# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 3 Related Work
The system also detects accidents, based on vehicle information, and broadcasts necessary alarms to all vehicles. SDN technology has also been utilized to improve routing in IoV environments. [PERSON] et al. [43] introduced road-aware routing based on SDN for IoV networks in smart cities. The introduced routing mechanism improved network performance in terms of end-to-end delay, packet delivery ratio, and routing overhead. [PERSON] et al. [44] introduced an innovative approach to real-time traffic analysis in Vehicular Ad-Hoc Network (VANET) environments based on SDN. The proposed approach relies on the programmability of SDN to implement a combination of different machine learning algorithms to model traffic flow in SDN-enabled smart cities. SDN has been proposed to manage traffic in smart cities in emergency situations, such as natural disasters, traffic accidents, and terrorist attacks. [PERSON] et al. [45] proposed an SDN-based architecture for smart cities that aims to manage traffic efficiency under disaster conditions. In that study, SDN controllers were utilized by the proposed architecture to collect information from different sensory networks in smart cities, such as traffic lights and surveillance cameras. The information collected is combined to obtain the best and fastestevacuation routes and access roads for emergency services units. The authors further tested their proposed architecture in a follow-up study [46]. The results showed that the architecture reduced network delay by 33%. In addition, energy consumption by the nodes in the sensory networks was modeled and evaluated. The results proved the scalability of the architecture, as they demonstrate that energy consumption increases linearly with the number of nodes. Other techniques have been proposed to handle emergency situations in smart cites. [PERSON] et al. [47] proposed a system based on citizens' smartphones to detect emergency situations in smart city environments. The proposed system utilizes data from accelerometers in smartphones to detect changes in user behavior. Different tests were performed using real devices to show the possibility of identifying different behaviors, including walking, running, falling, and remaining on the floor. Also, [PERSON], [PERSON], et al. [48] proposed a multi-agency disaster management framework for unmanned aerial vehicles (UAV)-assisted public safety systems that can be used for smart cities. The framework they introduce exploits the principles of game theory and reinforcement learning to support connectivity during disasters and under challenging communication situations. Furthermore, [PERSON] et al. [49] investigated the use of a machine learning approach to improve network connectivity. The approach addresses the connectivity holes that may exist within a network to achieve undisturbed connectivity. Applications that deal with emergency situations and surveillance applications are critical in smart cities. The availability of these applications relies fundamentally on network resilience. Note that our research targets improving network resilience in the face of natural challenges in order to maintain the availability of these important applications. To that end, related studies that address improving network resilience are reviewed. SDN is also utilized to achieve network resilience in smart cities. [PERSON] and [PERSON]. [50] presented a lightweight SDN architecture for resilient real-time IoT that can be used in smart cities. The architecture achieves resilience by optimizing the regular control plane and data plane to introduce lightweight versions. The control plane was optimized by stopping the two least important control functions based on an application-specific requirement and by reducing the duty cycle of the control plane based on the demand of the data plane. The data plane is optimized by implementing real-time routing protocols, load disruption, and adaptive traffic shaping. The lightweight SDN architecture was evaluated using Mininet-IoT.