Abstract
To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.
Highlights
Wireless sensor networks (WSNs) which are composed of lots of tiny, resource-constrained and cheap sensor nodes are self-organized networks
The sink node is capable of performing complex operations since it is usually supplied with unlimited resources
The remainder of this paper is organized as follows: in Section 2, we review the previous works involving dictionary learning and energy-efficient data gathering problem in wireless sensor networks
Summary
Wireless sensor networks (WSNs) which are composed of lots of tiny, resource-constrained and cheap sensor nodes are self-organized networks These nodes are always deployed in distributed mode to perform various applications, such as healthcare monitoring, transportation systems, industry service, and environmental measurement of humidity or temperature data [1]. Dictionary learning for sparse signal representation is one of the core problems of compressive sensing. The proposed algorithm aims to reduce the energy consumption for data gathering problem in WSNs. How to design ODL-CDG algorithm to be robust to environmental noise is our objective. The remainder of this paper is organized as follows: in Section 2, we review the previous works involving dictionary learning and energy-efficient data gathering problem in wireless sensor networks.
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