## 2 Literature Review  
A novel energy-efficient WSN routing protocol is accomplished in the proposed protocol by optimizing the path's minimal residual energy, while adhering to stated reliability limitations. The second protocol is a simplified variant of the first, in terms of duration and complexity. As illustrated by the simulation findings, the suggested procedures maximize the lifetime of WSNs, while adhering to reliability limits. In addition, they optimize how energy is distributed across the nodes in the network [14]. A CH selection technique for WSNs was created based on sampling and spider monkey optimization. This article offers a spider monkey optimization (SMO) approach based on sampling. If the sample population comprises nodes, CHs are chosen from the nodes. The simulation results were compared to those obtained using related protocols, including the threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), particle swarm optimization clustering protocol (PSO-C), and low-energy adaptive clustering hierarchy centralized (LEACH-C), in both homogeneous and heterogeneous configurations [15]. An energy-efficient clustering technique was shown in action for magnetic induction-based underwater WSNs. The paper offered a jellyfish-breathing method for choosing the CH and an algorithm for automatically adjusting sensor nodes. Then, it used the current system model to provide a clustering protocol comprising two main components. The results of the simulation showed that the proposed clustering strategy offers a high network capacity rate, while preserving a high degree of energy normalization [16]. A hybrid strategy for optimizing the quality-of-service measures in WSNs was proposed. The study suggested a novel mixed-model approach for maximizing network longevity. The major objective of this work was to enhance the lifespan of WSNs by combining hybrid optimization techniques with active routing algorithms. Five hundred twelve bytes were processed every second. Each data transport packet was 100 bytes in size. According to the simulation results, DSDV alone was less effective in WSN scenarios than DSDV combined with the two optimization approaches and the routing protocol [17]. The authors suggested a novel dynamic energy efficiency routing (DEER) protocol that ensured message delivery. To send the message from the source to the destination, the DEER dynamically chose the node with the maximum amount of residual energy at predefined energy levels. Comparing this suggested method to probabilistic energy profiles, effective hop routing, digraphs, and random algorithms, it was shown to increase session life time and data flow efficiency [18]. Based on DS evidence theory, a routing method that conserves energy was described. The DS evidence theory uses three attribute indexes as evidence, considering among other things the traffic, the distance of the route by the shortest path, and the residual energy of the nearby nodes. Next, using the entropy weight approach, the weights of the three indices were objectively determined. The simulation findings demonstrated the strong transmission reliability of the DS evidence theory, which can significantly reduce network energy consumption compared to the MCRP and FLEOR algorithms, increase network lifetime, and minimize packet loss rates [19]. An integration of the fuzzy clustering technique with the EHO-Greedy algorithm for efficient routing in WSNs was proposed. It analyzed the distinct sink nodes of a fixed and moving sink, to optimize energy usage. Clustering was accomplished using an improved EM approach, which collected SNs with similar features. The choice of CH was essential to this clustering scheme's success. The experimental outcomes indicated that the suggested system performed better than the existing techniques [20]. Harmony search provided energy-efficient clustering and routing for WSNs. The recommended method was broken down into two stages: clustering and routing.