Wireless Sensor Networks Optimization Using Machine Learning to Increase the Network Lifetime

Abstract

The wireless sensor networks optimization is the main approach for reducing energy consumption and for prolonging the network lifetime, by using the machine learning algorithms. The optimization of WSN achieves the clustering of nodes, the data aggregation of nodes, and reducing energy consumption. In this paper, more than 100 nodes are used to optimize networks with less energy consumption and less time requirement. This number of nodes gives the feasibility to the network. The energy consumption is to be reduced using the APTEEN protocol with threshold energy. The threshold value can be increased to 70%, and due to which the energy can also be reduced to 55%. The lifetime of the network happens when the dead node occurs in the system, where the minimum number of dead nodes exists to increase the network lifetime and to avoid packets loss. So, to reduce the dead nodes, the use of a machine learning algorithm is proposed. The results show the analysis of APTEEN protocol with a threshold and genetic machine learning algorithm which gives energy efficiency and improved network lifetime.