Zero-effort projection for sensory data reconstruction in wireless sensor networks

Xiancun Zhou and Haibo Ling

International Journal of Distributed Sensor Networks, 2016, vol. 12, issue 8, 1550147716659425

Abstract: Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. Existing compressive sensing–based data gathering techniques still follow sampling than compression paradigm. In this article, we proposed a random sampling zero-encoding data gathering scheme for wireless sensor networks, which exploits virtual Gaussian energy diffusion model to obtain sampling and compression data gathering. Our proposed data gathering model not only can make simultaneous sampling and compression but also do not need to assign projection matrix to each sensor node. Our scheme can efficiently resolve two types of sensor networks’ data gathering problems: recover missing sensory data and extend monitoring field using incomplete random sampling. Extensive experimental results show that our proposed random sampling zero-encoding data gathering model has good performance for reconstructing the sensory data in wireless sensor networks.

Keywords: Wireless sensor networks; compressive sensing; Gaussian energy diffusion; zero-encoding; random sampling (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:12:y:2016:i:8:p:1550147716659425

DOI: 10.1177/1550147716659425

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