Abstract
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.
Highlights
The wireless sensor network (WSN), which is composed of a large number of tiny resource-constrained wireless sensor nodes (SNs), has become a pervasive emerging technology [1].WSNs can be deployed for use in various fields, e.g., military applications, disaster management, industry, environmental monitoring, and agricultural farming [2]; they have received considerable attention from the research community [3] and have become a pillar of the Internet of Things (IoT) [4]
An Fuzzy Logic System (FLS) is a well-known technique for selecting appropriate weights over multiple inputs, various memberships, and outputs [6], and it is suitable for implementation in to overcome the abovementioned issue faced in adaptive block compressed sensing (ABCS), we propose a novel compressed sensing (CS)
An objective evaluation yields specific results based on mathematical formulas, while a subjective evaluation is generally based on observer perceptions when evaluating the quality of a reconstructed image [58]
Summary
The wireless sensor network (WSN), which is composed of a large number of tiny resource-constrained wireless sensor nodes (SNs), has become a pervasive emerging technology [1]. These ABCS algorithms adaptively allocate the sampling to each block based on an image feature, i.e., either saliency, standard deviation, edge, or texture. The main challenges in developing an ABCS algorithm are to select appropriate features, assign a suitable sampling to each block based on the selected features, and adaptively determine the convergence speed of the reconstruction algorithm to yield reconstructed images with acceptable quality. To overcome the abovementioned issue faced in ABCS, we propose a novel CS architecture that applies an FLS-based approach to the task of image transmission in WMSNs and improves the quality of the reconstructed images.
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