Abstract
Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.
Highlights
Wireless sensor network (WSN) is the backbone of ubiquitous computing applications such as military surveillance, disaster recoveries, environmental and structural monitoring, health and security monitoring and control, wildlife monitoring and precision agriculture, and habitat monitoring
In WSN applications where continuous monitoring of sensed phenomenon is extremely important, continuous communication of the sensed data is essential to maintain the reliability of the data received at the base station and importantly to be able to detect any changes in the sensed environment
The predictions of autoregressive integrated moving average (ARIMA) presented nonseasonal differencing with a constant term from the last value of original data reading, except for predictions of temperature where predictions were of the first-order autoregression
Summary
Wireless sensor network (WSN) is the backbone of ubiquitous computing applications such as military surveillance, disaster recoveries, environmental and structural monitoring, health and security monitoring and control, wildlife monitoring and precision agriculture, and habitat monitoring. In WSN applications where continuous monitoring of sensed phenomenon is extremely important (e.g., health monitoring systems, structural health monitoring systems, road traffic monitoring systems, and water quality monitoring systems [4]), continuous communication of the sensed data is essential to maintain the reliability of the data received at the base station and importantly to be able to detect any changes in the sensed environment. Such continuous transmissions add communication costs of the network that may deplete the energy of the batteries powering the nodes. Our simulations indicate that we were able to determine the optimum number of data sets required and the longest sleep period required for the prediction that minimizes energy consumption without compromising on the reliability and accuracy of data received
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