# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 1 Introduction
### Related Work  
[PERSON] et al. proposed the LEACH-DBCH (LEACH-Distance-Based Cluster Head) algorithm to relieve the high energy consumption of the classical LEACH algorithm [13]. The LEACH-DBCH algorithm designs a new threshold function in cluster head selection stage. The new function comprehensively involves some factors related to cluster head selection including remain energy of node, distance between node and base station, anddistance between node and cluster head. Simulations show that, compared with the classical LEACH algorithm, the LEACH-DBCH algorithm can effectively prolong lifetime of WSNs. According to requirement of target monitoring quality, [PERSON] et al. propose the LEACH-RARE (LEACH-Reduced Area REporting) algorithm to save energy of WSNs by restricting the number of nodes related to target monitor and the amount of data related to cluster head [14]. Based on the distance from the cluster member nodes to the cluster head, [PERSON] et al. propose a cluster head selection algorithm to prolong network lifetime by optimizing the number of cluster heads [15].  
[PERSON] proposed an efficient adaptive node selection algorithm named ANSTT (Adaptive Node Scheduling for Target Tracking) [16], which combined the network node's perception of moving targets and the relative residual energy of network nodes to develop a working node selection mechanism. It effectively reduced network energy consumption and extended the network life cycle. [PERSON] proposed an efficient and energy-efficient adaptive network nodes scheduling algorithm [17]. By prioritizing the decision function, the algorithm used the nodes with higher value as the working nodes to balance the local energy consumption of dynamic clustering. The node with the highest value became the cluster head, and the particle filter algorithm was used to track the target whose motion model is nonlinear. Research shows that [PERSON]'s algorithm solved the problem of unbalanced network energy consumption, which is caused by the uncertainty of target motion modeling and the randomness of cluster head selection. Moreover, [PERSON]'s algorithm can also effectively schedule network nodes and improve the tracking accuracy of moving targets.  
[PERSON] et al. proposed a node scheduling method based on an improved genetic algorithm. The fitness function of the improved genetic algorithm was constructed by some factors including the least number of nodes, the selected node coverage, and the remaining energy [18]. In addition, this algorithm constructs the set of working nodes by selecting some nodes close to the target, thus leading to reduced number of nodes and enhanced network efficiency. Although the above methods can effectively reduce network resources and balance network energy consumption, some factors including network environment, static/dynamic target, and so on, which were not considered to influence working nodes selection, cluster head selection, energy consumption modeling, and so on. When the wireless sensor network nodes cooperatively track the moving target, the target motion state (for example, the motion trajectory, speed, and direction) will affect the selection of the network working nodes and the network energy consumption.