Abstract
In recent years, sensing networks are widely used in the application of real-time monitoring. The change process of physical word is smoothing and continuous, but the sensing devices can only obtain the discrete data points. It is likely to lose the key points and distort the true curve if the discrete points are used simply to describe the physical world. Therefore, how to recover the approximate curve of physical world becomes a problem to be solved urgently. Based on this, an energy-efficient and smoothing-sensitive high-precision curve recovery algorithm for the sensing networks is proposed. Firstly, we recover the curve of physical world based on the existing physical-world-aware data acquisition algorithms preliminarily. And then a curve smoothing algorithm is proposed in order to acquire more key points (the inflexions are mainly considered in this paper) information which helps users better understand the change process of monitored physical world intuitively. Secondly, we propose an energy-efficient data source selection algorithm with residual energy of each data source and spatial correlation under consideration simultaneously. We select part of data sources to transmit data, maximize the lifetime of sensing network and minimize the error between the approximate curve and physical world. Finally, the effectiveness of our algorithms is verified by abundant experiments using both real and simulated data.
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Ma, Q., Gu, Y., Zhang, T., Li, F., Yu, G. (2015). Energy-Efficient and Smoothing-Sensitive Curve Recovery of Sensing Physical World. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_25
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DOI: https://doi.org/10.1007/978-3-319-22047-5_25
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