A Data Prediction Model based on Extended Cosine Distance For Maximizing Network Lifetime of WSN

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Abstract: Wireless Sensor Network is a randomly deployed collection of sensor nodes with the aim to collect the information near its sensing area. The approach is to sense some event, record its respective data value and transmit it to a sink where this data value is utilized and thus become information. The values received by the sink may contain duplicate, inappropriate and inconsistent values. The new research design may focus on collecting and sending only that value which may be utilized by the sink. The transmission of irrelevant data is avoided to increase the performance and lifetime. This paper aims at providing a data management technique which reduces load on sensor nodes to enhance network lifetime. To reduce extra burden on sensor nodes a data prediction model is built which restricts data transmission by predicting future data values. The algorithm finds relationship between data values. The goal is to calculate degree of relatedness between these values so to establish a relation which predicts future value. The proposed approach is compared with existing linear regression model, dual prediction model and least mean square model. The result reflects that the approach presents better results in terms of prediction accuracy and total energy consumption.

WSEAS Transactions on Computer Research, ISSN / E-ISSN: 1991-8755 / 2415-1521, Volume 7, 2019, Art. #3

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Arun Agarwal, Amita Dev, "A Data Prediction Model based on Extended Cosine Distance For Maximizing Network Lifetime of WSN," WSEAS Transactions on Computer Research, vol. 7, pp. 23-28, 2019, DOI:
Arun Agarwal, Amita Dev. A Data Prediction Model based on Extended Cosine Distance For Maximizing Network Lifetime of WSN. WSEAS Transactions on Computer Research. 2019;7:23-28.
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