## 3 Network Model and Problem Formulation
### Node Clustering Using LEACH Protocol  
WSN is susceptible to energy conservation, which depends on the network model and will affect the network's lifetime. The network life depends on the routing protocol used in the network. The deployment of nodes takes place in the network and plays a major role where the network nodes can obtain the maximum coverage. In the proposed system, the LEACH routing protocol has been used to deploy the moving nodes to optimize the route parameters based on the signal strength with the sensing domain. The node deployment involves a setup phase, where the clustering of the sensor nodes is formed, and all the information is communicated from the cluster head to the corresponding cluster nodes via the sink node. The cluster head collects the information from the nodes and sends it to the various anchors and the adjacent nodes in the network. The energy consumption in each node is assumed to be under control in each phase of the sensor selection. The energy consumption is assumed to be off in the cluster assumptions in the threshold values attained using balance analysis. The energy monitoring is conducted using the LEACH protocol with the interval range threshold value [1, 0]. The threshold value of the network is computed by using Equation (1):  
\[TSN(i)=\begin{cases}\frac{probability\ of\ percentage\ in\ cluster\ head(pr_{ch})}{1-probability\ of\ percentage\ in\ cluster\ head\ (pr_{ch})}\ if\ m\in N\\ 0\end{cases} \tag{1}\]  
where \(TSN(i)\) denotes the threshold values of the network.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameters** & **Details** \\ \hline X & Adjacent nodes coverage parameter \\ \hline Yn & Nearest hop nodes \\ \hline U = m\(-\)1, m\(-\)2, m\(-\)3 & Adjacent node \\ \hline U = n\(-\)1, n\(-\)2, n\(-\)3 & Nearest hop node estimation \\ \hline hop = NUI\(\cup\) & Hop distance \\ \hline \(hd_{m,n}^{est}\) & Nearest hop distance estimation \\ \hline \(hd_{m,n}^{est}\) & Nearest hop distance actual \\ \hline EA & The average error in distance between the sensor and adjacent nodes \\ \hline SD & Shortest distance between the sensor and unknown nodes \\ \hline Nm & Sensor nodes in the m segments \\ \hline Est\({}_{\text{Am}}\) & Estimation accuracy of the sensor nodes \\ \hline AN & Set of the unknown nodes in the coverage area \\ \hline AVG\({}_{\text{disterror}}\) & Average of the distance in the estimated and the actual \\ \hline TSN(i) & Threshold values of sensor network \\ \hline ED & Euclidean distance \\ \hline Pr\({}_{\text{ch}}\) & Probability of percentage in cluster head \\ \hline P\({}_{\text{sn}}\) & Probability of sensor nodes \\ \hline E\({}_{\text{s}}\) & Sensor node energy \\ \hline E\({}_{\text{src,dest}}\) & Energy of source node and destination node \\ \hline RX\({}_{\text{E}}\) & Received energy \\ \hline Tn & Total number of nodes \\ \hline Rt & Range of transmission nodes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
The _probability of percentage in cluster head_\((pr_{ch})\) is where '_m_' denotes the number of nodes possible within the internal cluster communication, and '_N_' denotes the set of sensor nodes possibly used within the framework estimation of the sensor nodes.