## 2 Related Works  
A review of the pertinent literature can be found in this section. In the last twenty years, the adoption of the IoT has received close attention from academics across diverse disciplines, influencing various facets of daily life. The essence of IoT lies in establishing connectivity among all entities, enabling human interaction with objects through a variety of SNs. In IoT, where battery-powered SNs are often deployed in remote or inaccessible areas, adopting energy-efficient strategies becomes paramount since these SNs have finite energy sources. To mitigate this challenge, energy consumption must be minimized. Numerous approaches have been proposed to prolong the lifespan of the IoT and enhance overall network longevity. The Cluster Head (CH) selection emerges as a crucial technique to ensure network stability by organizing SNs into clusters within the network area via machine learning, and CHs are selected from these clusters according to predetermined criteria. The utilization of CI, which is becoming more and more common in WSNs, the IoT, and crowd-sensing applications, can help to achieve this [31]. Numerous studies have explored metaheuristic optimization algorithm-based clustering techniques to address the ambiguity in WSNs. While various CH selection methods have been introduced, optimizing energy efficiency remains a persistent challenge in the IoT. Energy and Distance-Based CH Selection (EDB-CHS) and EDB-CHS with Balanced Objective Function (EDB-CHS-BOF) are two innovative protocols for CH selection that were proposed by [PERSON] et al. [32]. The objectives of these protocols are to prevent premature SN breakdown, handle imbalances in energy consumption among SNs, and increase the lifespan of the network as a whole. They suggest a hexagonal cluster structure for the EDB-CHS protocol and generate an expression with a closed form to find the ideal number of CHs in the network. They further offer an effective approach for choosing a CH based on a threshold probability that bears in mind many aspects, such as SN's probability of becoming a CH, closeness to the sink node, and SN's RE. On the other hand, long-distance communications between adjacent CHs are the focus of the EDB-CHS-BOF protocol. A new threshold probability for each SN to become a CH in a given round is proposed in this protocol. To guarantee a uniform distribution of CHs over the network, a balanced objective function is also introduced. Most importantly, the findings from experiments show that these proposed protocols perform better, far exceeding their rivals, especially when it comes to throughput and network longevity. The author in [33] utilizes two metaheuristic algorithms for clustering and routing tasks. Firstly, they employ the Whale Optimization Algorithm (WOA) to cluster the network, forming optimal CHs through a method called WOA-clustering. Subsequently, for routing these CHs to the sink node, they utilize the Harris Hawks Optimization (HHO) algorithm in a routing method termed HHO-Routing. By adopting these approaches, they achieve reduced energy consumption for data transmission to the sink node. To validate the efficacy of the proposed protocol, comparisons with existing algorithms are conducted for a more comprehensive assessment. The simulation outcomes reveal enhancements in data transmission rates, energy consumption, and the number of rounds before the first node dies. Motivated by the need to enhance energy efficiency in underwater WSNs and extend network lifespan, the authors in [34] presented an energy-efficient clustering and multi-hop routing protocol employing metaheuristic-based algorithms. However, cur rent metaheuristic-based techniques often employ distinct algorithms for clustering and multi-hop routing, resulting in increased computational intricacy, diverse initialization procedures, and challenges in hyperparameter tuning.