The key contributions of this paper are as follows:  
1.