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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:EI、CPCI-S

Volume:728

Page Number:148-160

Key Words:Data model; Adaptive collection; Sensor networks

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.