## 2 Related Works  
As a sensor node's energy is used predominantly for information gathering and transmission [19], the conventional steering technique considers how to move information from the source node to the BS as quickly as possible using the most limited resources. Nevertheless, in the energy-compelled sensor organization, much information is sent from source nodes to a sink node in a \"many-to-one\" mode, which effectively presents a genuine funnel effect and energy hole issues. Therefore, the energy utilization of nodes situated around a BS is much higher than in others, bringing about energy lop-sidedness and a lower network lifetime. Some customary strategies diminish the network's transmission distance and energy utilization, subsequently enhancing the general lifetime of the system [20; 21]. Neighbor nodes with the least utlization hop from source to sink as the handover, utilizing the leftover energy of the node as the determinant [20]. A ladder diffusion algorithm is developed based on ant colony optimization (ACO) to address the issue of energy utilization [21]. This method primarily utilizes the ACO component to decide the transmitting paths, which adequately diminishes energy utilization. An energy efficient TLC algorithm for WSNs is presented in [22]. Cluster relay node is introduced which acts as a backup for CH. This node is selected according to the minimum distance between the nodes in the cluster and the sink while considering the residual energy. The least hop-dependent calculations are comparable to the base energy direction [23]. This type of strategy can lessen the energy utilization of the network, but with clear inconveniences for few nodes that attempt information transmission in a specific timeframe while different nodes are inactive. In other words, when the sending node is picked, the leftover energy is ignored which can easily lead to a few nodes running out of energy, thus causing lop-sided energy conveyance between nodes. In this manner, the network lifetime is consistently at a lower level. A multi-objective particle swarm optimization with Levy distribution (MOPSO-L) is presented for organizing the clusters [24]. In every round of information exchange, the best of each local solution is assigned as a global optimal solution which acts as a CH. In any case, without limiting the energy utilization of the network, energy losses may occur due to diversions. Thus, as expected, a few calculation techniques can limit the energy use while guaranteeing the energy equilibrium of nodes [25; 26]. ACOHCM, proposed by [25], utilizes the upsides of the ACO and the minimum hop directing system to adequately diminish the network energy utilization. ESRA, proposed by [27], develops a base energy-use tree and uses a cut-edge technique to adjust the load between nodes, and successfully expanding the network lifetime. However, the computational intricacy and overhead generated by the two calculations are enormous and consume many assets in practical applications. Therefore, considering only single steering data or utilizing a basic numerical model for directing choices is irrational. As such, it is vital to thoroughly consider the different network properties. The key idea of [28] is to preferentially rank nodes which have better energy efficiency. Nodes at a large distance from the CH will consume higher energy compared with nodes nearer to CH. The distance of a node to its CH is also taken into account along with energy parameter to select the optimal forwarder. The original LEACH activity is isolated into rounds. Each round starts with a set-up phase, when the clusters are coordinated, followed by a steady-state stage, where information is moved from the sensor nodes to the BS. The set-up stage is split into advertisement, cluster set-up, and schedule creation phases. In the set-up stage, an arbitrary number between 0 and 1 is chosen for each node \(n\). When a selected node is presenting less than a threshold T(n), the node becomes the fortunate CH for the current round.