## 2 Related Work
It examines the history and state of connections between nodes to assess trust metrics among them. It establishes connections between SNs based on past connections, RE, location, and the state of the node. This trust-aware routing scheme offers improved security and optimizes metrics associated with unique features of the network. The proposed mechanism offers an improved network lifetime when compared with benchmarked schemes. [PERSON] et al. [20] proposed the Trust-aware routing protocol using the Adaptive Genetic Algorithm (TAGA) to overcome common routing attacks and also reduce the amount of energy consumed. This protocol determines total trust based on direct trust involving volatilization and dynamic penalty factors and indirect trust using filtering schemes. It offers a threshold function for choosing ideal CHs based on dynamic variations of nodes' total trust and RE. It employs REs with dynamic crossover and mutation probabilities for finding ideal secure routing for CHs. The proposed scheme reduces the amount of packets dropped by malevolent nodes, thus reducing their impact and offering improved energy efficiency. [PERSON] et al. [21] propounded the Cluster-based Trusted Routing scheme using Fire Hawk Optimizer (CTRF), which focuses on ensuring restricted energy. It uses a weighted trust mechanism (WTM) based on interaction between sensor nodes. It considers exponential coefficients for trust factors, including the energy state and weighted reception rate and redundancy rate, so that trust of nodes is increased or reduced exponentially depending on friendly or hostile behaviors. It produces a Fire Hawk Optimizer (FHO)-based clustering scheme for choosing CHs from candidate sets that include nodes with RE and trust more than mean RE and trust of all nodes. It uses a cost function based on CH location, energy, distance from the CH to the BS, and the size of the cluster. It determines routing paths between clusters using reliable routing and uses them to transmit data from CHs to BS. It builds routes based on energy, the quality and reliability of routes, and the quantity of hops. It offers improved performance in terms of throughput, energy, packet loss rate (PLR), detection ratio, accuracy, and latency. [PERSON] et al. [22] proposed the Trust-Aware Clustering Technique based on Improved Rat Swarm Optimizer (TACTIRSO) for an intelligent transportation system (ITS) using WSN. This uses RSO for efficiently selecting trusted CHs. It forms a fitness function based on the RE and trust of nodes. It uses an exponential moving average model to dynamically modify the pre-defined threshold based on the network state. For improved performance, it applies diverse local search approaches along with a trust- and energy-based scheme to initialize the rat population. The proposed mechanism offers better results in terms of energy efficiency, the selection of reliable nodes, the mean network lifetime, and the network stability. [PERSON] and [PERSON] [23] proposed a Trust Cluster-based Secure Routing (TCSR) framework by using optimization algorithms. This framework forms clusters by using the Modified Tug of War Optimization (MTWO) algorithm that offers load-balanced clusters to support energy-efficient transmission of data. It selects ideal CHs based on the Received Signal Strength Indicator (RSSI), the rate of congestion and data loss, and throughput. It optimizes parameters using the Butterfly Optimal-Deep Neural Network (BO-DNN) that offers improved security in the selection of CHs. It uses lightweight signcryptionfor encrypting data that are transmitted between nodes during the transmission of data. The proposed model aids in estimating the trust of every path to help the source in choosing the most reliable one. Nodes enhance reliability as well as security by sustaining the reliability component. [PERSON] and [PERSON] [24] focused on determining an ideal path with a maximum reliability level. The proposed fuzzy and reliable clustering scheme is propounded for enhancing energy efficiency as well as security.