# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 2 Related Work
The period taken for the node to become CH varies according to the remaining energy, so the probability of a high-energy node becoming a CH is higher than that of a low-energy node, which lowers the energy consumption between nodes and lengthens the stable transmission period of the network. An energy efficient clustering protocol to enhance the performance of the HWSN, EECCPEP-HWSN [36], was proposed. EECCPEP-HWSN was designed as a three-level HWSN, namely including ordinary nodes, advanced nodes, and super nodes. In the process of CH selection, the initial energy of the sensor nodes, hops and residual energy ofthe sensor nodes during operation are considered. This protocol improves the energy efficiency and stability of HWSN. In recent years, heuristic and metaheuristic algorithms have been applied to WSNs for theoretical research [37; 38; 39]. However, many of the above algorithms have also been applied to clustering routing protocols to optimize the selection process of cluster heads, thus improving the network's life cycle [40; 41; 42; 43; 44; 45]. [PERSON] et al. proposed a special clustering method called energy centers searching using particle swarm optimization (EC-PSO) [46]. First, the protocol selects CHs by dividing node positions according to geometric methods. Then, the particle swarm optimization (PSO) algorithm is used to search the energy center of the network, and the nearest node to the energy center is selected as the CH. Finally, in order to avoid weak nodes becoming relay nodes, the algorithm proposes a protection mechanism with low energy consumption. By combining the above method, the protocol can effectively lengthen the network lifespan. A Genetic-Algorithm-Based Energy-Efficient Clustering (GAEEC) [47] was proposed. The genetic algorithm (GA) is used twice in the protocol. First, in order to evenly select the number of CHs, the protocol uses GA to divide the network into static clusters by distance. Second, the GA is used in the phase of CH selection. However, the selection of CHs is based on the remaining energy of the current node and the total transmission cost, so that nodes with a larger remaining energy have a higher probability of becoming the CH. Thus, the protocol effectively enhances the performance of the network lifespan. A multilayer hierarchical routing protocol (MLHP) [48] was presented. The algorithm divides the sensor nodes into three levels (normal nodes, advanced nodes, and super nodes). At the level of normal nodes, a centralized selection is proposed in which the BS plays an important role in CH selection. In the area of advanced nodes, a GWO route is presented for data transmission, and a distributed clustering algorithm based on cost function is presented for the level of super nodes. The above algorithm makes the MLHP have a longer network life cycle. In [49], an energy-efficient routing protocol for WSNs based on an improved grey wolf optimizer (FIGWO) was proposed. FIGWO improves the searching ability of the GWO optimal solution by considering the fitness value, and a better distribution and more balanced clustering structure of CHs are guaranteed. The transmission distance of the sensor node is recalculated according to the distance from CH and BS, thus reducing the network energy consumption and prolonging the network lifespan.