Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application

Abstract

Transmission of high volume of data in a restricted wireless sensor network (WSN) has come up as a challenge due to high-energy consumption and larger bandwidth requirement. To address the issues of high-energy consumption and efficient data transmission adaptive block compressive sensing (ABCS) is one of the optimum solution. ABCS framework is well capable to adapt the sampling rate depending on the block’s features information that offers higher sampling rate for less compressible blocks and lower sampling rate for more compressible blocks In this paper, we have proposed a novel fuzzy rule based adaptive compressive sensing approach by leveraging the saliency and the edge features of the image making the sampling rate selection completely automatic. Adaptivity of the block sampling ratio has been decided based on the fuzzy logic system (FLS) by considering two important features i.e., edge and saliency information. The proposed framework is experimented on standard dataset, Kodak data set, CCTV images and the Set5 data set images. It achieved an average PSNR of 34.26 and 33.2 and an average SSIM of 0.87 and 0.865 for standard images and CCTV images respectively. Again for high resolution Kodak data set and Set 5 dataset images, it achieved an average PSNR of 32.95 and 31.72 and SSIM of 0.832 and 0.8 respectively. The experiments and the result analysis show that proposed method is efficacious than the state of the art methods in both subjective and objective evaluation metrics.