An Efficient Deductive Learning Approach on LPWAN for Optimizing Power Consumption by Using Gaussian Process
Author(s):
Dr.M.Kumarasamy
Keywords:
Puk kernel, LPWAN, Gaussian approach, Mean Absolute Error, Poly kernel
Abstract
In the last years, the Internet of Things (IoT) has emerged as a key application context inthe design and evolution of technologies in the transition toward a 5G ecosystem. More and moreIoT technologies have entered the market and represent important enablers in the deployment ofnetworks of interconnected devices. As network and spatial device densities grow, energy efficiencyand consumption are becoming an important aspect in analyzing the performance and suitability ofdifferent technologies. In this framework, this survey presents an extensive review of IoT technologies,including both Low-Power Short-Area Networks (LPSANs) and Low-PowerWide-Area Networks(LPWANs), from the perspective of energy efficiency and power consumption.A low-power wide-area network or low-power wide-area network or low-power network is a type of wireless telecommunication wide area network designed to allow long-range communications at a low bit rate among things, such as sensors operated on a battery. This proposed system finds that the efficient model for producing best optimal solution. Here, all the kernels are producing strongly correlated coefficient which is from 0.77 to 0.82 of correlation coefficient. The Normalized kernel is having highest correlation coefficient value which is 0.82. The RBF kernel is having lowest correlation coefficient which is 0.77. The Poly kernel has highest mean absolute error value which is 288.08%.The Lowest mean absolute error value is 218.94% which is produced by Normalized kernel of Gaussian approach. The Poly kernel of Gaussian approach has highest root mean squared error value which is 364.63%. The least root mean squared error value is 212.24% which is produced by Normalized kernel of Gaussian approach. Poly kernel of Gaussian classifier has highest relative absolute error value which is 94.48%. The least relative absolute error value is 81.43% which is produced by Normalized kernel of Gaussian classifier. The Poly kernel of Gaussian approach has highest root relative squared error value which is 95.95%. The least root relative squared error value is 79.71% which is produced by Normalized kernel of Gaussian approach. This system finds that the Normalized kernel of Gaussian classifier model give
Article Details
Unique Paper ID: 153731

Publication Volume & Issue: Volume 8, Issue 8

Page(s): 324 - 329
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