# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 1 Introduction  
IoT and 5G technologies promise a massively improved scalable and reliable connectivity. These technologies bring various innovative applications that offer high-quality services and efficient management of infrastructure in urban areas. The development of these technologies turns expectations of smart cities into reality. Their vision is to provide a better life quality for citizens and to enhance city management to react quickly to events and emergencies [1]. A smart city uses different devices, such as sensors, actuators, vehicles, and cameras for its various operations. The devices' deployment and operating compose a diversity of access networks that process automating data collection, provide necessary information used to manage infrastructures and resources efficiently, and meet applications' advanced requirements. This collection is analyzed and exploited in various systems, such as transportation, energy, education, and health systems [2]. With a common goal of enabling these systems to be intelligent, many applications have stringent real-time requirements in exchanging information between apparatus and everything around them. The smart transportation system is mainly based on delay-sensitive applications. However, the high mobility and dynamic topology of the vehicle networks diminish data dissemination and bring more challenges in implementing communication protocols that guarantee efficient and reliable transmission [3], and resource management systems [4]. Moreover, the vehicle networks' heterogeneityand scalability increase the complexity in designing new communication solutions and managing multifaceted technologies. This elevates the demand of create new communication initiatives, more specifically in developing routing protocols. Despite the significant number of existing routing protocols in the literature, we note that current routing protocols are relatively limited and cannot resolve a smart environment's new issues. Furthermore, the existing routing protocols are inherently distributed, since each node communicates with others by broadcasting information in its vicinity. They reflect partial visibility of the network, and they cannot know the real network conditions. There are still needs for novel approaches taken by the researchers in conceiving the original solving and considering significant heterogeneity challenges, scalability and new application requirements. A programmable and dynamic solution named Software-Defined Networking (SDN) was proposed to reverse this situation, where new protocols can be implemented by software without needing any hardware modification on networks. The SDN separates the control and data planes to guarantee flexibility. The data plane includes the devices that forward the information. The control plane comprises a controller that generates and maintains a database containing network-wide knowledge such as network topology. The main aim of SDN is to improve rapid packet delivery, low latency, and overhead communication, best bandwidth utilization and selection of best routes, as well as to facilitate network programming. Recent trends in smart cities reveal that SDN has been used in wired networks [5, 6], making it unusable in the general case of an intelligent environment where the devices are ubiquitous and distributed. Therefore, SDN must be adapted to support the wireless nature of smart environment systems [7, 8]. The intelligent environment complexity and SDN centralized management make the computation of routing solutions under various requirements of real-time applications even more difficult. However, Machine Learning (ML) algorithms afford a new possibility in solving such problems, which keeps computation more reasonable and manageable [9]. In this perspective, we explore using a ML algorithm and SDN technologies in proposing a new routing protocol to reduce end to end delay. We use the useful and practical machine learning technique to discover and extract knowledge for predicting the routing paths calculation.