Efficient distributed storage strategy based on compressed sensing for space information network

Abstract

This article investigates the distributed data storage problem with compressed sensing in the space information network. Since there exists a performance-energy trade-off, most existing strategies focus only on improving the compressed sensing construction performance or reducing the energy consumption, respectively. In order to achieve a better balance, a novel and efficient strategy, referred to as distributed storage strategy based on compressed sensing, is proposed in this article. Unlike other strategies which require source packets visiting the entire network, the proposed strategy is a “one-hop” method since information exchange is only performed between neighbors. Therefore, the compressed sensing measurement matrix depends heavily on the degree of each space node. We prove that the proposed strategy guarantees the compressed sensing reconstruction performance under both sparse orthonormal basis and dense orthonormal basis. Simulation results validate that, compared with the representative CStorage strategy and compressive data persistence strategy, the proposed strategy consumes the least energy and computational overheads, while almost without sacrificing the compressed sensing reconstruction performance.