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In sensor networks, due to power outage at a sensor node, hardware dysfunction, or bad environmental conditions, not all sensor samples can be successfully gathered at the sink. Additionally, in the data stream scenario, some nodes may continually miss samples for a period of time. In this paper, a sparsity-based online data recovery approach is proposed. We construct an overcomplete dictionary composed...
Estimating missing sample values is an inherent problem in sensor network applications. In wireless sensor networks, due to power outage at a sensor node, hardware dysfunction, or bad environmental conditions, not all sensor samples can be successfully gathered at the sink. Additionally, in the context of data streams, some nodes may continually miss samples for a period of time. To address these...
Compressed sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It holds valuable implications for wireless sensor networks because power and bandwidth are limited resources. In this paper, applying the theory of compressed sensing to the practical sensor network data recovery problem,...
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