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Improving Accuracy of the Shewhart-basedData-Reduction in IoT Nodes using PiggybackingAuthors: Anish Shastri,Vivek Jain,Sachin Chaudhari,Shailesh Chouhan,Stefan Werner Conference: IEEE 5th World Forum on Internet of Things Location Limerick, Ireland IEEE Date: 2019-04-15 Report no: IIIT/TR/2019/26 AbstractThis paper proposes the use of Shewharttest to reduce the number of data-transmissions in IoTnetworks. It is shown to outperform the widely-used leastmean square (LMS) based data reduction method interms of the number of data-transmissions, implementationcomplexity and mean square error (MSE) in predictionof time-series data at the sink node based on the partialtransmissions of the measured time-series data from thesensor node. The paper also proposes the use of piggy-backing and interpolation to further reduce the MSE ofthe estimated time-series data at the sink node withoutincreasing the number of packet transmissions. The time-series data used for the comparison of data reductionalgorithms is a set of measured temperature values inindoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, thetotal current consumption of the sensor node is measuredfor different number of piggybacks and correspondingbattery lifetime is estimated. It is shown that the proposedpiggyback approach significantly reduces the MSE at thecost of slight decrease in battery-lifetime Full paper: pdf Centre for Communications |
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