## 2 Related Works
In addition, this article employed a polling control access method based on busy/idle nodes, which conserved energy and improved network throughput. According to the simulation findings, the proposed method performed well in energy usage balance, power efficiency, network lifespan, network stability duration, and network throughput. Nevertheless, the developed algorithm could only be used in fixed networks. [PERSON] et al. (2020) [21] proposed an enhanced energy-efficient clustering protocol to increase the lifetime of WSN-based IoT networks by addressing clustering structure issues that decrease protocol performance. By optimizing the clustering structure, the proposed technique lowered and balanced the power usage of nodes. As a result, the presented protocol was declared viable for networks with a longer lifespan requirement. In addition, the proposed method used a novel goal function to choose CHs in ideal locations. Though, uncertainty is a big concern with this model. [PERSON] and [PERSON] (2021) [22] developed a method for determining the optimal values of direct detection of paradigm elements by multiple objective genetic algorithms. It was discovered that adjusting these factors will optimize energy efficiency while reducing the likelihood of reconstruction error. The proposed method achieved a fair balance between these two goals. They also devised a compressive sensing approach to conserve energy by decreasing the length of the sensing vector. When the proposed algorithms were evaluated to the performance of typical cluster selection algorithms, it was discovered that they had greater energy efficiency. However, the complexity of this technique was high. [PERSON] et al. (2020) [23] introduced the hybrid Chicken Swarm- GA (CSGA) clustering algorithm, in which the CSO algorithm was updated to optimize the energy utilisation in WSNs, to enhance network lifespan. CSGA used a hierarchal order approach. The population was separated into three groups and then sorted according to fitness values to choose the finest nodes that function as CHs every round. To enhance population variety, CSGA used crossover and mutation mechanisms. In addition, the fitness function was created to reduce the overall amount of energy spent and the total number of times the chosen set of nodes worked as CHs. However, this method failed to address the network heterogeneity. To tackle the optimal data transmission routing path in WSNs, [PERSON] et al. (2021) [24] proposed a unique and efficient Robust ACO (RACO) method based on ACO. The proposed method comprehensively considers power consumption, node distance, and connection security, improving ACO's heuristic value. Furthermore, the numerical testing revealed that the RACO approach was resilient and improved network overhead performance without raising network design, operation, or communication. However, the proposed method has a higher computational cost. To prolong the lifetime of heterogeneous WSNs, [PERSON] et al. (2020) [25] developed a Modified GA (MGA). In contrast to prior studies, MGA represented solutions using two-level organized chromosomes. The value of this chromosome was that it represented the exact scheduling of each set and the detailed energy allotment of each sensor. For the generation of the initial population, a greedy strategy was used. This approach has a temporal complexity. However, it considerably increases the pace of searching. Forward and backward mutation procedures were created as unique mutation operations. The forward mutation ensures chromosomal variety, whereas the backward mutation can aid in efficiently leaping out of local optima. However, scalability and mobility were not provided in this approach. [PERSON] et al. (2019) [PERSON] et al. (2019) proposed a unique Load Balancing Ant-based routing protocol (LBAR) for WSNs. Under the limits of a limited energy supply, LBAR aimed to balance energy consumption, extend network lifetime, and speed up route-finding convergence.