# A Node Density Control Learning Method for the Internet of Things
### Related Work  
In some early work, an optimized coverage control method for joint-aware wireless sensors was proposed in [5]. The solution process of maximum seamless coverage when three-node joint coverage was given in this method. On the one hand, the method for solving the node coverage quality expectation and the coverage determination method when compared to neighbour nodes was verified by the probability correlation knowledge when the sensor nodes were covered in the monitoring area. On the other hand, when there was redundant coverage, a scaling factor was introduced to complete the calculation process of redundant coverage when any sensor node was in redundant node coverage, thereby completing the control. This method can effectively suppress the rapid consumption of node energy. However, the control completion time was too long.  
We have also seen a WSN monitoring node grouping control method based on evolutionary game theory proposed in [6]. Firstly, the non-cooperative game model was constructed with the utility function of the game as the objective function by mapping the search space of the optimal node set to the policy combination space of the game. Secondly, the target optimization was realized by the Nash equilibrium analysis and the balanced disturbance recovery process; Then, the clustering algorithm was designed to optimize the node set to form the corresponding group to participate in the final positioning; Finally, the control was done based on the final positioning result. The method avoided the problem of high energy consumption of nodes in the common coverage area of multiple radiation sources, however, the control power consumption was large.  
In [7], a multi-source fault-tolerant topology node control method in a WSN was proposed. In a heterogeneous wireless sensor actuator network model consisting of a large number of computational, energy-constrained sensor nodes and a small number of better-performing actuator nodes, any sensor nodes must be guaranteed and executed to minimize the total path power consumption. There were at least k disjoint paths between nodes, at the same time; the nodes with better weights were selected. In this way, the connectivity of the network was not affected when any \(k-1\) nodes fail, thereby effectively completing the node control. However, the time taken for the method to control the completion was too long.  
Recently, a three-dimensional WSN topology control method based on node degree estimation was proposed in [8]. According to the node degree factor of the degree of topology change, the node degree estimation model for evaluating the comprehensive performance of the network was constructed in this method. At the same time, the topology control algorithm based on the model was proposed. Topology creation and optimization were achieved by arranging sensor nodes, creating network topologies, generating data transmission links, and correcting node transmit power. The method had a better overall performance of topology control; however, the control power consumption was large. Therefore, a density control method of self-learning based wireless sensor mobile node is proposed here and applied to the Internet of Things.