Data reduction using incremental Naive Bayes Prediction (INBP) in WSN

Ganjewar, Pramod D.; Barani, S.; Wagh, Sanjeev J.

doi:10.1109/infop.2015.7489415

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…In [17], data from WSNs may be decreased using incremental Naive Bayes Prediction, while data collection has been recommended using compressive sensing technology in [4] and [18], where active sensor nodes are improved using particle swarm optimization to minimize the quantity of duplicate data. Fuzzy Dstar-Lite was suggested by the authors of [14] as the optimal information routing strategy for HWSNs.…”

Section: Ijici Issn: 2791-2868 Rmentioning

confidence: 99%

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Alsawad

2022

IJICI

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The mining and processing of data for wireless sensor networks (WSNs) have been a significant research area in a number of computer science disciplines, including distributed applications, database management systems, and information gathering. The main goal of implementing applications based on WSNs is to make real-time choices, which have proven very difficult due to the limitations of computing, communication, and data mining capacity for WSNs. Thus, due to the nature and unique characteristics of sensor data and the limitations of WSNs, traditional data mining methods cannot be easily applied to them. This paper presents a new approach, the Kalman Filter (KF) with K-Nearest Neighbors (KNN) named (KF-KNN) which is proposed for data classification and collection as well as noise elimination in WSNs, increasing the network efficiency and lifetime. The proposed technique is compared to KNN to illustrate the effectiveness of the recommended ways in increasing energy consumption and prolonging network lifetime.

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Section: Ijici Issn: 2791-2868 Rmentioning

confidence: 99%

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Alsawad

2022

IJICI

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“…Pramod D Ganjewar et. all in [8] proposed Incremental Naive Bays method for prediction of the data value which is going to transmit the sink. The prediction will take place from groups of data values.…”

Section: Related Workmentioning

confidence: 99%

Threshold Based Data Reduction for Prolonging Life of Wireless Sensor Network

Ganjewr¹,

Barani²,

Wagh³

2017

IJANS

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Wireless sensor network is a set of tiny elements i.e. sensors. WSN is used in the field of Health Monitoring, Civil Construction, Military Applications and Agricultural etc., for monitoring environmental parameters. The WSN is having the challenges like less processing power, less memory, less bandwidth and battery powered. The data monitored through the sensors would be sent to the sink for data processing. The data sent from sensor node can be controlled for saving the energy, as maximum energy is consumed for transmission of data and it is not possible to replace the batteries frequently. In this work threshold based and adaptive threshold based data reduction techniques with energy efficient shortest path are used for minimizing the energy of sensor node and the network. Adaptive approach saves energy and reduce data by 30% to 40% as compared to threshold based approach.

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“…all in [8] proposed Incremental Naive Bays method for prediction of the data value which is going to transmit the sink. The prediction will take place from groups of data values.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2017

IJANS

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Data reduction using incremental Naive Bayes Prediction (INBP) in WSN

Cited by 8 publications

References 8 publications

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Threshold Based Data Reduction for Prolonging Life of Wireless Sensor Network

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