## 2 Related Work
The proposed model computes the trust value of each node using the direct trust computation method. Similar to the methodology in [22], a node may be identified as a malicious node if it fails to participate in a communication process within a specific time due to network issues. When a node is not communicating with other nodes, its number of interactions decreases. Consequently, the public friend count factor is also likely to decrease for that node. [PERSON] et al. proposed a lightweight trust-based routing protocol to mitigate black hole attacks in mobile ad hoc networks (MANET) [24]. Instead of opting for the shortest route as in traditional routing, preference is given to the most trusted path. Each node maintains the trust value for its neighbor nodes, indicating the level of trust they have in these neighboring nodes. In this model, the trust value for a path is calculated based on the activities completed by interlinked neighbor nodes. However, a drawback of this model is the excessive consumption of network energy, as each node in the network computes the trust value for its neighbor nodes. [PERSON] et al. [25] focused on integrating a trust model into the RPL routing protocol. During the construction of the network topology, several crucial steps were implemented to secure the network from both external and internal attacks. The model emphasizes the computation of both link and node trust through a multi-dimensional approach. The evaluation of trust involves four stages: information gathering; trust composition (integrating both node and link trust); trust database creation; and trust application. Trust calculation is matrix-based, with influencing factors including the level of energy, positive interaction, and node satisfaction. When compared to existing approaches, their model demonstrates greater accuracy in detecting and isolating malicious nodes. However, challenges persist regarding network delays. As discussed above, the existing schemes employ various methods for reporting trust scores in the identification and isolation of malicious nodes within the network. In some of the schemes, if a specific node's trust score is reported and found to be below a predefined threshold, immediate isolation of the node follows. Conversely, in other approaches, if the node's trust score remains below the predefined threshold for a specific duration, the node is then isolated. The issue with the former approaches is that the trust value of a node can be adversely affected by having malicious neighbor nodes or encountering different network issues, rendering it an unreliable approach for isolation. The latter methodologies also present multiple challenges. There is a possibility that the node's trust value may not be sufficient within a short time span. Moreover, extending the time span for the root to gather more feedback results in an increase in computation cost and overhead. Such escalation in computation cost and overhead renders the mechanism unsuitable for resource-constrained nodes in an IoT-based network. Furthermore, with greater numbers of feedback, more memory is consumed at the root or CH. Therefore, a method needs to be devised to reduce the computation cost for nodes in the network and minimize overhead. It is crucial not to isolate a node from the network solely based on the immediate feedback received by the root if its trust score is below the threshold. This is because frequent variations in topology can also compromise the stability of the network. Table 1 compares the existing schemes for various parameters.