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Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA

Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA

Kalyan Das, Satyabrata Das
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 17
ISSN: 1548-3673|EISSN: 1548-3681|EISBN13: 9781799893868|DOI: 10.4018/IJeC.290292
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MLA

Das, Kalyan, and Satyabrata Das. "Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA." IJEC vol.18, no.1 2022: pp.1-17. http://doi.org/10.4018/IJeC.290292

APA

Das, K. & Das, S. (2022). Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA. International Journal of e-Collaboration (IJeC), 18(1), 1-17. http://doi.org/10.4018/IJeC.290292

Chicago

Das, Kalyan, and Satyabrata Das. "Energy-Efficient Cloud-Integrated Sensor Network Model Based on Data Forecasting Through ARIMA," International Journal of e-Collaboration (IJeC) 18, no.1: 1-17. http://doi.org/10.4018/IJeC.290292

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Abstract

An energy-efficient Model for Sensor-cloud is proposed based on data forecasting through an autoregressive integrated moving average (ARIMA). Generally, all the user requests are redirected to the wireless sensor network (WSN) through the cloud. In the traditional approach, user requests are generated every fifteen minutes, so the sensor must send data to the cloud every fifteen minutes. In the current approach, the sensors within the WSN communicate with the cloud every two hours. The data forecasting technique addresses most of the user requests using the ARIMA one-step ahead forecasting model in the cloud. This results in less frequency of data communication, thereby increasing the battery life of the sensor. The ARIMA-based forecasting model provides better accuracy because of fewer temperature data changes with respect to the current temperature, for the next two hours. The proposed method for the simulation in the sensor cloud system consumes significantly less energy than the traditional approach, and the error in forecasting becomes highly negligible.

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