# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 4 Proposed Hybrid PSOGA
### Trust-Based Cluster Head Selection  
Over the past few decades, researchers have employed a trust parameter in WSNs [30; 31; 32; 33; 34]. For instance, the authors of [35] proposed a framework that forms clusters in a manner so as to prevent the likelihood of compromised or malicious nodes being elected as CHs. For the same purpose, the current model uses statistical methods to evaluate trust without considering the trust among the sensor nodes. In [35], the authors suggested a model that evaluates trust using a classical probability model. Furthermore, the suggested model employs simple statistical methods for the same purpose, barring the trust recommendation among sensor nodes that prevents nodes from reflecting trust in an accurate manner. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline Ir(N\({}_{\text{i}}\)) & Information register with information of individual node Ni in the network \\ \hline A & Area \\ \hline N\({}_{\text{i}}\) & Node i \\ \hline L\({}_{\text{s}}\) & Sink location \\ \hline N\({}_{\text{s}}\) & Number of grids in a network \\ \hline F\({}_{\text{ch}}\) & Farthest CH \\ \hline S & Network sink \\ \hline V* & Set of random routes of a grid to adjacent grids in the network. For example, from the farthest CH to the sink from (F\({}_{\text{ch}}\) to S) \\ \hline BestScore & Best route score \\ \hline N\({}_{\text{a}}\) & All nodes in route \\ \hline R\({}_{\text{score (i)}}\) & Global route score \\ \hline Score (\({}_{\text{i}}\)) & Individual route score \\ \hline G\({}_{\text{BestScore}}\) & Global best score \\ \hline L\({}_{\text{bestScore}}\) & Local best score \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters used in the proposed PSOGA algorithm. The authors of [36] also proposed a trust-based LEACH (T-LEACH) protocol that utilizes CH-assisted monitoring to reduce energy consumption. In the proposed model, the trust module consists of two different modules, namely, a monitoring module and a trust evaluation module. Here, the monitoring module observes the network and reports to the trust evaluation module if any misbehavior is noticed. Further, a node also maintains a neighbor situational trust table (NSTT) loaded with a trust value for each pair of node IDs and situation operations, such as data sensing, localization, and routing. As a result, T-LEACH loses less data than LEACH, as half of all the information sent by cluster participants is processed by the gateway. However, it is not possible to stop the constant loss of data in T-LEACH because of the lack of monitoring on the cluster head. Similar work is also carried out in [37] by the proposed node behavioral techniques trust analysis algorithm banding belief theory (NBBTE), which combines the method of the node behavioral strategy and Dempster-Shafer (DS) evidence theory. Here, a variety of network application-related trust factors, such as the packet size, the forwarding rate of the packet, data reliability, and security grade and coefficients are considered to determine the confidence values by measuring the weighted average of the trust factors. The simulation tests demonstrated that the proposed scheme is capable of effectively evaluating the trust nodes. However, the method of trust evaluation can entail excess energy, time, and costs due to the cooperation and interaction with neighbors and other numerous parameters. In [19], a cluster head selection method was proposed, which is based on a trust factor that ensures all nodes are trustworthy and authentic during communication. To achieve this, direct trust is calculated using parameters such as the residual energy and the distance between the nodes, along with the use of the k-means clustering algorithm. The selection factor, \(F(f)\), for recommended nodes is evaluated using the following weight function:  
\[F(f)=w_{1}*\left(\frac{e}{d}\right)+w_{2}*trust\_value \tag{10}\]  
\[w_{1}+w_{2}=1 \tag{11}\]  
where \(w_{1}\) and \(w_{2}\) are the weight values, which can vary between 0 and 1.