Abstract
Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs.
Highlights
Wireless sensor networks have been widely deployed in a variety of applications including environmental monitoring, traffic surveillance and social sensing and analysis [1,2,3,4]
We prove that the sensing matrix for spatial dimensional signal which is constructed from the proposed random walk algorithm and a kernel-based sparsity representation basis satisfies the Restricted Isometry Property (RIP)
We studied an energy-efficient data gathering scheme using compressive sensing for spatial-temporal sensory data in mobile wireless sensor networks
Summary
Wireless sensor networks have been widely deployed in a variety of applications including environmental monitoring, traffic surveillance and social sensing and analysis [1,2,3,4]. In such networks, data gathering is one of most fundamental tasks, where a large amount of sensory data is required to be transmitted to a fusion center (FC). Compressive sensing is able to perform sensing and compression simultaneously to reduce the amount of data transmitted over the network so as to save energy consumption at each sensor node
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