Optimal deployment of large-scale wireless sensor networks based on graph clustering and matrix factorization

Abstract

It is undeniable that there are a large number of redundant nodes in a wireless sensor network. These redundant nodes cause a colossal waste of resources and seriously threaten the life of the sensor network. In this paper, we provide a sensor nodes optimization selection algorithm based on a graph for a large-scale wireless sensor network. Firstly, we propose a representation-clustering joint algorithm based on Graph Neural Network to partition the large-scale graph into several subgraphs. Then, we use Singular-Value-QR Decomposition for the node selection of each subgraph and achieve the optimal deployment for a large-scale wireless sensor network. We conduct the experiments on the CIMIS dataset. The results show that the mean square error between the reconstructed network and the original network is as low as 0.02433. Meanwhile, we also compare our algorithm with the classical optimization algorithm. The results imply that the mean square error of the proposed algorithm is lower and the distribution is more uniform. Further, we verify the scalability of the algorithm for the optimal deployment of the large-scale wireless sensor network.