# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 2 Related Work  
With the rapid evolution and development of sensor networks, the routing protocols of WSNs have attracted the attention of many scholars. However, the clustering routing protocol is a more efficient way to reduce the energy consumption of sensor nodes and lengthen the life cycle of networks relative to other typical routing protocols [28; 29]. The most famous clustering routing protocol among homogeneous WSNs is the LEACH protocol [14]. In LEACH, the CHs are randomly selected, and the sensor nodes are periodically rotated into CHs. The network energy consumption is evenly allocated to each sensor node to lengthen the network life cycle and enhance the network throughput. However, many improved protocols based on LEACH have been proposed, such as LEACH-B [30], LEACH-C [17], CL-LEACH [31], H-LEACH [32], O-LEACH [33], etc. Vice-CH-enabled centralized cluster-based routing protocol (VCH-ECCR) [19] is a routing algorithm. The VCH-ECCR reduces the clustering frequency by selecting two-stage vice CHs, thereby reducing the overhead caused by clustering. The number of CHs is updated according to the number of surviving nodes in each round in the network. The experimental results illustrate that the protocol is better than that of traditional routing protocols. A fuzzy logic-based energy-efficient clustering for WSNs based on minimum separation distance enforcement between CHs (FL-EEC/D) [20] was proposed. In FL-EEC/D, a fuzzy logic model is proposed for the selection of CHs. When using this model for CH selection, the residual energy, compaction, density, position suitability, and distance from the BS are used as the main parameters to select a better node as the CH. The life cycle of the FL-EEC/D is raised relative to LEACH. The above protocol performs well in homogeneous WSN, but it performs poorly in HWSNs. On the basis of the above routing protocols of homogeneous WSNs, many routing protocols of HWSNs have been proposed to further lengthen the life cycle of the network [34; 35]. A SEP [21] for the HWSNs was proposed by [PERSON] et al. The different initial energy levels of nodes indicate that this network is a multi-level HWSN. A two-level HWSN is defined by the SEP; namely, advanced nodes and normal nodes. The SEP protocol has different threshold formulas for advanced nodes and normal nodes. Compared with normal nodes, advanced nodes have a higher probability of becoming CHs, which reduces the remaining energy deviation between ordinary nodes and advanced nodes, thus better lengthening the life cycle of the network. An M-SEP [22] for HWSNs was proposed in 2015. The residual energy of the current node and the average energy of the network are considered in the M-SEP protocol, so that nodes with high residual energy have a higher probability of becoming the CH. The transmission rate of packets and the lifetime of the network are improved through this technique. A P-SEP [23] was proposed in 2017. In the selection of CHs of each round, the protocol controls the inherent randomness and deploys the threshold of heterogeneous energy to prevent low-energy nodes from becoming CHs in the next round. This approach results in improved performance in the network's lifetime. [PERSON] et al. proposed an energy-centric cluster-based routing protocol (ECCR) [11]. In this protocol, a predefined static cluster is proposed, whose purpose is to reduce the control information overhead in the cluster formation problem. Next, a gatekeeper is introduced, which carries the rank information of nodes with local data. The future cluster director is transferred to the role of the former cluster director of the previous round. This reduces the cost of CH elections over the entire network life cycle, thus extending the network life cycle. In [25], a DEEC for HWSNs was designed. This protocol is a multi-level heterogeneous network protocol. DEEC chooses the CHs by defining a threshold formula, which is influenced by the remaining energy of the node and the average remaining energy of the network.