## 3 Proposed System  
Load balancing in a fog environment might be challenging; thus, we provide a DEELB strategy to do just that. It is an efficient approach for balancing the load on the system by allocating resources to users based on their energy consumption and processing requirements. It is through the following steps that Figure 2's DEELB plan is applied:  
1. The user will send the Task Manager \"n\" tasks. Based on the instructions it provides, each activity's computing cost and energy consumption are seen as being preset. 2. In this scenario, the information provider will register n resources via the Resource Registration and Inventory Protocol. Each task must also have specified computational expenses and energy requirements for each resource based on the work instructions. 3. The Tasks Manager sends task information to the Resource Scheduler. Jobs are arranged in descending order based on cost and energy calculations: S1S2R2 > R3... Rn. 4. RIP provides information about resources that are readily available to the Resource Scheduler. The available resources are arranged according to computational cost and energy consumption: R1 > R2 > R3... Rn. 5. The Resource Engine needs data from the Resource Scheduler regarding jobs and available resources to carry out its task. 6. When tasks are assigned to available resources, the Resource Engine starts the execution process and updates the Resource Load Manager on its status. 7. After a process, the Resource Load Manager will inform the Resource Power Manager of the current resource load. 8. The Resource Power Manager decides whether or not the resource is powered on based on the resource's usage. 9. If everything goes as planned, the Resource Engine will collect and provide the data to the user. When implementing the suggested DEELB technique, jobs are forwarded to the Tasks Manager. Information aggregators track hardware and software in a cloud data center. The Resource Scheduler receives all of this data regarding jobs and available resources. Tasks are prioritized by the resource scheduler in light of historical data and projected demands. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Author** & **Techniques/Methodology** & **Year** \\ \hline [PERSON] et al. [26] & Dynamic load balancing using VM for Cloud & 2019 \\ \hline [PERSON] et al. [27] & The buffer-aided transmission system uses power-splitting & 2019 \\  & relaying which consumes less energy & 2020 \\ \hline [PERSON] et al. [28] & Geography-Aware Task Scheduling & 2020 \\ \hline [PERSON] et al. [29] & Review of load balancing approached in cloud computing & 2020 \\ \hline [PERSON] et al. [31] & Shark Smell Optimization algorithm (SSO) & 2020 \\ \hline [PERSON] et al. [32] & Lightweight Certificateless Signature (CLS) protocol & 2021 \\ \hline [PERSON] et al. [30] & Techniques for managing loads and resources in a diverse and homogenous cloud environment & 2021 \\ \hline [PERSON] et al. [33] & Whale Optimization Algorithm (WOA) & 2021 \\ \hline [PERSON] [37] & Fog computing orchestration & 2021 \\ \hline [PERSON] et al. [34] & Reconfigurable Intelligent Surface (RIS) & 2022 \\ \hline [PERSON] et al. [36] & Directed Acyclic Graphs (DAGs) & 2022 \\ \hline [PERSON] et al. [38] & Systematic literature review of fog orchestration & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature Review on DEELB in the IoT environment. The Resource Engine distributes tasks among the team's active members using the data from the Resource Scheduler. When a task is finished, the Resource Power Manager and the Resource Load Manager are informed about the resources that are still available. Powering and disabling resources fall under the purview of the Resource Power Manager. When a job is finished, the Resource Engine notifies the client of the outcome. Figure 2 displays the suggested DEELB approach's block diagram.