# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
## 2 Related Studies
### Trust Models and Their Classification  
Recently, numerous schemes for trust management and reputation have been proposed, such as e-commerce, web-based services, peer-to-peer networks, and WSNs. It is essential to conduct a review of the most cutting-edge technology (TM) schemes that have been offered for WSNs to gain an understanding of the current status of the field and outstanding research concerns. In Figure 2, we present some proposed taxonomy schemes, comparisons, and TM schemes. The probability trust model features are designed for security and trustworthiness in WSNs. Trust models help in assessing the trustworthiness of the sensor nodes and the data they transmit, which is crucial in environments where sensor readings are used to make critical decisions. They rely on probability theory to estimate the trustworthiness of each Node in the network, including parameters such as its previous behavior, recommendations from other nodes, and data provision quality. It is assumed that the sensor nodes will perform peer trust monitoring of each other, which represents a for this problem under a probabilistic trust model. In a probabilistic trust model, which characterizes the usual approach to the issue in which each sensor node has its own monitoring trust score, it is expected that these sensors will behave or function correctly. This score is updated with time as the Node interacts with the network and the rest of the other nodes. The trust scores, if appropriately managed, can be used to decide whether a node is good enough for use in critical tasks or not, and if yes, then what type of queries should be answered by that particular Node while excluding others from the network based on their low trust levels. The resource-constrained nature of sensor nodes (e.g., power, memory, and computational capacity) is one of the most challenging barriers to developing trust models in WSNs. Thus, trust models should be light and efficient when employed on WSNs. This kind of probabilistic trust model is beneficial in situations where we have to make decisions on their behavior or the surroundings under uncertainties. It has a probabilistic strategy, and this makes the models capable of providing some estimation for Trust throughout the network, which is also vital while creating more secure WSNs. In WSNs, trust management typically relies on probabilistic models that use sensors' behavior and interactions with other nodes in order to estimate the probability of the credibility for every Node. [PERSON] et al. (2015) proposed a trust model based on the Bayesian and entropy trust models. The Bayesian trust model updates the value based on the behavior of observed nodes and utilizes Bayesian inference. \[T_{new}=\frac{T_{old}\times P+S}{T_{old}+N} \tag{1}\]  
\(T_{new}\) is the new trust value; \(T_{old}\) is the old trust value; \(P\) is the prior trust value; and \(S\) is the number of successful interactions. \(N\) is the total number of interactions. If we want to express the uncertainty in the Trust of some node, then this will be entropy. Therefore, lower entropy on the confidence of a node indicates that it is more reliable. The Trustfulness of a node, using Equation (2): The entropy (H) is defined based on the trust value in each Node as follows:  
\[H=-\sum_{i=1}^{n}p_{i}logx_{2}p_{i} \tag{2}\]  
where \(p_{i}\) is the probability of the \(i\)-th outcome, and \(n\) is the number of possible outcomes. WSNs warrant a trust management regime to ensure that the information collected and disseminated by nodes is accurate and reliable. A weighted trust model in WSN is used to evaluate and govern a node's credibility, which reserves its activities and interaction. The most appropriate model for this version is one that consists of components, each rated according to core and complementary networks. Trust metrics, trust evaluation, trust update, and trust-based decision-making are the significant components of the weighting trust model.