Abstract
To obtain efficient data gathering methods for wireless sensor networks (WSNs), a novel graph based transform regularized (GBTR) matrix completion algorithm is proposed. The graph based transform sparsity of the sensed data is explored, which is also considered as a penalty term in the matrix completion problem. The proposed GBTR-ADMM algorithm utilizes the alternating direction method of multipliers (ADMM) in an iterative procedure to solve the constrained optimization problem. Since the performance of the ADMM method is sensitive to the number of constraints, the GBTR-A2DM2 algorithm obtained to accelerate the convergence of GBTR-ADMM. GBTR-A2DM2 benefits from merging two constraint conditions into one as well as using a restart rule. The theoretical analysis shows the proposed algorithms obtain satisfactory time complexity. Extensive simulation results verify that our proposed algorithms outperform the state of the art algorithms for data collection problems in WSNs in respect to recovery accuracy, convergence rate, and energy consumption.
Highlights
Wireless sensor networks (WSNs) are composed of large-scale, self-organized sensor nodes, which are capable of sensing, data storage, and communication
Since similar trends are obtained for graph based transform regularized (GBTR)-A2DM2, we just omit it here
When the sampling is 1%, GBTR-alternating direction method of multipliers (ADMM) and GBTR-A2DM2 can reconstruct the original missing values with recovery errors of less than 20%
Summary
Wireless sensor networks (WSNs) are composed of large-scale, self-organized sensor nodes, which are capable of sensing, data storage, and communication. A large number of data collection methods have been proposed to reduce the energy consumption with different levels of data reconstruction precision in the literature [1,2,3] These obtained data in WSNs possess spatial and temporal correlations, which are intrinsic characteristics of a physical environment. Since the sensed data require to be transmitted to the sink node in multihop communication, Rosana et al [3] proposed a novel algorithm to construct spanning trees for efficient data gathering in wireless sensor networks. To the best of our knowledge, this is the first time the GBT sparsity has been applied to a matrix completion problem In consideration of both the GBT sparsity and the low-rank feature of the sensed data, the GBTR-ADMM and the GBTR-A2DM2 algorithm are proposed.
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