Compressive data gathering for large-scale wireless sensor networks

By Chong Luo, Feng Wu, Jun Sun, Chang Wen Chen. ACM/IEEE International Conference on Mobile Computing and Networking, 2009

626 citations recorded by the source corpus.

This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/7a659f69ce4ca008d43a265c97173c0283b910d9

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Citations in the corpus

DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, methodology): “To fully explore the temporal characteristics of the data, we follow common practice [31], [47], [56] and partition it into a series of segments according to a time window size Nw 2as shown in Figure 3.”
Compressed Sensing (2014, neutral): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
Compressive sensing: From theory to applications, a survey (2013, methodology): “Baseline Data Gathering and Compressive Data Gathering(adapted from [91])”
Compressed data aggregation for energy efficient wireless sensor networks (2011, background): “…to suggest a way of reducing the sensory data traffic for WSNs without the need for adapting to the data correlation structure [11], the complication involved in the interaction between data routing and CS-based aggregation has postponed the development on this front until very recently [12], [13].”