Layer-Adaptive Communication and Collaborative Transformed-Domain Representations to Optimize Performance in Next-Generation WSNs

Abstract

In this paper, we combat the problem of performance optimization in wireless sensor networks. Specifically, a novel framework is proposed to handle two major research issues. Firstly, we optimize the utilization of resources available to various nodes at hand. This is achieved via proposed optimal network clustering enriched with layer-adaptive 3-tier communication mechanism to diminish energy holes. We also introduce a mathematical coverage model that helps us minimize the number of coverage holes. Secondly, we present a novel approach to recover the corrupted version of the data received over noisy wireless channels. A robust sparse-domain based recovery method equipped with specially developed averaging filter is used to take care of the unwanted noisy components added to the data samples. Our proposed framework provides a handy routing protocol that enjoys improved computation complexity and elongated network lifetime as demonstrated with the help of extensive simulation results.

Performance Optimization in IoT-Based Next-Generation Wireless Sensor Networks

“…We opt globally adopted PSNR and SSIM as evaluation metrics to prove that the denoising section of our proposed framework produces equally promising outcomes. The provided table summarizes denoising results of a number of images, as PSNR/SSIM, over a range of noise levels, i.e., σ n = [10,15,20,50,100] . Similarly, we also present the extensive denoising results of color images in Fig.…”

“…Presently, researchers are fundamentally concentrating on the technologicallyenriched tools for performance optimization of network structure, as a result of which the lifetime of WSNs is possible to increase. This possibility provides a roadway for scientists working in this research domain to propose low-cost, energy-efficient and optimized algorithms [19,20].…”

“…A part of this work has already been published in [18], and this is the extended version of our previous work. In [18], we introduced a novel framework to tackle two major concerns in WSNs: 1) performance optimization via efficient energy utilization, and 2) combating the unavoidable presence of Gaussian noise, added as a result of multiple communications among the nodes via wireless channel. We proposed a fast and low-cost sparse representations based collaborative system enriched with layer-adaptive 3-tier communication mechanism.…”

“…Comparison of Denoising Image Data Samples in Terms of PSNR/SSIM Denoising 256 × 256 grayscale Cameraman standard test data images over noise σ =[5,10,15,20,25, 50, 100] when received at a node µα. Each row represent an original image, a noisy image, a partially denoised, and a fully denoised image, respectively, corrupted by a specific level of additive white Gaussian noise (AWGN).…”