## 2 Related Work  
Some of the recent research works related to efficient CH selection and routing were reviewed in this section.  
[PERSON] and [PERSON] [20] proposed an Energy-efficient Cluster-based Routing Protocol with Enhanced Ant Colony Optimization (ACO) and Unequal Clustering (ECRP-UCA).  
To effectively balance the load across Cluster Heads: Based on residual energy, ECRP-UCA splits the network into uneven clusters; how far away the sink is; several nodes in the same neighborhood; and an additional factor called prior cycle's number of backward relay nodes. The next hop sensor node's energy is factored into the heuristic function, the distance between the current and the next sensor nodes.  
To address these challenges, [PERSON] et al. [21] suggested a different cluster formation and Cluster Head (CH) selection technique. There are two stages to the procedure. Initially, by the Saifish Optimization Algorithm (SOA),the CH is chosen to be called a Swarm Intelligence Algorithm. Next, the Euclidean distance forms the cluster. Previous clustering techniques include issues with the network's short lifespan, imbalanced load among network nodes, and end-to-end delay.  
[PERSON] et al. [22] introduced an Energy Enhanced Routing Protocol (EERP), which is a novel routing protocol utilizing high-efficiency data transfer rules. For creating a fault-free communication model for the WSN environment, the new EERP technique incorporates the scheme's sophisticated clustering logic. With this method, a standard routing structure concerning the base station and sensor nodes is created. The management of CH is the most significant component of a cluster-based wireless technology, as it must be chosen based on specific communication rules, including distance estimation, node capacity, positioning of the base station, and cluster region position. For analyzing the CH and improving the estimation of the pathway process, these restrictions are necessary. To provide an effective CH election process, the presented EERP method makes use of the efficient CH election algorithm Firefly.  
To increase network energy, [PERSON] et al. [23] introduced a multi-hop routing scheme depending on the path tree. To build a cluster headset, initially, several nodes that are closer to the base station and then have a lot of leftover energy are chosen. After that, the whole cluster is partitioned into smaller regions, and the nodes with higher remaining energy than the cluster's regular remaining energy are chosen as working nodes in each region. Eventually, the CHs are ordered based on how far they are from the base station.  
The optimal route creation and CH selection presented by [PERSON] et al. [24] are considered tough challenges in WSNs. To improve the network's lifetime and minimize total energy usage, a mixture of ACO and BOA was employed in this study. The node centrality, neighbors' distance, nodes' residual energy, the Base Station's (BS) distance, and node degree were all used in the BOA-based CH selection. To identify the best CH, through the node groups, this fitness function was utilized. By optimizing ACO with three distinct factors, the most energy-efficient path was found. The Advanced Configuration and Power Management Interface (ACPI) and JEHDO are both independently battery- and energy-efficient, according to exploratory results by [PERSON] et al. [25; 26; 27]. An enhanced cuckoo search algorithm is used for node localization to obtain optimal energy efficiency routing by [PERSON] [28].An improved version swarm intelligence approach of the whale optimization algorithm was used for node localization to improve the energy efficiency of the network by [PERSON] [29].