## 1 Introduction
Most of the routing algorithms focus on dynamic clustering in which clusters are re-created during the lifetime of WSN based on the current states of residual energy, node density, and other parameters. Each sensor node performs a few primary tasks, such as sensing environment, memory management, internal computing, and code execution. In WSNs, after cluster formation, there are different possible states of a node. A node can be (i) an independent node that is not a member of any cluster, (ii) a candidate for cluster head, (iii) a cluster head, (iv) a member node, (v) an assistant cluster head, (vi) a transient node that acts as a link between CH and cluster members, and (vii) a gateway node that acts as a linker between CH to CH. Additional responsibilities are assigned to the nodes in different states. Figure 4 describes the primary responsibilities of CHs, non-CHs, and individual sensor nodes. These additional tasks are also leading to a quick drain of the energy available at the sensor nodes. Since the transmission range of these miniature devices is very short, the placement of nodes and sink is one of the most challenging tasks in the formation of WSNs. As most of a node's energy is dissipated in the transmission phase as shown in Figure 2. If a CH is located far from the base station (BS) or sink, then it will drain more energy in the transmission phase than those CHs which are located closer to the BS. Therefore, several existing routing techniques have been extended to multi-hop routing techniques to manage coverage in larger areas and network stability. It is observed that the addition of nodes with higher energy levels exhibited enhanced performance. This heterogeneity of nodes in terms of residual energy provides more stability [17; 18]. A variety of algorithms are discussed in the next section, including heterogeneous nodes. Some researchers focused on complex techniques for data aggregation to reduce the size of data transmission at CHs, which often overburdened the CH nodes and increased transmission delay. Figure 4: Common energy consuming tasks. In this paper, we focus on hierarchical routing in which an entire network is distributed in a group of nodes (clusters). A node is selected as the CH from each cluster using computational and probabilistic operations. These CHs are responsible for collecting data from their respective clusters and transmitting the aggregated data to the BS. By a few specified nodes, this controlled transmission shows significant improvement in the network stability by hierarchical (cluster-based) routing. We deploy homogeneous and heterogeneous nodes randomly within specific boundaries and collect multi-type information by clustering techniques. Furthermore, we use threshold values to regulate unnecessary transmission which shows more stability in our evaluation. The rest of the paper is organized as follows. Section 2 describes related work and the background concepts relevant to our proposed technique. Section 3 describes the architecture of our proposed model and its phases. Section 4 provides the details of the proposed energy model. Section 5 presents the evaluation of the proposed approach. Finally, Section 6 concludes this paper.