Reordering for Better Compressibility: Efficient Spatial Sampling in Wireless Sensor Networks

—Compressed Sensing (CS) is a novel sampling paradigm that tries to take data-compression concepts down to the sampling layer of a sensory system. It states that discrete compressible signals are recoverable from sub-sampled data, when the data vector is acquired by a special linear transform of the original discrete signal vector. Distributed sampling problems especially in Wireless Sensor Networks (WSN) are good candidates to apply CS and increase sensing efficiency without sacrificing accuracy. In this paper, we discuss how to reorder the samples of a discrete spatial signal vector by defining an alternative permutation of the sensor nodes (SN). Accordingly, we propose a method to enhance CS in WSN through improving signal compressibility by finding a sub-optimal permutation of the SNs. Permutation doesn't involve physical relocation of the SNs. It is a reordering function computed at the sink to gain a more compressible view of the spatial signal. We show that sub-optimal re...

• Type: Journal
• Year: 2010
• Where: SUTC
• Authors: Mohammadreza Mahmudimanesh, Abdelmajid Khelil, Neeraj Suri
• Keywords: Sensor Networks, Spatial Sampling, Spatial Sampling Rate, Spatial Signal, SUTC 2010