LAUSR

Bandwidth and energy constraints of underwater wireless sensors networks necessitate an efficient data transmission between sensor nodes and the fusion center. This paper considers the data gathering underwater networks for monitoring oceanic environmental elements (e.g. temperature, salinity) and only a portion of measurements from sensors allows for oceanic information map reconstruction under compressed sensing (CS) theory. By utilizing the spatial sparsity of active sensors’ data, we introduce an activity and data detection based on CS at the receiver side resulting in an efficient data communication by avoiding the necessity of conveying identity information. For an interleave division multiple access (IDMA) sporadic transmission, CS-CBC detection that combines the benefits from chip-by-chip (CBC) multi-user detection and CS detection is proposed. Further, by successively exploring the sparsity of sensor data in spatial and frequency domain, we propose a novel efficient data gathering scheme named Dual-domain compressed sensing (DCS). Simulation results validate the effectiveness of the proposed scheme compared to IDMA-CS scheme and an optimal sensing probability problem related to minimum reconstruction error is explored.