## 3 Proposed Scheduling Scheme
### Recurrent Learning  
Service distribution and power allocation are estimated by introducing recurrent learning to the user-centric concurrent sharing interval. Here, power management is used to deploy congestion-free transmission and evaluate sharing with the end-user on time. The prediction is achieved by mapping the existing process and deploying resource access for service shar  
Figure 3: CS3 queuing process. ing. Training data determine the input neuron and perform an efficient distribution in this recurrency phase. In this processing, the input is the queued process, and it enters the initial neuron state, from the output of the first neuron forward as the input to the second neuron. Thus, the service delivery is estimated for the power allocation and addresses the deficiency. The equation below is used to analyze the current state of neuron processing. \[k_{0}=u(p_{i})+r_{a}(S+q_{0})\times o_{0} \tag{5}\]  
In Equation (5), the current state represents the service distribution by deploying resource access associated with power allocation. Here, the queued process transmits to the next neuron layer, followed by the number of devices and users. Sharing is performed on time for the valid user based on a queued process. Scheduling is performed by deploying network and device cumulative congestion and power management. The first state indicates resource sharing, and from this, it is forwarded to the next neuron layers in recurrent learning. Here, service delivery is conducted to allocate power and deploy distribution. The neuron layer is denoted as \(o_{0}\); the function is used to define the current state, and it is represented as \(u\) and \(k_{0}\). The current state defines the number of neuron layers in recurrent learning that deploys joint scheduling, and power allocation is addressed. Based on the scheduling process, the analysis is performed for the current neuron state; resource access is forwarded to the second neuron layers. The dynamic user densities are due to the concurrency in service sharing and resource access in SDN-based IoT applications. The hidden layer improves training data, concurrent service sharing, and resource access. The following Equation is used to derive the hidden layer, and here, two hidden layers are used to enhance service distribution efficiently. \[\
abla_{1}=\left\{ \begin{array}{c}o_{0}=(S+r_{0})\times\left(\frac{M+i_{d}} {q_{0}+v_{0}}\right)+b^{\prime}-e_{t}\\ o_{1}=(S+r_{1})\times\left(\frac{M+i_{d}}{q_{0}+v_{1}}\right)+b^{\prime}-e_{t }\\ \vdots\\ o_{n}=(S+r_{n-1})\times\left(\frac{M+i_{d}}{q_{0}+v_{n-1}}\right)+b^{\prime}-e _{t}\end{array} \right. \tag{6a}\] \[\
abla_{2}=\left\{ \begin{array}{c}o_{0}=q_{0}\times\left(\frac{a_{0}}{r_{0}} \right)+\sum\limits_{k_{0}}v_{0}\times c_{k}(r_{0})\\ o_{1}=q_{0}\times\left(\frac{a_{0}}{r_{1}}\right)+\sum\limits_{k_{0}}v_{1} \times c_{k}(r_{1})\\ \vdots\\ o_{0}=q_{n}\times\left(\frac{a_{0}}{r_{n-1}}\right)+\sum\limits_{k_{0}}v_{n-1} \times c_{k}(r_{n-1})\end{array} \right. \tag{6b}\]  
In Equation (6a), the first hidden layers deploy the scheduling for the number of users in an SDN-based IoT application. Here, power management is used to determine the current layers in neuron layers, and balancing is achieved for resource access at the mentioned time. The neuron layer indicates scheduling for the number of users and deploys resource access based on power allocation. Concurrent service sharing and resource access are provided to the appropriate user. Here, the hidden layer enhances the training data for access to resources in the SDN. Scheduling and power allocation are conducted for resource access by performing service sharing. In Figure 4, the recurrent layer process is illustrated. Figure 4 illustrates an RNN; it should typically show nodes connected in a way that indicates temporal or sequential processing. This might include feedback loops or connections where the output from one time step is used as the input for the next.