# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 1 Introduction
### Related Work  
In WSNs, data transmission follows the preset rules and standards. A routing protocol is responsible for selecting the best path between any two nodes in WSNs. At present, researchers have thrown a diversity of routing algorithms from different points of view. A WSN node is usually low-cost and equipped low-capacity battery. It is hard to replace the battery after the node was deployed. Energy routing protocols have always held excellent research potential. There are some traditional routing algorithms, such as the Maximum Residual Energy algorithm (MRE) [13], the Minimum Path-Energy Consumption algorithm (MPEC) [14], and the Minimum Transmission Energy Consumption algorithm (MTEC) [15]. The MRE algorithm takes residual energy of each node as the routing standard and forwards the data to the node with more residual energy. The MPEC algorithm constructs the path with the minimum energy consumption from the data source to the anchor, cutting down the end-to-end transmission energy. The MTEC strives to reduce the energy of transmission and selects the node with the least single-hop energy consumption to relay data. Although the routing protocol considers the influence of the energy factor, the performance of these traditional algorithms is more prominent in terms of some aspects of network performance. However, this causes other aspects to be unacceptable because of a remarkably one-sided pursuit of energy. For example, the MRE algorithm constructs a longer routing path to bypass low-energy nodes and extend the network lifetime. Therefore, it can deteriorate the mean energy consumption and delay in wireless sensor networks. Therefore, different from the above traditional energy routings with a single consideration, some improved algorithms are proposed in [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Among them, the authors of [16; 17; 18; 19] propose a routing selection strategy combining the residual energy and nodes distance. In [16], for the limited energy problem and as the nodes around the sink consume energy faster in the existing routing algorithms, the authors propose a routing algorithm for dynamically adjusting the forward angle according to the residual energy. The algorithm introduces the residual energy and the nodal distance into an ant-colony probability transfer function to avoid premature death of critical nodes. In [17], the paper designs a routing algorithm depended on residual energy and communication cost between nodes. This algorithm takes full account of the remaining energy of all sensor nodes for cluster head selection and competition and selects the optimal node as the cluster head. Then a sensor with a small communication cost and a large amount of remaining energy will be used as the next hop to achieve energy balance. In [18], the authors propose a Clustering Protocol based on the Residual Energy Level (CPREL) that can effectively balance the energy dissipation of all network nodes and expand the network lifetime. In [19], the authors propose a type of energy-efficient clustering routing strategy based on Low-Energy Adaptive Clustering Hierarchy (LEACH). This strategy considers the level with two different nodes; namely, it selects the normal nodes with less energy as the cluster members and takes nodes with more remaining energy as the cluster head. At the same time, the strategy seeks the path to anchor with the smallest distance. To summarize, these algorithms take notice of the residual battery energy of nodes in the routing decision-making process and try to use high-energy nodes to forward data to extend the network lifetime. Meanwhile, the introduction of the distance factor can avoid excessive energy consumption and a long path of data sending nodes to a certain extent. Here, there are also some strategic routing algorithms based on adjusting the node transmission distance to achieve the purpose of directly decreasing the node energy decay speed in [20; 21; 22; 23].