## 2 Related Works
It is presented in two modes, including fixed and mobile nodes. SMTrust finds trust scales and examines whether these scales are suitable for enhancing security in RPL. The authors combine SMTrust and RPL to optimize the network performance because this trust-based approach identifies and isolates malicious nodes. The performance of this protocol has been evaluated in accordance with topology stability, packet loss, throughput, and consumed energy. This evaluation proves its success in comparison with other methods. In [38], a deep learning (DL)-based trusted routing approach is presented for the industrial IoT (IIoT). This network suffers from vulnerabilities at different levels. Thus, the learning process must be reliable. The authors have trained an adversarial model to identify the desired attacks in RPL. Therefore, a trusted learning model is established. Moreover, a generative adversarial network classifier (GAN-C) is created using GAN and support vector machines (SVM). The performance of GAN-C is analyzed based on SVM. In this technique, real data and noise data are separated to identify an attack. Then, different attacks are assigned to the corresponding classes in RPL to prevent their effect on the network performance. This supports two deep learning features, namely a parallel learning and detection model. When various attacks are detected timely, it reduces the packet loss and latency in the data transmission process. The parallel model has been analyzed, and decentralized and centralized attack detection systems are compared to the RPL network. GAN-C, which is a parallel model, has a significant reduction in training time. In [39], a trust-based secure energy-efficient routing scheme (TBSEER) is offered. It acquires the total trust based on three scales, namely direct trust, indirect trust, and energy trust. This technique deals with black hole, Grey hole, sink hole, and flooding attacks. It defines an adaptive penalty mechanism and a volatilization scale to quickly identify the abnormal nodes in TBSEER. In this mechanism, nodes only obtain the direct trust, and the task of BS is to calculate the indirect trust. This lowers the consumed energy caused by repeated computations. Finally, CHs must find secure multi-hop paths according to the total trust to prevent the wormhole attack. The experiment results confirms that TBSEER consumes low energy in the network, quickly identifies abnormal nodes, and resists common attacks. Table 1 compares various security techniques mentioned in this section and emphasizes their strengths and weaknesses. Our research studies show that many researchers have separately studied communication security and energy efficiency in IoT networks but energy-efficient secure methods have received less attention. Robust security mechanisms, for example CBBMOR-TSM [32], SPRL [34], SecTrust-RPL [35], SMTrust [37], and GAN-C [38], consume a lot of energy. In this case, IoT nodes suffer from limited resources, especially energy. For this reason, energy-efficient secure routing protocols such as EEMSR [29], MCEAAACO-QSRP [31], ETERS [33], EHTARA [36], and TBSEER [39] provide security and energy efficiency simultaneously. Thus, we propose a cluster-tree-based energy-efficient secure routing protocol using the dragonfly algorithm (CTSRD) in IoT networks. This approach considers network security and energy efficiency simultaneously. CTSRD is a hierarchical routing algorithm, which includes two operations: secure clustering (T-Clustering) and a dragonfly algorithm-based routing tree (DA-Tree). Furthermore, CTSRD provides a distributed and lightweight trust mechanism called weighted trust (W-Trust) to evaluate the trust level of IoT nodes in the network.