## 3 Problem Statement  
The specific problems employed in the existing works are briefed in this section. Furthermore, this section lists the problems faced by each work and also provides the corresponding solution.  
The authors in this work perform packet processing and validation mechanisms for ensuring security to the software-defined network environment [31]. The route update messages were filtered out as they contained malicious and redundant updates based on two conditions such as frequent announcement and withdrawal, respectively. In a similar manner, the authors in [32] enable secure multipath routing for a SDN environment. The entities involved in this work were software-defined nodes, open flow switches, and controller. The network flow information was collected by the controller for packet processing based on validating the flow rules and forwarding them to the other neighbor node. The problems encountered in these works are listed below:  
* Here, the security during routing was ensured by validating the route update messages of the neighbor nodes. However, only validating the route updating message and leveraging the neighbor node legitimacy leads to route misdirection attacks.  
* Even though this work adopts a routing security problem in the SDN environment, this work lacks with considering interference and link failures during routing, which easily welcomes attackers to impose several malicious attacks.
* Furthermore, the routing path in this work was of a static nature, which was easy prey for the cyber crooks to impose reconnaissance attacks, thereby manipulating the routing path.
* In addition to the static routing path, this work also kept the detection layer as static, which caused the software-defined network switches to be compromised and perform black-hole and wormhole attacks, respectively.  
The authors in this work adopt optimization algorithms for enabling energy-efficient secure routing in a wireless sensor network environment [33]. Taylor-based cat swarm optimization algorithm was used. The main entities involved in this work were wireless sensor nodes and base station. The problems that were faced this work are listed below:  
* Here, the wireless sensor nodes and base stations were placed in a random manner without awareness of the network dynamics. Such random placement of entities leads to connectivity issues among the nodes and the base station.
* The low-energy adaptive clustering hierarchy protocol was utilized for cluster head selection. However, the adoption of the low-energy adaptive clustering hierarchy protocol limits with sparse distribution of cluster heads, thereby affecting the energy efficiency.  
The authors in this work performed an adaptive scheduling method for wireless sensor network environments in a dynamic manner [34]. The entities involved in this work are wireless sensor nodes and base station. This work adopts a duty cycling method for achieving the adaptive and dynamic scheduling in the wireless sensor network environment. The major problems employed in this work are:  
* This work only provides scheduling rules for the sensor nodes. As the network is composed of multiple sensor nodes, the mentioned scheduling rules (i.e., four states) did not provide proper management, leading to a high chance of interference.
* Improper management of sensor nodes and base station also affects the scalability and connectivity issues, thereby leading to high delay and energy consumption. In addition, security threats also happened due to improper node management.