# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 3 Proposed Studies
### Proposed Algorithm
For normal operation such as sensing and communication consume around 1000 units, while CH selection shows the highest energy consumption which is 7000 units and for anomaly identification energy consume approximately 800 unit. Figure 7: Sensor node deployments. Figure 8: Cluster head selection. Figure 9: Energy consumption during normal operation, CH selection, and anomaly identification in WSN. Figure 10 shows various performance indicators while WSN simulation. The metrics are displayed normalized values to compare across different scales. he successful deliveries = 30,000 represent the number of packets delivered, total latency = 154,428 reflects cumulative communication delay in the network, total packets sent = 50,000 shows the overall volume of data generated, packet forwarding rate (pfr) = 0.616 shows the reliability, latency (lt) = 5.013 is average delay per transmission, energy consumption (ec) = 308.00, network longevity as a fraction of alive nodes (NL) = 0.384 shows operation efficiency of the network over time, Routing Overhead (ROH) = 0.0033 shows protocol related overhead. Identifying the suitable algorithm requires a balance between energy efficiency and latency, especially as the network grows. The graph below represents the trade-offs that different algorithms present when scaling up a network. TIOCHR and especially TEAHR appear to be more scalable solutions with respect to latency. As defined in Algorithms 1 and 2, the TEAHR adopts a multi-level trust assessment mechanism, which includes energy trust, forwarding trust, consistency trust, behavioral trust, and anomaly detection. These may increase computation and storage overhead. To determine whether it does impose an additional performance burden or not, we have analyzed the performance impact of the multi-level assessment as defined in Algorithms 1 and 2, the result of which is Table 4, which shows that the trust assessment mechanism introduced a minimal additional overhead and the TEAHR approach is feasible without significant performance degradation. The multi-level trust assessment mechanism helps in detecting and mitigating anomaly nodes, but it is vulnerable to strategic spoofing attacks where malicious nodes can be able to manipulate trust metrics to evade anomaly detection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Computation Time (sec) & 0.006 \\ Memory Usage Before (MB) & 2.60 \\ Memory Usage After (MB) & 2.53 \\ Overhead (MB) & 0.076 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multilevel trust assessment performance analysis. Figure 10: Successful deliveries, total latency, total packets sent, pfr, lt, ec, nl, and roh. Table 5 provides a range of measures related to spoofing attacks and how these attacks affect network performance and integrity. num_nodes represents the total number of nodes in the network. num_anomaly represents the number of nodes that have been designated as anomalies. These are nodes suspected or confirmed to participate in spoofing or other harmful activities. anomaly_percentage defines the percentage of nodes that are malicious relative to the total number of nodes. This offers a normalized view of how prevalent anomaly activity is within the network. pre_attack_excluded count of nodes or connections that were excluded (or filtered out) by the network's security mechanisms before any spoofing attack occurred. pre_attack_malicious_excluded nodes excluded before the attack, this column represents those that were malignant. It is an indication of the initial effectiveness of the pre-attack filtering mechanisms in catching malicious nodes. pre_attack_detection_rate is a percentage that illustrates the effectiveness of detection mechanisms before an attack. post_attack_excluded refers to the number of nodes (or connections) that were excluded after the spoofing attack was underway. post_attack_malicious_excluded the nodes after the attack begins; this column counts those that were identified as anomalies.