## 2 Related Works  
In [29], an energy-efficient multi-level secure routing (EEMSR) technique is proposed to extend IoT applications and reach the best performance. EEMSR has simultaneously focused on two important issues in IoT, i.e., energy efficiency and security. EEMSR utilizes the clustering technique because it is a useful solution for increasing energy efficiency and enhancing scalability. In addition, the authors used two techniques, namely the analytic hierarchy process (AHP) and improved genetic algorithm (GA), to specify the exact weight of IoT nodes for selecting CHs and determine the optimal path between CHs, respectively. In addition, EEMSR provides several trust levels (i.e., data perception, data aggregation, and connection levels) to protect the network against various attacks. The experiment results prove that EEMSR provides energy efficiency and network security. In [30], the authors analyzed the consumed energy within clusters in heterogeneous WSNs and proposed the E-BEENISH routing approach. This scheme defines the CH selection process in accordance with the weighted selection probability specified for each node. This probability is dependent on remaining energy and the distance between the base station (BS) and the node. Additionally, E-BEENISH investigates the effect associated with the heterogeneity of the energy status of nodes. E-BEENISH picks out CHs based on various scales such as the distance parameter, the average network energy, and the remaining energy. Furthermore, this method utilizes a normalized weighted value to prolong the network lifetime. According to the simulation results, E-BEENISH has improved the system lifetime compared to existing clustering protocols. It is a critical parameter for many applications. In [31], the authors presented a QoS-based secure routing approach called MCEAACO-QSRP in IoT. This scheme initializes the primary population based on a chaotic optimization strategy. The purpose of this technique is to enhance its diversity. As a result, the algorithm is not trapped in the local optimum. For dynamic pheromone tuning and updating, MCEAACO-QSRP supports an adaptive optimization strategy to effectively accelerate the convergence of the algorithm by lowering iterations. This optimizes energy consumption. Furthermore, MCEAACO-QSRP introduces a secure QoS-based routing framework based on a trust model to protect against black hole attacks. It computes the trust value with regard to the successful behavior of nodes when exchanging (i.e., sending/receiving) data packets. Finally, it is used to detect normal and abnormal nodes. The experimental results confirm that MCEAACO-QSRP can simultaneously optimize security and energy consumption. This meets multiple QoS requirements, for example, it lowers latency and provides reliable services to users. In [32], a chaotic bumble bee mating optimization (CBBMO) algorithm is suggested to securely transfer data packets. The security is guaranteed using a trust structure called CBBMOR-TSM. The authors combine the chaotic concept with CBBMO to accelerate its convergence rate. In the first step, the trust structure is obtained from both indirect and direct trusts. This structure is applied to specify the trust value of IoT nodes. As a result, this structure helps CBBMOR-TSM detect and isolate malicious nodes. Moreover, this structure can be used to specify suitable and secure paths for transmitting data packets. According to the simulation results, this method can lower the delay and consumed energy and enhance the data transfer rate when there are hostile nodes in the network. In [33], an energy-aware trust-based efficient routing scheme (ETERS) is offered for a clustered network. This approach provides a multi-trust solution, including communication, energy, and data trusts to deal with various attacks, for example, badmouthing, Sybil, Grey hole, on-off, and black hole. Furthermore, ETERS presents a CH selection process called ECHSA, which builds a load balance between all CHs.