Design and evaluation of an adaptive sampling strategy for a wireless air pollution sensor network

Gupta, Manik; Shum, Lamling Venus; Bodanese, Eliane; Hailes, Stephen

doi:10.1109/lcn.2011.6115154

Cited by 20 publications

(14 citation statements)

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“…We choose the EDSAS algorithm [18] for comparison since it is the latest published and best method for adaptive data sampling. For convenience, we use H-Based to denote the Hermit Interpolation based algorithm, S-Based to denote the Spline Interpolation based algorithm, and EDSAS to denote the algorithm in [18].…”

Section: Resultsmentioning

confidence: 99%

“…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%

“…0Þ to express the value of the jth derivative of f at t. In order to implement OðÞ-approximation to the physical world, the Hermit interpolation based data acquisition algorithm not only samples discrete sensed data from the physical world, but also returns a continuous sensed curve b s i ðtÞ to be the OðÞ-approximation to s i ðtÞ. Many mathematical models of s i ðtÞ have been studied [3], [15], [17], [18] based on the assumption that the monitored physical world varies continuously and is very smooth in most applications. Most of the models have the continuous fourth derivative functions.…”

Section: Hermit Interpolation Based Data Acquisition Algorithmmentioning

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%

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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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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Hermit Interpolation Based Data Acquisition Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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Approximate Physical World Reconstruction Algorithms in Sensor Networks

Cheng

Gao

et al. 2014

IEEE Trans. Parallel Distrib. Syst.

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“…The ongoing research that forms the basis of this paper is a collaborative effort between teams in the UK and in India [5][6] [14]. We have collected several sets of pollution data in both countries using our bespoke carbon monoxide monitors with high temporal and spatial resolution in relation to the norm for atmospheric science.…”

Section: Related Workmentioning

confidence: 99%

Bi-scale temporal sampling strategy for traffic-induced pollution data with Wireless Sensor Networks

Shum

Hailes

Gupta

et al. 2014

39th Annual IEEE Conference on Local Computer Networks

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Carbon Monoxide (CO) induced by traffic pollution is highly dynamic and non-linear. In a pilot research, we collected some fine-grained 1Hz CO pollution data from a residential road and a busy motorway in Hyderabad, India, in preparation of the deployment of a larger scale, longer term wireless sensor monitoring system. Power conservation is an important issue as the sensor nodes are battery operated. We studied the characteristics of the collected data and designed an adaptive sampling algorithm, Bi-Scale temporal sampler, which adapts the sampling frequency to the statistics collected in real time. This design has incorporated practical engineering considerations including minimising electronic noise, sensor warm-up time and data characteristics. Results show that Bi-Scale sampler achieves better energy saving and statistical deviation ratio for our requirements than burst sampling and eSENSE sampling strategies, which are techniques popularly used in environmental monitoring applications.

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Energy-Efficient and Smoothing-Sensitive Curve Recovery of Sensing Physical World

Zhang

et al. 2015

Big Data Computing and Communications

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Design and evaluation of an adaptive sampling strategy for a wireless air pollution sensor network

Cited by 20 publications

References 10 publications

Approximate Physical World Reconstruction Algorithms in Sensor Networks

Approximate Physical World Reconstruction Algorithms in Sensor Networks

Bi-scale temporal sampling strategy for traffic-induced pollution data with Wireless Sensor Networks

Energy-Efficient and Smoothing-Sensitive Curve Recovery of Sensing Physical World

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