## 2 Related Work  
### Cluster-Based Routing Protocol  
The energy-efficient design of WSNs in IoT systems is complicated owing to the energy constraints of sensor nodes. The energy efficiency of WSNs is an important issue in the evaluation of IoT performance. To achieve energy efficiency for sensor nodes, many routing algorithms based on clustering have been proposed [21; 22]. Figure 2: Reverse transmission of sensor nodes. LEACH is a well-known cluster-based routing protocol for WSNs [23]. WSNs select CHs at regular intervals, and each sensor node has the same probability of becoming a CH. The sensor node transmits data to the BS through the CH, thereby increasing the energy efficiency of the sensor node to extend the lifetime of the network. Many protocols have been proposed that improve performance based on LEACH. A clustering-based routing algorithm in IoT-aware wireless mesh networks configures clustering by selecting adjacent clusters to avoid flooding the network [24]. It also increases network throughput by analyzing undistributed routes. The k-means clustering algorithm proposes an optimized cluster structure using the weighted function of the k-means clustering algorithm [25]. The weighted function evaluation determines whether to split or merge the cluster structure. The CH conserves energy in the network by merging data. The dynamic CH selection method for wireless sensor networks uses an estimation algorithm to overcome the imbalance in energy consumption by electing a new CH based on the ratio of the remaining and average energy of the node on the network from the dead time of the node [26]. In this study, the monitoring area was distributed through a mesh algorithm. The relocation of the area after the sensor node's death in the monitoring area should be considered. Improved energy-efficient CH selection in WSNs (IEECHS) improves the CH selection technique in LEACH and selects dual CH [27]. The first CH is used for data transmission, and the second CH is used to deduplicate data to reduce the communication cost of the network. The use of two CHs in this study shows better performance than LEACH. However, it may affect data transmission delay. A related study [28] proposed a distributed CH rotation mechanism to balance CH energy consumption. To avoid the energy hole problem of associated with a CH located far away from the BS, the energy efficiency is improved by designing a dynamic multi-hop routing algorithm for CH nodes using a proposed distance and energy recognition cost function. A hybrid hierarchical clustering approach (HHCA) is a proposed clustering method based on a three-layer structure [29]. It uses a centralized grid for head selection at the top level and distributed clustering for head selection at the lowest level. It uses a clustering approach similar to LEACH and re-selects grids and clusters in each round. Partitioned-based energy efficient-LEACH (PE-LEACH) is a proposed method to reduce energy consumption by avoiding multi-hop communication between the CH and BS of sensor nodes with mobile BS [30]. The area of the WSNs is divided into a number of quadrants, and the BS will move according to the quadrant. The CH is selected based on the residual energy of the node. IoT-LEACH (I-LEACH) is a proposed method to extend the energy efficiency of WSNs by extending the term of the CH if the residual energy of the elected CH is above a threshold [31]. The proposed method shows better performance than LEACH, but it may cause problems associated with energy imbalance in the CH. Learning automata-based multilevel heterogeneous routing (LA-MHR) is a proposed S-model-based LA for the election of CHs that uses single-hop and multi-hop transmission to increase energy efficiency [32]. The energy-aware routing protocol (EAP) compares the average residual energy of all neighboring nodes in the CH selection and uses a new clustering parameter in which the sensor node with the highest residual energy becomes the CH [33].