## 3 Literature Review  
A huge variety of clustering methods are available for maximizing energy efficiency and increasing the lifetime of a node. Algorithms for this will offer both advantages and disadvantages. This section reviews some existing research on these algorithms [4; 5]. A longer network lifetime can be achieved by balancing the energy consumption of SN and distributing the load among them. Uniform/non-uniform/equal/unequal clustering is possible in these techniques [9]. These clustering techniques employ characteristics like distance, residual energy, node density, etc., in a probabilistic manner known as the weighted-sum approach to optimize network lifetime [7]. However, there is still room for improvement in these energy-efficient clustering algorithms to maximize each node's lifespan. Algorithms for energy-efficient power consumption are commonly based on battery conservation, node scheduling, load balancing, coverage area size, clustering, and routing techniques [13]. These algorithms reduce energy usage as much as possible to maximize network longevity and coverage. We will discuss several newly proposed guidelines that deal with energy and coverage in the following. Low-energy adaptive clustering hierarchy (LEACH) [1] is a first-of-its-kind WSN clustering routing technique that chooses CHs based on rotation and a random value. LEACH operations are done in rounds; each round has two parts: setting up and staying steady. During setup, a CH is chosen, a cluster is made, and a Time Division Multiple Access (TDMA) schedule is given to each member node. In the CH selection process, each node comes up with a random precedence value between 0 and 1. If the random number that SN comes up with is less than T(n), it becomes CH. Choosing the CH based on residual power is vital to how well a network works and how long it lasts. The network can live longer if the chosen CH is a sensor node with the most energy left out of all the cluster members. Based on residual energy, node location, and neighbors' numbers, the Improved-Low-energy adaptive clustering hierarchy (I-LEACH) [5] clustering routing system selects the CHs. There are considerations for additional characteristics that affect the network's lifespan, as they are not in the original LEACH algorithm's CH selection. The residual energy, the number of neighbors' nodes, and the distance to the BS are all accounted for in the threshold function of the I-LEACH approach. Coverage Preserving Clustering Protocol (CPCP) [6] was presented to preserve coverage through CH selection approaches. The CPCP considers numerous cost parameters that will affect the lifespan and range. In addition, it prioritizes SN with a high deployment density as CHs, active nodes, and routing nodes. It is a good solution for network coverage. However, CPCP has a colossal processing burden on sensors, making it inefficient. Choosing a CH and active node in a randomly dispersed network can be more accessible with the Coverage-Aware Clustering Protocol (CACP). CACP, on the other hand, requires each CH to submit the aggregated data straight to the sink. As a result, the CHs die more quickly and consume more energy. Centralized Low-energy adaptive clustering hierarchy (LEACH-C) [7] is a centralized clustering algorithm that uses the excess energy of nodes to select CHs and thus creates clusters within the network. The typical LEACH protocol's efficiency problem is addressed by LEACH-C, which uses a centralized method to pick the most skilled CH from a pool of nominated CHs. During the first phase, sensor nodes communicate with the sink to learn how much energy they have left and where they are. To identify which nodes are to be chosen as CHs, the sink uses these data to calculate the average power of all grounded nodes in the system. As a result, CHs will be selected from nodes with more energy remaining than the average in this round. Each stage of this retransmission wastes a small amount of energy, which is not ideal.