Enabling ε-approximate querying in sensor networks

Liu, Yu; Li, Jianzhong; Gao, Hong; Fang, Xiaolin

doi:10.14778/1687627.1687647

Cited by 7 publications

(4 citation statements)

References 27 publications

(41 reference statements)

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“…Similarly, starting from this new data point, new lines are used to approximate subsequent arrivals. The enabling approximate querying (EAQ) algorithm was proposed [30] that first converts the original time series into a special time series description multi-version array (MVA). With this MVA prefix, the approximate version of the original time series with certain errors can be recovered.…”

Section: Related Workmentioning

confidence: 99%

Data Uploading Strategy for Underwater Wireless Sensor Networks

Huang

Sun

Yang

2019

Sensors

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Underwater wireless sensor networks (UWSNs) have become a popular research topic due to the challenges of underwater communication. The existing mechanisms for collecting data from UWSNs focus on reducing the data redundancy and communication energy consumption, while ignoring the problem of energy-saving transmission after compression. In order to improve the efficiency of data collection, we propose a data uploading decision-making strategy based on the high similarity of the collected data and the energy consumption of the high similarity data compression. This decision-making strategy efficiently optimizes the energy consumption of the networks. By analyzing the data similarity, the quality of network communication, and uploading energy consumption, the decision-making strategy provides an energy-efficient data upload strategy for underwater nodes, which reduces the energy consumption in various network settings. The simulation results show that compared with several existing data compression and uploading methods, the proposed data upload methods has better energy saving effect in different network scenarios.

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Section: Related Workmentioning

confidence: 99%

Data Uploading Strategy for Underwater Wireless Sensor Networks

Huang

Sun

Yang

2019

Sensors

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“…To alleviate these drawbacks, researchers have made efforts in constructing the representations with guaranteed maximum allowable approximate error on each data point (L ∞ norm), which is termed as the error-bounded representations. It has been engaged in many real world applications, such as continuous queries over data streams [15,19], sensor network management [25,35], and monitoring physiological data for surgery operations [23,36].…”

Section: Introductionmentioning

confidence: 99%

“…1). These advantages make PLR the most popular representation technique for the data stream [14], and it has been widely applied to support date indexing [5,13,26], similarity search [29,37], and correlation analysis [33,35]. Histogram is also employed as contemporary error-bounded representation, i.e., Piecewise Constant Representation (PCR).…”

Section: Introductionmentioning

confidence: 99%

Error-Bounded Approximation of Data Stream: Methods and Theories

Xie

Pang

Zhou

et al. 2018

Studies in Big Data

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As the development of sensor network and Internet of Things, the volume of data is rapidly increasing and the streaming data has attracted much attention recently. To efficiently process and explore data streams, the compact data representation is playing an important role, since the data approximations other than the original data items are usually applied in many stream mining tasks, such as clustering, classification and correlation analysis. In this chapter, we focus on the maximum error-bounded approximation of data stream, which represents the streaming data with constrained approximation error on each data point. There are two criteria for the approximation solution: self-adaption over time for varied error bound and real-time processing. We reviewed the existing data approximation techniques and summarized some essential theories such as optimization guarantee. Two op-Qing Xie( ) * Reprinted by permission from Springer Nature: Springer, The VLDB Journal, Maximum error-bounded piecewise linear representation for online stream approximation, Q. Xie et al., c Springer-Verlag Berlin Heidelberg 2014. (doi: https://doi.org/10.1007/s00778-014-0355-0) 2 Q. Xie et al.timal linear-time algorithms are introduced to construct error-bounded piecewise linear representation for data stream. One generates the line segments by data convex analysis, and the other one is based on the transformed space, which can be extended to a general model. We theoretically analyzed and compared these two different spaces, and proved the theoretical equivalence between them, as well as the two algorithms.

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“…For example, battery lifetime of sensors, which is a critical issue in wireless sensor networks, can be substantially extended by reducing the amount of transmitted data [10,13,26], while also maximizing the utilization of limited communication bandwidth [8,39,41]. Moreover, compression enables fast data processing by evaluating queries directly over compressed data [12,24,30,33,42], incurring much less I/O, since compressed data are generally stored using lower numbers of disk pages [40].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Model-Based Approaches to Sensor Data Compression

Nguyen

Jeung

Aberer

2013

IEEE Trans. Knowl. Data Eng.

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Abstract-As the volumes of sensor data being accumulated are likely to soar, data compression has become essential in a wide range of sensor-data applications. This has led to a plethora of data compression techniques for sensor data, in particular modelbased approaches have been spotlighted due to their significant compression performance. These methods, however, have never been compared and analyzed under the same setting, rendering a 'right' choice of compression technique for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the model-based compression techniques. Specifically, we re-implemented several state-ofthe-art methods in a comparable manner, and measured various performance factors with our benchmark, including compression ratio, computation time, model maintenance cost, approximation quality, and robustness to noisy data. We then provide in-depth analysis of the benchmark results, obtained by using 11 different real datasets consisting of 346 heterogeneous sensor data signals. We believe that the findings from the benchmark will be able to serve as a practical guideline for applications that need to compress sensor data.

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Enabling ε-approximate querying in sensor networks

Cited by 7 publications

References 27 publications

Data Uploading Strategy for Underwater Wireless Sensor Networks

Data Uploading Strategy for Underwater Wireless Sensor Networks

Error-Bounded Approximation of Data Stream: Methods and Theories

An Evaluation of Model-Based Approaches to Sensor Data Compression

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