## 2 Related Work  
In this section, the latest key research which has already been conducted to address the issue of routing will be critically reviewed and their findings analyzed and presented in a critical analysis table at the end of this section. Several studies propose mechanisms to detect and prevent wormhole attacks, but most of them require excessive resource utilization at the node level. In one study, a new approach called the PCC-RPL (Parental Change Control RPL) was introduced to manage changes made by parents to IoT devices using the trust concept. This model's calculations are carried out at the node level of the topology [15]. In another study, a model was developed for enhancing RPL security and mitigating vulnerabilities to WSN-inherited and RPL-specific attacks. The researchers utilized machine-learning techniques, specifically supporting vector machines for data classification, to create a secure and improved version of the RPL for enhanced security [16]. One study identified parameters to monitor network flow and evaluation [17]. In another study, a mechanism model was proposed to detect and prevent wormhole attacks using the AODV protocol [18]. Another proposed a monitoring mechanism in which softwarized approach was used to detect  
Figure 3: Wormhole Attack. RPL based Attacks [19]. Another proposed a trust management framework that calculates and analyzes the trust of devices or nodes using MADM, and Evidence-Based Subjective Logic (EBSL) [20]. Another system is proposed as a secure version of the RPL that uses support vectors and a level-based approach to detect Sybil attacks [21]. Another proposed methodology for IoT security using leveraging blockchain technology [22]. However, our study proposed a \"Subjective Logical Framework (SLF)-based Trust Model for Wormhole Attack Detection and Mitigation in IoT applications\" to calculate trust values between trusted networks while saving resources at the node level. In another study to secure against Rank and Sybil attacks, the RPL routing protocol incorporated the Secure Trust (SecTrust) trust system. The SecTrust-RPL utilizes a trust-based mechanism to identify and contain attacks, all while optimizing network performance [23]. The study is analyzed using key parameters related to our study. The details are mentioned in Table 1 with their respective citations. Many of the cutting-edge mechanisms have often overlooked the resource limitations inherent in the RPL. Contemporary approaches have typically tasked ordinary nodes with implementing security measures, and while these methods have proven effective, they have also led to a significant drain on node resources. Therefore, this study aims to enhance and compare the results of the PCC-RPL methodology, which is the most recent technique to mitigate and detect wormhole attacks in RPL-based IoT devices. The PCC-RPL methodology worked well in terms of better network lifetime, attack detection rate, average residual energy, packet loss ratio, and attack detection time compared with other proposed methodologies. However, the node-level calculation and evaluation of trust metrics in the PCC-RPL resulted shorter network lifetime and high energy consumption by the nodes in comparison with SLF-RPL proposed method. However, a novel trusted model using routing can be utilized for attack detection [24]. Therefore, this study proposes a methodology with a controller that calculates and evaluates results to cover up the gap. While the proposed methodology is based on device  
\begin{table}
\begin{tabular}{c c  
layer, sink layer, controller layer, trust model, trust calculation, and trust model evaluation, the selection of nodes for routing and the isolation of the attacker node are discussed in detail. In this section, the proposed SLF-RPL methodology is presented in detail along with the layer discussion, followed by a designed architectural diagram, flowchart, and trust calculation equations.