A Review on Big Data Security and Privacy in Healthcare Applications

Aqeel-ur-Rehman, .; Khan, Ismail; Rehman, Sadiq Ur

doi:10.1007/978-3-319-45498-6_4

Cited by 26 publications

(26 citation statements)

References 20 publications

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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%

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

Zhang

Zhao

Shin

et al. 2018

JSAN

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Abstract:We propose a new peak searching algorithm (PSA) that uses Bayesian optimization to find probability peaks in a dataset, thereby increasing the speed and accuracy of clustering algorithms. Wireless sensor networks (WSNs) are becoming increasingly common in a wide variety of applications that analyze and use collected sensing data. Typically, the collected data cannot be directly used in modern data analysis problems that adopt machine learning techniques because such data lacks additional information (such as data labels) specifying its purpose of users. Clustering algorithms that divide the data in a dataset into clusters are often used when additional information is not provided. However, traditional clustering algorithms such as expectation-maximization (EM) and k-means algorithms require massive numbers of iterations to form clusters. Processing speeds are therefore slow, and clustering results become less accurate because of the way such algorithms form clusters. The PSA addresses these problems, and we adapt it for use with the EM and k-means algorithms, creating the modified PSEM and PSk-means algorithms. Our simulation results show that our proposed PSEM and PSk-means algorithms significantly decrease the required number of clustering iterations (by 1.99 to 6.3 times), and produce clustering that, for a synthetic dataset, is 1.69 to 1.71 times more accurate than it is for traditional EM and enhanced k-means (k-means++) algorithms. Moreover, in a simulation of WSN applications aimed at detecting outliers, PSEM correctly identified the outliers in a real dataset, decreasing iterations by approximately 1.88 times, and PSEM was 1.29 times more accurate than EM at a maximum.

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Section: Outlier Detection In Wsn Applicationssupporting

confidence: 44%

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

Zhang

Zhao

Shin

et al. 2018

JSAN

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“…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%

Environmental Monitoring with Distributed Mesh Networks: An Overview and Practical Implementation Perspective for Urban Scenario

Ometov

Bezzateev

Voloshina

et al. 2019

Sensors

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Almost inevitable climate change and increasing pollution levels around the world are the most significant drivers for the environmental monitoring evolution. Recent activities in the field of wireless sensor networks have made tremendous progress concerning conventional centralized sensor networks known for decades. However, most systems developed today still face challenges while estimating the trade-off between their flexibility and security. In this work, we provide an overview of the environmental monitoring strategies and applications. We conclude that wireless sensor networks of tomorrow would mostly have a distributed nature. Furthermore, we present the results of the developed secure distributed monitoring framework from both hardware and software perspectives. The developed mechanisms provide an ability for sensors to communicate in both infrastructure and mesh modes. The system allows each sensor node to act as a relay, which increases the system failure resistance and improves the scalability. Moreover, we employ an authentication mechanism to ensure the transparent migration of the nodes between different network segments while maintaining a high level of system security. Finally, we report on the real-life deployment results.

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“…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%

Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality

Zhang

Wang

et al. 2020

Sensors

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Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.

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A Review on Big Data Security and Privacy in Healthcare Applications

Cited by 26 publications

References 20 publications

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

Environmental Monitoring with Distributed Mesh Networks: An Overview and Practical Implementation Perspective for Urban Scenario

Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality

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