Managing and Mining Sensor Data 2012
DOI: 10.1007/978-1-4614-6309-2_7
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Abstract: Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series.

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Cited by 18 publications
(11 citation statements)
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“…The traditional knowledge discovery environment has been adapted to process data streams generated from sensor networks in (near) real time, to raise possible alarms, or to supplement missing data [6]. Consequently, the development of sensor networks is now accompanied by several algorithms for data mining which are modified versions of clustering, regression, and anomaly detection techniques from the field of multidimensional data series analysis in other scientific fields [4].…”
Section: Preface Preamblementioning
confidence: 99%
“…The traditional knowledge discovery environment has been adapted to process data streams generated from sensor networks in (near) real time, to raise possible alarms, or to supplement missing data [6]. Consequently, the development of sensor networks is now accompanied by several algorithms for data mining which are modified versions of clustering, regression, and anomaly detection techniques from the field of multidimensional data series analysis in other scientific fields [4].…”
Section: Preface Preamblementioning
confidence: 99%
“…The results obtained are much higher scalability and throughput without sacrificing reliability. Palpanas [17] review and analyses WSNs problems as real-time collection of the sensed data, and real-time processing of these data series. Based on these aspects different methods are discussed with their advantages and flaws.…”
Section: Introductionmentioning
confidence: 99%
“…An approach to reduce communication without compromising data quality is to predict the trend followed by the data being sensed, an idea at the core of many techniques [1]. This data prediction approach 1 is applicable when data is reported periodically-the common case in many pervasive computing applications.…”
Section: Introductionmentioning
confidence: 99%
“…This data prediction approach 1 is applicable when data is reported periodically-the common case in many pervasive computing applications. In these cases, a model of the data trend can be computed locally to a node.…”
Section: Introductionmentioning
confidence: 99%