Layer-Adaptive Communication and Collaborative Transformed-Domain Representations for Performance Optimization in WSNs

TL;DR

This paper introduces a comprehensive framework for optimizing performance in wireless sensor networks by enhancing resource utilization, reducing coverage holes, and improving data recovery over noisy channels, leading to longer network lifetime.

Contribution

It presents a novel layered communication scheme and a sparse-domain data recovery method, significantly improving energy efficiency and data integrity in WSNs.

Findings

Enhanced network lifetime demonstrated through simulations

Reduced coverage holes via mathematical modeling

Improved data accuracy over noisy channels

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.