Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

Sensors

Sovannarith Heng¹  Tri Gia Nguyen¹  Phet Aimtongkham¹  ChakchaiSo-In¹  Van Nhan Vo² 

[1] Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand;International School, Duy Tan University, Danang 550000, Vietnam;

关键词: adaptive sampling;    block compressed sensing;    feature selection;    fuzzy logic system;    wireless multimedia sensor networks;   

DOI  :  10.3390/s20216217

来源: DOAJ

【 摘 要 】

: The transmission of high-volume multimedia content (e.g., images) is challenging for a resource-constrained wireless multimedia sensor network (WMSN) due to energy consumption requirements. Redundant image information can be compressed using traditional compression techniques at the cost of considerable energy consumption. Fortunately, compressed sensing (CS) has been introduced as a low-complexity coding scheme for WMSNs. However, the storage and processing of CS-generated images and measurement matrices require substantial memory. Block compressed sensing (BCS) can mitigate this problem. Nevertheless, allocating a fixed sampling to all blocks is impractical since each block holds different information. Although solutions such as adaptive block compressed sensing (ABCS) exist, they lack robustness across various types of images. As a solution, we propose a holistic WMSN architecture for image transmission that performs well on diverse images by leveraging saliency and standard deviation features. A fuzzy logic system (FLS) is then used to determine the appropriate features when allocating the sampling, and each corresponding block is resized using CS. The combined FLS and BCS algorithms are implemented with smoothed projected Landweber (SPL) reconstruction to determine the convergence speed. The experiments confirm the promising performance of the proposed algorithm compared with that of conventional and state-of-the-art algorithms.

【 授权许可】

Unknown