# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 3 Proposed Studies
### Proposed Algorithm
* Blackhole and Wormhole Attack Prevention
* TEAHR flags nodes dropping packets at an abnormally high rate. If a CH fails multiple integrity checks, it is demoted, isolated, and replaced. In Figure 5 shows a dynamic trust evaluation framework for WSN to detect a malicious node and isolate that node. Each node \(x_{i}\) is evaluated by its cluster head (CH) based on four metrics i.e., energy trust, forwarding trust, consistency trust and behaviors trust. These trusts are combined using the weighted to calculate overall trust, which is being compared with dynamic threshold value. If trust value meets or exceed the threshold value then that sensor node be consider trustworthiness and continue allow them in network. If it is failed below the threshold but not shows malicious behaviors then allow them to be part of network or if shows the malicious behaviors then discard those sensor nodes. These steps will ensure the security and by continuous monitoring the node behaviors. Figure 5: The flow of CH selection. ## 4 Result and Discussion

### Trusted Energy Aware Hierarchical Routing (TEAHR)

Authors [34] demonstrate the effectiveness of the TIOCHR algorithm in enhancing the network lifetime and energy efficiency; it needs to fully explore the potential challenges of implementing such trust-based systems in highly dynamic or adversarial environments where node trustworthiness might frequently change. A dynamically changing WSN experiences node failure, mobility, energy depletion, and varying network traffic. To maintain robustness, TEAHR's trust assessment mechanism must be adaptive across multiple trust metrics. Trust scores are updated continuously, monitoring nodes' actions. If a node's behavior fluctuates (e.g., it forwards packets inconsistently), its trust score is gradually lowered. Nodes with stable, positive behavior receive incremental trust increases. If a node starts dropping packets due to congestion, the system distinguishes unintentional failures from malicious intent by analyzing past behavior. Instead of immediately isolating the node, TEAHR might reduce trust slowly and allow recovery if normal behavior resumes. Furthermore, this study might benefit from a comparative analysis with other trust-based models to establish the specific advantages or limitations of the TIOCHR approach in various real-world applications. In our proposed model, we tried to overcome the drawback of TIOCHR. In Figure 6 green color is base station which gathers all information of the sensor network, black color is cluster head of which is surrounded by the sensor nodes are blue color. Bule line shows that routing from cluster head to based station and dotted line shows the sensor node communication to cluster head. Table 2 shows the notations descriptions for various trust and selection metrics used in WSN for cluster-based architecture. The parameters such as energy, communication, node behavior and other network related factors to find the trustworthiness. Figure 6: Sensor-to-sensor, sensor-to-group head, cluster head to cluster head-to-group head, and cluster head to base station trust and routing. ### Initialization

Each Node in the network starts with an initial percentage of being a cluster head (CH) candidate, called the \"CH probability\" (P\({}_{\rm ch}\)), and sets its status to undecided. The initial P\({}_{\rm ch}\) can be a system parameter based on the size and density. Let P\({}_{\rm ch}\)(i) be the probability of Node i becoming a CH. Each Node I broadcasts its P\({}_{\rm ch}\)(i) along with its residual energy, E\({}_{\rm res}\)(i). The nodes assess their residual energy relative to a reference, which can be the initial energy or an average energy level. This step ensures that nodes with higher energy have a higher chance of becoming a cluster head. Let E\({}_{\rm res}\)(i) be the residual energy of Node i, and E\({}_{\rm ref}\) be the reference energy level, which could be the initial energy level E\({}_{\rm init}\) or the average energy level E\({}_{\rm avg}\) of all the nodes in the network.