Dynamic clustering and compressive data gathering algorithm for energy-efficient wireless sensor networks

Abstract

Existing clustering algorithms of data gathering in wireless sensor networks neglect the impact of event source on the data spatial correlation. In this article, we proposed a compressed sensing–based dynamic clustering algorithm centred on event source. The main challenges of the prescribed scheme are how to model the impact of event source on spatial correlation and how to obtain the location of event source. To solve both the problems, we first formulate the Euclidean distance spatial correlation model and employ joint sparsity model-1 to describe the impact on the spatial correlation caused by event source. Based on these models, we conceive an efficient clustering scheme, which exploits the compressive data for computing the location of event source and for dynamic clustering. Simulation results show that the proposed compressed sensing–based dynamic clustering algorithm centred on event source outperforms the existing data gathering algorithms in decreasing the communication cost, saving the network energy consumption as well as extending the network survival time under a same accuracy. Additionally, the three performance affecting factors, namely, the attenuation coefficient of event sources, the distance between event sources and the number of event sources, are investigated and provided for constituting the application condition of the compressed sensing–based dynamic clustering algorithm centred on event source. The proposed scheme is potential in large-scale wireless sensor networks such as sensor-based IoT application.