Wireless sensor networks energy-efficient communication using generalized inverse nonnegative matrix factorization
Min Wu · Journal of Central South University(Science and Technology) · 2013
In order to solve the dilemma of the high-dimensional and high-redundant communication data in wireless sensor networks(WSNs),based on the fact that low-dimensional manifold of plausible space embedded in the high-dimensional space,novel algorithm(giNMF) using generalized inverse nonnegative matrix factorization was proposed for energy-efficient communication in WSNs.Firstly,a singular value decomposition(SVD) method was employed to initialize the original communication data matrix,and the corresponding feature space was found.Then,the nonnegative matrix factorization(NMF) approach was adopted to reduce dimensions of the matrix decomposed by SVD into lower dimensions,and the multiplication update law was used quickly to acquire the final dimension reduction results.The numerical results show that giNMF has its effectiveness in compressing the communication data,so as to reduce the communication energy consumption and prolong the network lifetime,and finally to achieve the goal of energy saving.