Mining Data Generated by Sensor Networks: A Survey

Mahmood, Azhar; Shi, Ke; Khatoon, Shaheen

doi:10.3923/itj.2012.1534.1543

Cited by 13 publications

(13 citation statements)

References 21 publications

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“…Th ere are many predictive modeling methods available, including tree-based, rule-based, nearest neighbor, logistic regression, artifi cial neural networks, graphical methods, and support vector machines. Th ese methods are designed to solve two types of predictive modeling tasks: classifi cation and regression [11]. Using these prediction models the number of sensors that need to report their measurements is reduced by reducing both node activity and bandwidth.…”

Section: Figurementioning

confidence: 99%

“…Using these prediction models the number of sensors that need to report their measurements is reduced by reducing both node activity and bandwidth. From analysis made in [11] it is observed that the techniques intended for mining sensor data at network side are helpful for taking real time decision as well as serve as prerequisite for development of eff ective mechanism for data storage, retrieval, query and transaction processing at central side. On the other hand centralized techniques are helpful in generating off -line predictive insights which in turn can facilitate real-time analysis.…”

Section: Figurementioning

confidence: 99%

“…Th is is the reason for exploring novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs [11]. For such reasons, in recent years a great interest emerged in the research community in applying data mining techniques to the large volumes of sensor data.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection

Maksimović¹,

Vujović²

2013

JITA

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Abstract:Wireless sensor networks (WSN) are a rapidly growing area for research and commercial development with very wide range of applications. Using WSNs many critical events like fi re can be detected earlier to prevent loosing human lives and heavy structural damages. Integration of soft computing techniques on sensor nodes, like fuzzy logic, neural networks and data mining, can signifi cantly lead to improvements of critical events detection possibility. Using data mining techniques in process of patterns discovery in large data sets it's not often so easy. A several algorithms must be applied to application before a suitable algorithm for selected data types can be found. Therefore, the selection of a correct data mining algorithm depends on not only the goal of an application, but also on the compatibility of the data set. This paper focuses on comparative analysis of various data mining techniques and algorithms and in that purpose three different experiments on WSN fi re detection data are proposed and performed. The primary goal was to see which of them has the best classifi cation accuracy of fuzzy logic generated data and is the most appropriate for a particular application of fi re detection.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection

Maksimović¹,

Vujović²

2013

JITA

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“…But the extraction of useful knowledge from raw sensor data is a difficult task and traditional data mining techniques are not directly applicable to WSNs due to the distributed nature of sensor data and their special characteristics (the massive quantity and the high dimensionality), and limitations of the WSNs and sensor nodes [4]. This is the reason for exploring novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs [5]. The main goal of data mining techniques is to find and describe the structural patterns in the data in order to attempt to explain connections between data and create predictive models based on them.…”

Section: Introductionmentioning

confidence: 99%

“…There are many predictive modeling methods available, including tree-based, rulebased, nearest neighbor, logistic regression, artificial neural networks, graphical methods, and support vector machines. These methods are designed to solve two types of predictive modeling tasks: classification and regression [5]. Using these prediction models the number of sensors that need to report their measurements is reduced by reducing both node activity and bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Applying Fuzzy Logic and Data Mining Techniques in Wireless Sensor Network for Determination Residential Fire Confidence

Maksimović¹,

Vujović²,

Milosevic³

2014

JESTR

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The main goal of soft computing technologies (fuzzy logic, neural networks, fuzzy rule-based systems, data mining techniques…) is to find and describe the structural patterns in the data in order to try to explain connections between data and on their basis create predictive or descriptive models. Integration of these technologies in sensor nodes seems to be a good idea because it can significantly lead to network performances improvements, above all to reduce the energy consumption and enhance the lifetime of the network. The purpose of this paper is to analyze different algorithms in the case of fire confidence determination in order to see which of the methods and parameter values work best for the given problem. Hence, an analysis between different classification algorithms in a case of nominal and numerical data sets is performed with the goal to realize which of applied techniques obtain higher accuracy and less error

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An Efficient Data Extraction Framework for Mining Wireless Sensor Networks

Rashid

Gondal

2016

Neural Information Processing

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A wide range of Sensor Networks (SNs) are deployed in real world applications which generate large amount of raw sensory data. Data mining technique to extract useful knowledge from these applications is an emerging research area due to its crucial importance but still it's a challenge to discover knowledge efficiently from the sensor network data. In this paper we proposed a Distributed Data Extraction (DDE) method to extract data from sensor networks by applying rules based clustering and association rule mining techniques. A significant amount of sensor readings sent from the sensors to the data processing point(s) may be lost or corrupted. DDE is also estimating these missing values from available sensor reading instead of requesting the sensor node to resend lost reading. DDE also apply data reduction which is able to reduce the data size while transmitting to sink. Results show our proposed approach exhibits the maximum data accuracy and efficient data extraction in term of the entire network's energy consumption.

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Mining Data Generated by Sensor Networks: A Survey

Cited by 13 publications

References 21 publications

Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection

Comparative Analysis of Data Mining Techniques Applied to Wireless Sensor Network Data for Fire Detection

Applying Fuzzy Logic and Data Mining Techniques in Wireless Sensor Network for Determination Residential Fire Confidence

An Efficient Data Extraction Framework for Mining Wireless Sensor Networks

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