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Wireless sensor networks (WSNs) show immense promise in many applications, such as environmental monitoring and remotely metering. Compressive sensing (CS) is a novel signal processing that has been envisioned as a useful regime to address the energy and scaling constraints in WSNs. CS is able to move the burden of sensory nodes to central cloud/server. However, prevailing CS reconstruction algorithms...
The reconstruction in compressive sensing is an underdetermined question. Almost all existing reconstruction algorithms utilize pseudo inverse to solve this problem. However, the matrix inverse in pseudo inverse has high complexity. In this paper, we apply least mean square filter (LMS) to signal reconstruction and propose a new reconstruction algorithm for compressive sensing. The results show that...
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