Multiple linear regression to improve prediction accuracy in WSN data reduction

Carvalho, Carlos Giovanni Nunes de; Gomes, Danielo G.; Souza, José Neuman de; Agoulmine, Nazim

doi:10.1109/lanoms.2011.6102268

Cited by 18 publications

(10 citation statements)

References 10 publications

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“…The pseudo code for initialization phase is shown in Figure 4 and the design of the proposed model is shown in Figure 5. To evaluate how effective PCA is for dimensionality reduction, this study used the approximation accuracy and approximation error as they are common metrics widely used by the extant research Carvalho et al, [22]; Le Borgne et al, [23]. In addition, the performance of the proposed WSN's anomaly detection model is measured by several metrics including detection accuracy, detection rate, false positive rates and false negative rates.…”

Section: A the Proposed Pca-based On Anomaly Detection Modelmentioning

confidence: 99%

Medical Healthcare System Based on Wireless Body Area Networks: The Importance of Anomaly Detection

Hassaballah¹,

Iraq²,

Ali³

et al. 2020

IJEAT

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In this research paper, a modern framework is presented to detect anomaly in medical wireless body sensor network systems that are incorporated in distant observation of patient’s vital signs. The suggested framework effects analysis of data in a sequential manner using a mini gateway utilized as a root station to discover abnormal alterations and to deal with inaccurate computations in gathered medical information minus advance awareness of irregular occurrences or consistent data patterns. The suggested perspective relies on Principal Component Analysis (PCA) utilized in spatial analysis and dimension reduction for gathered medical details. The key goal is distinguishing defective calculations from clinical dangers for reduction of false alarms prompted by incorrect computations or ill-behaved sensors. The result from the experiments on real medical datasets reveal that the suggested PCA perspective is able to attain good discovery perfection with lesser falt alarm rate when contrasted with other perspectives that fail to minimize the excessive dimension of gathered more information so in multivariate Wireless Body Sensor Networks (WBSN) implementations, and dynamic streaming nature of sensor data, mostly in medical implementations.

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Section: A the Proposed Pca-based On Anomaly Detection Modelmentioning

confidence: 99%

Medical Healthcare System Based on Wireless Body Area Networks: The Importance of Anomaly Detection

Hassaballah¹,

Iraq²,

Ali³

et al. 2020

IJEAT

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“…In de Carvalho et al [44], the authors proposed to use a method based on multiple linear regression to improve prediction accuracy. The improvement is achieved by the multivariate correlation of readings gathered by sensor nodes in the field.…”

Section: G Energy Saving Techniques In Wsnmentioning

confidence: 99%

A Review of Data Gathering Algorithms for Real-Time Processing in Internet of Things Environment

Kadhim¹,

Wahid²

2020

IJACSA

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Today, Wireless Sensor Network (WSN) has become an enabler technology for the Internet of Things (IoT) applications. The emergence of various applications has then enabled the need for robust and efficient data collection and transfer algorithms. This paper presents a comprehensive review for the existing data gathering algorithms and the technologies adopted for that applications. After reviewing the algorithms and the challenges related to them, which extend the physical reach of the monitoring capability; they possess several constraints such as limited energy availability, low memory size, and low processing speed, which are the principal obstacles to designing efficient management protocols for WSN-IoT integration.

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“…Based the analysis for the performance of the tradeoff in [ 19 , 20 ], an energy-efficient framework for clustering-based data collection is proposed in [ 21 ], where the benefit of adaptive scheme to enable/disable prediction operations is exploited. To improve prediction accuracy, authors of [ 22 , 23 ] perform prediction of data based on the multivariate correlation and multiple linear regression methods, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks

Tan

Xiong

2014

Sensors

101

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One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.

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Multiple linear regression to improve prediction accuracy in WSN data reduction

Cited by 18 publications

References 10 publications

Medical Healthcare System Based on Wireless Body Area Networks: The Importance of Anomaly Detection

Medical Healthcare System Based on Wireless Body Area Networks: The Importance of Anomaly Detection

A Review of Data Gathering Algorithms for Real-Time Processing in Internet of Things Environment

A Structure Fidelity Approach for Big Data Collection in Wireless Sensor Networks

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