## 2 Literature Survey  
This section itemizes the existing literature on SDN, WSN, and SDWSN environments in a brief manner. Furthermore, we have also highlighted the corresponding literature issues in the existing works. In this paper, the author proposed a secure and energy-efficient routing mechanism in the WSN environment [18]. Initially, this work mainly focuses on enhancing network security, thereby minimizing network energy consumption. The nodes trust evaluation scheme was performed using the ant colony optimization algorithm. The proposed model learns the energy consumption in the WSN, and then based on that, the basic ant colony enhances its efficiency to improve the secure routing. Finally, the multiobjective ant colony algorithm was proposed to ensure secure routing. Here, the ant colony optimization algorithm is utilized for performing secure and optimal routing in the WSN environment. However, adoption of ant colony optimization algorithm has poor convergence speed. In [19], a novel architecture for minimizing the load distribution and elongating lifetime in the SDN network was proposed. Initially, a new scheme was introduced for routing of load balancing by SDN and virtualization-based topology, BS, virtual routing, link and controller discovery. Then the Open Flow protocol was adapted for evaluating the load balancing routing for individual flow based on the network functioning status and gathering information of load link through direct monitoring. Furthermore, the flows in several resource applications pass to the BS through various routes. Finally, the virtual routing intends to forward the optimal nodes for every IoT application. Here, the load balanced routing was performed in an effective manner in the SDWSN environment; however, without considering the security of the SDWSN environment during routing leads to cybersecurity threats. In this paper, the author offers a model-based intelligent traffic control mechanism entrenched on deep graph reinforcement learning (DGRL) in the SDWSN environment [20]. Initially, the proposed algorithm learns and optimizes the data forwarding policy for improving intelligent traffic control. Then, the intelligent control policies are generated by the SDWSN controller for network topologies and dynamic traffic patterns. Finally, the designed actor-critic which combines both graph convolution network and deterministic policy gradient was employed for determining the policies accomplished at sensor nodes for optimizing the process of data forwarding. This work performs intelligent and optimal routing by using deep reinforcement algorithm. However, the lack of managing the entities in the physical layer leads to increased connectivity issues. The author of this work proposed a trust-based and energy-aware routing protocol in the WSN environment [21]. This protocol was proposed by utilizing adaptive genetic algorithm TAGA, which aims to resist general routing attacks and specific trust attacks, thereby reducing energy consumption computed by data transmission. Initially, the TAGA builds the nodes' trust values entrenched on their direct trust values contemplating volatilization and adaptive penalty factors. Then, the filtering approaches were exploited for constructing the indirect trust values. Furthermore, the cluster head was optimally selected using threshold function based on nodes' dynamic changes, residual energy, and trust values. Finally, the secure and optimal routing path was identified using a genetic algorithm. Here, the secure routing was achieved by adopting the trust values of the wireless sensor nodes. However, the trust values were not enough for performing secure routing in the WSN environment. In this work, the author proposed a secure routing approach based on an enhanced optimization algorithm in the WSN [22; 23]. Initially, the optimal nodes or paths were selected for secure transmission based on optimal link-state multipath routing.