A Balanced Energy-Efficient Clustering Strategy for WSNs
Yanxia Liang, Songlin Zhao, Xin Liu, Hua He, Xiaofan Zhao, Huan Wang
Abstract
Due to characteristics of small size, passive operation, easy deployment, real-time monitoring, and data collection, wireless sensor networks (WSNs) are widely used in the field of the Internet of Things (IoT). However, sensor networks face challenges related to limited energy and short lifespan. In order to improve the network energy efficiency and extend the lifecycle of WSN, we propose a multifactor competitive clustering routing algorithm based on K-means and weighted interstandard correlation, denoted by MC-CRITIC-KM. First, in the clustering stage, the K-means algorithm is used to cluster the sensor network nodes. Second, in the selection of the cluster head stage, considering factors such as residual energy, distance, density, and load rate, as well as the objectivity of the WSN environment, the criteria importance through intercriteria correlation (CRITIC) weighting method is used to weight the factors. The objectivity of the CRITIC standard eliminates the subjective impact of adjusting the weights of each factor. Finally, four factors and their respective weights are designed to change in each round of data collection to adapt to the high dynamic changes of the sensor network in subsequent rounds. The simulation results show that this scheme is superior to low energy adaptive clustering hierarchy (LEACH), KMeans-LEACH, energy efficient unequal clustering (EEUC), and improved ant colony clustering adaptive routing protocols (IACAs) in terms of network node energy efficiency and improves the energy efficiency and lifecycle of the entire system.