## 2 Background and Related Works
To extract basic trust traits, the authors used a numerical technique. In addition, unattended machine learning methods were applied: k-meant clusters to categorize interactions as reliable and untrustworthy. The optimal size of the cluster was computed using the Elbow technique and the main component analysis was further employed to decrease the training matrix. Features have been standardized to provide sequence data in the range of 0 and 1. The radial function kernel is being used to reach the nonlinear limit that can differentiate reliable and unreliable events and learn the optimum strategy to integrate trust characteristics to acquire the last trust value. In addition, the combination of WSN, different security and double locking systems was used and the Trusted and Secure Routing (TSRS) was proposed in [36]. TSRS has been able to ensure that the path takes place, combating some internal threats such as black hole attacks and selective redirection attacks based on the commitment trust. This technique may integrate force selection and give a secure path by applying the trust and cuckoo search algorithm to distinguish the trusted path. The system also guarantees the life of the network. Furthermore, authors in [37] developed an Energy Trust Model (ETM) to address the problems connected to relay nodes' dependability and data sensing secrecy. Finding a trustworthy connection and secure data transmission have also been implemented by the trust-based Secure Directed Diffusion Routing (TSDDR) protocol. Furthermore, the proposed approach ensured the corresponding energy usage and data confidentiality. Likewise, the authors in [38] proposed a fuzzy-based hierarchical trust management algorithm. The nodes that are securely joining the cluster are established utilizing an intelligent and protected fuzzy clustering technique using a Balanced Weight Sub-cluster formation (BWS). The BWS procedure uses the scheme Fuzzy S-means for CH-identification. The CH is liable for trust-based pathways and the utilization of energy. The authors in [39] suggested a Trusted Moth Flame Optimization and Genetic Algorithm (TMFOGA) for CH selection. The algorithm depends on the density of a node, residual energies, packet forwarding progress, distance and transmission latency. However, with TMFOGA, there is a significant drawback that omits its suitability for hostile applications. To make it easier to understand and clarify what we discussed earlier, we have created Table 1 which includes an analysis of all the techniques discussed above. It highlights technology, its main idea, strengths and weaknesses. Based on the studies presented previously, most focus on calculating confidence and the amount of improvement in one part without the other (security or energy). In addition, most did not mention the authentication methods used or even the key management processes. Therefore, in this paper, we will detail the trust calculation process, the CH selection process, authentication methods, key generation based on the trust level, the encryption algorithms used and the adjustment mechanism between trust and the level of encryption that will be used.