## 11 Scheduled Beacon-Based Node Broadcasting  
The proposed architecture for Beacon Node Broadcasting is presented in Figure 2. It has been demonstrated that the SRM-BNB method, which is a metaheuristic intelligence-based approach, offers the best scheduling for limited situations and unscheduled nodes. It is recognized that certain control parameters for optimization-based algorithms might affect them. Every algorithm has its own set of parameters, and the performance is governed by the correct setting of the control parameters. By avoiding a poorer option, the employment of the Bacteria Foraging Search (BFS) and its BFA with the BNB algorithm, which is influenced by metaheuristic, ensures that the solution to be determined does not depart from the optimum solution. It also has a single phase, making it simple to implement. Let 'h(x)' be the objective function that is minimized at any iteration 't'. Suppose that there are 'n' decision variables (i.e., j = 1, 2,...n) in the \(\mathrm{j}^{\mathrm{th}}\) variable, \(\mathrm{V}_{\mathrm{j,l,l}}\) value is obtained for the \(\mathrm{l}^{\mathrm{th}}\) individual performing the \(\mathrm{l}^{\mathrm{th}}\) iteration. For our case, the modified value is obtained with Equation (16) as follows:  
\[\mathrm{{}^{\mathrm{th}}(x)=V^{\prime}_{\mathrm{j,l,l}}+V_{\mathrm{j,l,l}}+V_{ \mathrm{1,l,l}}(V_{\mathrm{j,ideal,l}}-\mid V_{\mathrm{j,l,l}}\mid 1)-V_{\mathrm{j,l}}(V_{\mathrm{j,worst,l}}-\mid V_{ \mathrm{j,l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{ l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{ \mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid \endbox{\mathrm{where}~{}V^{\prime}_{\mathrm{j,l,l}}\) is the updated value of \(\mathrm{V}_{\mathrm{j,l,l}}\) and \(\mathrm{V}_{\mathrm{j,l,j}}\). \(\mathrm{V}_{\mathrm{j,l,l}}\) is in the range [0, 1]. The term \(\mathrm{V}_{\mathrm{j,l,l}}\)(\(V_{\mathrm{j,ideal,l}}-\mid V_{\mathrm{j,l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\mid_{ \mathrm{l}}\mid_{\mathrm{l}}\mid_{\mathrm{l}}\)) indicates the tendency of the solution to move closer to the ideal solution. The term \(\mathrm{V}_{\mathrm{2,j,l}}\)(\(V_{\mathrm{j,worst,t}}-\mid V_{\mathrm{j,l,l}}\mid_{\mathrm{l}}\)) indicates the tendency of the solution to avoid the worst solution. \(V_{\mathrm{j,l,t}}\) is accepted for a better function value at the end of the iteration that is used as the input to the subsequent iteration. The several functions described above are considered to determine how the suggested model works. Here, the value of 'x' that minimizes the multi-objective function is sought for. The stages that make up the suggested model are as follows:  
**Step 1:** Obtain the beacon node generating individual dispatchable \(\mathrm{B}^{\mathrm{unit}}\), the \(\mathrm{G}_{\mathrm{all}}\) and \(\mathrm{G}_{\mathrm{dall}}\), load, while 'P' is taken as 30 individuals' solution to exchange the position, and 100 iterations are used as the stopping criteria. **Step 2:** Use the upper and lower limits to generate the starting population at random within the range value that corresponds to h(x). A counter is set to the maximum iteration after the population generation. **Step 3:** Run the first beacon node dispatch without the BNB, then use Equations (1) and (5), respectively, to obtain the objective function that has to be reduced for the adoptive and opportunistic nodes. **Step 4:** At the conclusion of each iteration, update the population based on the fitness function comparison. In the event that the answer makes the goal constraint clear, be sure to perform the local search. Implement a strategy to keep and add variety to the population. Additionally, if the new solution provides a higher objective value, a global solution is obtained. **Step 5:** Convert the adoptive population to a binary representation that is indicated by the on/off value of a dispatchable \(\mathrm{B}^{\mathrm{unit}}\).