Abstract
To monitor the physical world, Equi-Frequency Sampling (EFS) methods are widely applied for data acquisition in sensor networks. Due to the noise and inherent uncertainty of the environment, EFS based data acquisition may result in misconception to the physical world, and high frequency scheme produces massive sensed data, which consumes substantial cost for transmission. This paper proposes a novel sensed data model. Based on maximum likelihood estimation, the model can minimize measurement error. It is proved that the proposed model is asymptotic unbiased. Furthermore, this paper proposes Model based Adaptive Data Collection (MADC) Algorithm and designs a distributed lightweight computation algorithm named Distributed Adaptive Data Collection Algorithm (DADC). Based on the error of prediction, both algorithms can adaptively adjust the cycle of data collection. Performance evaluation verifies that the proposed algorithms have high performance in terms of accuracy and effectiveness.
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (61602084, 61502099 61572104), the Post-Doctoral Science Foundation of China (2016M600202), the Doctoral Scientific Research Foundation of Liaoning Province (201601041).
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Bi, R., Tan, G., Fang, X. (2017). Critical Value Aware Data Acquisition Strategy in Wireless Sensor Networks. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_13
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DOI: https://doi.org/10.1007/978-981-10-6388-6_13
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