Abstract
One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.
Highlights
Wireless sensor network (WSN) has been well-suited for use with a variety of applications including environmental monitoring, biological detection, smart spaces, and battlefield surveillance
We propose a novel approach to implement sensor scheduling by maintaining the continuous sampling data fidelity that is defined by the structural similarity successful applied to image quality assessment methods
Our structure fidelity data collection (SFDC) framework is more suitable for the large scale dense WSNs in order to achieve a better effect on energy saving
Summary
Wireless sensor network (WSN) has been well-suited for use with a variety of applications including environmental monitoring, biological detection, smart spaces, and battlefield surveillance. We attempt to exploit the dependencies to reduce the number of nodes required to work for sampling and data transmission Such reduction is bound to save energy and prolong the network lifetime of WSNs. In this paper, we propose a novel approach to implement sensor scheduling by maintaining the continuous sampling data fidelity that is defined by the structural similarity successful applied to image quality assessment methods. The ultimate goal of this paper is to save energy in the continuous data sampling applications for WSNs by designing a novel node scheduling method, which considers the data fidelity in term of spatial structure instead of the traditional MSE.
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