Big Data Management 2016
DOI: 10.1007/978-3-319-45498-6_4
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A Review on Big Data Security and Privacy in Healthcare Applications

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Cited by 26 publications
(26 citation statements)
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“…First, sensor nodes are vulnerable to failure because the WSNs are often deployed in harsh environments [18][19][20][21]. Outliers are commonly found in datasets collected by the WSNs installed in harsh environments [22,23].…”
Section: Outlier Detection In Wsn Applicationssupporting
confidence: 44%
“…First, sensor nodes are vulnerable to failure because the WSNs are often deployed in harsh environments [18][19][20][21]. Outliers are commonly found in datasets collected by the WSNs installed in harsh environments [22,23].…”
Section: Outlier Detection In Wsn Applicationssupporting
confidence: 44%
“…The second big group is related to rural environmental monitoring [37]: (i) landslide and avalanche prevention [38], i.e., monitoring of soil moisture, vibrations, and earth density to detect dangerous patterns of inland conditions; (ii) earthquake early detection [39], i.e., distributed control in specific places of tremors; and (iii) forest fire detection [40], i.e., monitoring of combustion gases and preemptive fire conditions to define alert zones. A standalone section within rural monitoring is dedicated to agricultural monitoring [41] covering the following applications: (i) greenhouse parameter control [42], i.e., control of micro-climate conditions to maximize the production of fruits and vegetables and its quality; (ii) meteorological station network [43], i.e., monitoring of weather conditions in fields to forecast ice formation, rain drought, snow, or wind changes; (iii) animal tracking [44], i.e., location and identification of animals grazing in open pastures or location in big stables; (iv) wine production and quality enhancing [45], i.e., monitoring the productive cycle of high-quality wine; (v) monitoring of the toxic gas level of farm animals [46], i.e., a study of ventilation and air quality in farms and the detection of harmful gases from excrements; and (vi) compost monitoring [47], i.e., control of humidity and temperature levels in alfalfa, hay, straw, etc. to prevent fungus and other microbial contaminants.…”
Section: Overview On Environmental Monitoring Applications and Main Smentioning
confidence: 99%
“…Among these, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data obtained by wireless sensor networks (WSNs), like soil moisture data obtained from three different depths, meteorological data and so on, are two key monitoring methods with substantial potential for conducting farmland quality monitoring [4][5][6][7]. Moreover, the respective spatial and temporal benefits provided by these separate methods has generated considerable interest in the integration of the data collected by these two types of monitoring systems into a single farmland quality monitoring system [8][9][10][11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%