Layer-adaptive communication and collaborative transformed-domain representations to optimize performance in next-generation WSNs

In this paper, we combat the problem of performance optimization in wireless sensor networks of next-generation and beyond. Specifically, a novel framework is proposed to handle two major research issues. Firstly, we optimize the utilization of resources available to various sensing nodes. 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 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 remarkable computation complexity and elongated network lifetime as demonstrated with the help of extensive simulation results.