## 2 Background  
This section mainly focused on CH selection techniques for WSNs. [PERSON] et al. [24] proposed a CH selection approach for a livestock industry WSN. The rabbit optimization algorithm (ROA) was used to select the designated CH in the network. The MATLAB 2021a platform was used for simulation in this study. The performance of EECHS-ARO was examined by comparing it with those of the QOBOA, ALO, and TLBO. EECHS-ARO improved the network lifespan and packet delivery ratio by 15% and 5%, respectively. However, EECHS-ARO requires convergence time while selecting the CH. The ROA requires considerably less convergence time than QOBOA, ALO, and TLBO. [PERSON] et al. [25] proposed a metaheuristic-optimized CH selection-based routing algorithm for WSNs. It uses the dragonfly optimization algorithm (DOA) to minimize energy consumption and achieve fast data transmission. MOCRAW has two phases: CH selection algorithm (CHSA) and routing selection algorithm (RSA). CHSA selects the optimal CH based on distance, RER, and node density metrics. MOCRAW was implemented using the NS2 simulation tool. The efficacy of MOCRAW was examined using E-FUCA, EAFTC-RIS, GAPSO-H, ECRP-UCA, and HMBCR. MOCRAW improved the overall energy efficiency by 10-15% compared with E-FUCA, EAFTC-RIS, GAPSO-H, and ECRP-UCA. However, convergence time is required to select the optimal CH. [PERSON] et al. [26] proposed a sandpiper optimization algorithm (SOA) based on CH selection in IoT. SOA focuses on both cluster formation and CH selection using Euclidean distance. A MATLAB 2019a simulation environment was used for the SOA. The effectiveness of the SOA technique was improved compared to the EECHS-ARO, IABC, PSO, and ABC-Cd techniques. Compared with similar methods, the proposed SOA increased the network lifetime by 3-18% and throughput by 6-10%. Consequently, the SOA decreases the overall energy consumption while selecting the CH. Therefore, it transmits more packets with less delay during CH selection. [PERSON] et al. [27] proposed a hybrid swarm optimization algorithm for CH selection in a WSN. To select the CH, the harmony optimization algorithm (HOA) and competitive swarm optimization (CSO) approaches were combined. A network simulator (NS2) was used to simulate HAS-CSO. The performance of HAS-CSO was computed based on various metrics: RER, count of dead nodes, and throughput. As a result, the HAS-CSO approach surpassed the overall network lifetime of ABC-SD, FAMACROW, and Bee Swarm by 5-10%. However, the fitness function requires more computation time for CH selection. [PERSON] et al. [28] proposed a hybrid optimization-based energy-aware CH selection method for WSNs to improve overall network performance in terms of energy and network lifetime. A MATLAB simulation environment was used to implement the proposed PDU-SLnO energy-efficient protocol. The PDU-SLnO approach was developed using PSO and sea lion optimization (SLno) techniques. The efficacy of PDU-SLnO was analyzed with respect to various metrics, such as delay, energy, quality of service (QoS), and distance. The proposed PDU-SLnO enhanced the overall normalized energy by 32.90% compared to GA, PSO, ALO, GAL-LF, GOA, FGF, and CS-PS. However, this protocol can only be used in specific applications. [PERSON] et al. [29] proposed an energy-efficient CH selection protocol for WSNs. This paper presented an improved sparrow search algorithm for choosing the best CH possible in a cluster. The proposed EECHS-ISSADE approach was computed using various parameters, such as the number of active nodes, inactive nodes, throughput, and unused energy. The EECHS-ISSADE approach was simulated using MATLAB R2018a. The proposed EECHS-ISSADE improved the network longevity by 6-8%. However, it required a long convergence time during CH selection. The energy efficiency of EECHS-ISSADE was superior to that of EECHS-ABC, TABU-PSO, and LEACH. [PERSON] et al. [30] proposed a hybrid model to select the optimal CH in a WSN to ensure network consistency.