Compressed sensing and mobile agent based sparse data collection in wireless sensor networks

In the paper, we study how the theory of compressed sensing is applied in wireless sensor networks to capture sparse signals of interest. Traditional data collection and compressive data gathering separately use client/server paradigm and dense random matrix, leading high computation load and energy consumption. We adopt sparse binary matrix for the measurement matrix. A mobile agent based compressed sensing paradigm is proposed. In the paradigm, the sensed data are stored locally, and it doesn´t require network topology as prior knowledge. For the routing of mobile agents, we formulate a Mobile Agent Path Design Algorithm(MAPDA), which is designed by greedy algorithm. Finally Orthogonal Matching Pursuit(OMP) algorithm is used for signal recovery. The experimental results show the proposed algorithm can save energy than client/server paradigm based on different sparse binary matrix and plain-CS. In addition, we observe that sparse binary matrix has the same reconstruction performance as Gaussian random matrix(dense random matrix).