Energy-efficient data acquisition by adaptive sampling for wireless sensor networks

Law, Yee Wei; Chatterjea, Supriyo; Jin, Jiong; Hanselmann, T.; Palaniswami, Marimuthu

doi:10.1145/1582379.1582631

Cited by 29 publications

(20 citation statements)

References 19 publications

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“…This capability could benefit power-constrained environmental sensor networks. For example, a remote seismic sensor could conserve resources by limiting its measurements during normal conditions and increasing its sample cadence during rare earthquake events [1]. Similar methods could enhance robotic exploration where a sensor follows a fixed trajectory, such as an exploration rover transect or a deep space flyby.…”

Section: Introductionmentioning

confidence: 99%

“…These tests typically use batch optimizations rather than an on-line update, and are limited to the family of graphical models. An ARIMA-based method by Law et al does adapt to local changes in real time [1], but this approach relies on heuristic rules and does not enforce a global budget on the number of samples. We seek a new approach to permit a true information-theoretic treatment, with on-line updates, over a broader class of probabilistic models.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive sensing of time series with application to remote exploration

Thompson

Cabrol

Furlong

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-We address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive sensing of time series with application to remote exploration

Thompson

Cabrol

Furlong

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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show abstract

“…The works in [16], [17], and [18] provide three adaptive sampling methods for data acquisition based on some statistical modeling techniques, Box-Jenkins and exponential double smoothing techniques, respectively. These methods are also based on the EFS method and only care about saving energy, thus they have the similar problems as those in [15].…”

Section: Related Workmentioning

confidence: 99%

“…Besides, the larger the sampling frequency, the bigger the amount of collected data, which burdens transmission and storage. Therefore, several adaptive sampling methods [15], [16], [17], [18] were proposed for acquiring data from the physical world by WSNs. However, all these algorithms are developed based on the EFS method, and they focus on saving energy rather than improving accuracy.…”

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confidence: 99%

Approximate Physical World Reconstruction Algorithms in Sensor Networks

Cheng

Gao

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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To observe the complicated physical world, the sensors in a network sense and sample the data from the physical world. Currently, most existing works use the Equi-Frequency Sampling (EFS) methods or EFS based methods for data acquisition. However, the accuracy of EFS and EFS based methods cannot be guaranteed in practice since the physical world keeps changing continuously, and these methods do not effectively support reconstruction of the monitored physical world. To overcome the shortages of EFS and EFS based methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support OðÞ-approximation to the physical world for any ! 0. Two physical-world-aware data acquisition algorithms are proposed. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and the given . The thorough analysis on the performances of the algorithms are also provided. It is proven that the error bounds of the algorithms are OðÞ and the complexities of the algorithms are Oð 1 1=4 Þ. Based on the new data acquisition algorithms, an algorithm for reconstructing the physical world is proposed and analyzed. The theoretical analysis and experimental results show that the proposed algorithms have high performances on the aspects of accuracy and energy consumption.Index Terms-Wireless sensor networks, data acquisition Ç 1 MOTIVATION W IRELESS sensor networks (WSNs) are extremely important in cyber-physical systems (CPS) for observing and cognizing the complicated physical world at low cost. To observe the physical world by a WSN, the sensors in a WSN sense and sample the data from the physical world. Most of the current works use a simple equi-frequency sampling (EFS) method, in which each sensor senses and samples data from the physical world with equi-frequency. Based on EFS, many energy efficient data computation algorithms were proposed for WSNs, including the data collection algorithms [2], [3], [4], [5], the data query processing algorithms [6], [7], [8], [9], the data compression algorithms [10], [11], [12], the data mining and analyzing algorithms [13], [14], etc.However, since EFS only samples some discrete data points with a fixed frequency from the continuously-varying physical world, it may overlook some critical data and lead to distortion of the observed physical world when the sampling frequency is low, which results in misunderstanding of the physical world. To see the problem of EFS, we have performed an experiment to monitor wind velocity during 100 hours in the city of Harbin in China. The experimental results are presented in Fig. 1. Fig. 1a shows the curve for the real wind velocity, and other ones show the curve generated by the EFS method. As shown in Figs. 1b and 1c, many important values, such as maximal, minimal and inflection points are missed by EFS when the sampling frequency is not large enough. In addition, they also indicate that the distortion introduced by the EFS method becomes more obvious when the monitored physical worl...

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“…Similar to [87], article [134] uses the Box-Jenkins method for time series modeling to address adaptive sampling. This approach forecasts future samples based on existing samples and estimates those that can be skipped.…”

Section: Temporal Correlation-based Decentralized Adaptive Samplingmentioning

confidence: 99%

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Energy-efficient data acquisition by adaptive sampling for wireless sensor networks

Cited by 29 publications

References 19 publications

Adaptive sensing of time series with application to remote exploration

Adaptive sensing of time series with application to remote exploration

Approximate Physical World Reconstruction Algorithms in Sensor Networks

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