# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 2 Related Work
### State-of-the-Art
Table 1 presents the summary of recent literature related to routing attacks in IoT. In the existing research works, a number of trust models have been proposed for secure routing in IoT. They lack some features, such as consideration of IoT node mobility, heterogeneity in IoT environments, adaptability to IoT networks and routing, and consideration of RPL-specific attacks. Furthermore, trust dynamics and network performance are not taken into account in some of the presented solutions. However, some of the papers focus solely on network performance and routing behavior, neglecting to address routing attacks and security concerns. Moreover, critical security attacks, particularly routing attacks in RPL, are not evaluated in some trust-based network security solutions. Traditional security solutions are insufficient to provide substantial security for smart applications. The use of trust models in IoT networks can reduce the uncertainty factor for nodes interconnection. The trust models proposed for IoT lack some critical features, including heterogeneity, dynamicity, and resource-constrained nature of IoT [43]. Some trust models involve computationally expensive IDS-based approaches for attacks detection, which require a hardware security chip embedded in the device [15]. As a result, a comprehensive trust model needs to be developed that considers the attacks with high impact such as Blackhole and Rank attacks, node mobility in an IoT environment, and the selection of appropriate trust metrics for an effective security solution for RPL-based IoT networks. Our proposed model differs from the above-mentioned secure routing protocols in the selection of metrics, particularly involving mobility-based trust metrics along with direct and recommended trust. In terms of trust metrics, most of the existing methods use success rate, historical observations, and feedback which is also referred to as recommended trust. In addition to these metrics, SMTrust also employs the energy level, mobility, and the location and link stability. Table 2 shows the summary of trust metrics as employed by existing trust-based models. Secondly, in SMTrust, the malicious nodes are detected and isolated using a trust evaluation mechanism based on simple trust metrics calculations that do not require extra hardware to be embedded for security module, as in [15]. Additionally, SMTrust is evaluated for three different scenarios, based on static nodes, mobile sender nodes, and mobile sink node. To the best of our knowledge, only one study, DCTM [16], considers the mobile sender nodes, and there is no study in the literature that considers sink node mobility in an IoT environment. Hence, SMTrust is proposed and evaluated for static and mobile nodes, including mobile sink node. The proposed protocol is proved to outperform in terms of topology stability, throughput, packet loss rate, and power consumption, comparing with recent trust-based routing protocols, for example, SecTrust [14], MRTS [15], and DCTM [16]. From the literature review, the types of routing attacks considered by recent research works, and the high impacts of Rank and Blackhole attacks, among others, in RPL, justify their selection for this research study. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Technique(s) and Description** & **Attacks Considered** & **Mobility** & **Research Gaps** \\ \hline \multirow{4}{*}{[14]} & Using the fuzzy logic-based approach for threshold-based trust & \multirow{4}{*}{Rank; Sybil} & \multirow{4}{*}{✘} & Lack of node mobility, consideration for recommendation uncertainty, evaluation for colluding attacks, energy consumption, and E2E delays; Packet loss rate is significant. & \multirow{4}{*}{✓} \\  & A dynamic, comprehensive, multidimensional, hierarchical trust model.