Noisy Data Gathering in Wireless Sensor Networks via Compressed Sensing and Cross Validation

2019 ◽  
pp. 101-111
Author(s):  
Xiaoxia Song ◽  
Yong Li ◽  
Wenmei Nie
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Cross Validation ◽  
Data Gathering ◽  
Noisy Data ◽  
Wireless Sensor

scholarly journals Data Gathering in Wireless Sensor Networks Based on Reshuffling Cluster Compressed Sensing

2015 ◽  
Vol 11 (11) ◽  
pp. 260913 ◽  
Author(s):  
Lu Zhu ◽  
Baishan Ci ◽  
Yuanyuan Liu ◽  
Zhizhang (David) Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Data Gathering ◽  
Wireless Sensor

scholarly journals Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2654 ◽  
Author(s):  
Yuan Rao ◽  
Gang Zhao ◽  
Wen Wang ◽  
Jingyao Zhang ◽  
Zhaohui Jiang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressed Sensing ◽  
Data Acquisition ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Improve Energy Efficiency ◽  
Switching Mechanism ◽  
Acquisition Scheme

Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events.

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