Energy-efficient WSNs Model Based on Fuzzy Logic Clustering Protocol and MOML

The problem of energy consumption has been restricting the further development of wireless sensor networks (WSNs). The existing clustering protocols rely too much on precise mathematical models, lack flexibility, and consider a single factor, which is inconsistent with the actual application scenario of WSNs. In this paper, a clustering protocol based on fuzzy logic is proposed, which takes into account the network structure, node density and node energy status, and realizes the reasonable selection and energy distribution of cluster head nodes. At the same time, the Multi-Objective Meta-Learning (MOML) algorithm is combined. An adaptive energy consumption management scheme (hereinafter referred to as EE-WSNs FLCP&MOML) is designed. By managing and optimizing the machine learning model, intelligent energy consumption management of WSNs is realized, and energy efficiency and network stability are improved. The experimental results show that the energy-saving model can effectively reduce the energy consumption of WSNs and improve the stability and reliability of the network. The research results of this paper can provide new ideas and methods for energy saving optimization of WSNs.