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An Underground Radio Wave Propagation Prediction Model for Digital Agriculture

Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA
Information 2019, 10(4), 147; https://doi.org/10.3390/info10040147
Received: 20 March 2019 / Revised: 10 April 2019 / Accepted: 16 April 2019 / Published: 18 April 2019
(This article belongs to the Section Information and Communications Technology)
Underground sensing and propagation of Signals in the Soil (SitS) medium is an electromagnetic issue. The path loss prediction with higher accuracy is an open research subject in digital agriculture monitoring applications for sensing and communications. The statistical data are predominantly derived from site-specific empirical measurements, which is considered an impediment to universal application. Nevertheless, in the existing literature, statistical approaches have been applied to the SitS channel modeling, where impulse response analysis and the Friis open space transmission formula are employed as the channel modeling tool in different soil types under varying soil moisture conditions at diverse communication distances and burial depths. In this article, an electromagnetic field analysis is presented as an enhanced monitoring approach for subsurface radio wave propagation and underground sensing applications in the field of digital agriculture. The signal strength results are shown for different distances and depths in the subsurface medium. The analysis shows that the lateral wave is the dominant wave in subsurface communications. Moreover, the shallow depths are more suitable for soil moisture sensing and long-range underground communications. The developed paradigm leads to advanced system design for real-time soil monitoring applications. View Full-Text
Keywords: signals in the soil; electromagnetic waves; sensors for real-time monitoring of soil; digital agriculture; wireless underground communications; underground sensing; subsurface antenna signals in the soil; electromagnetic waves; sensors for real-time monitoring of soil; digital agriculture; wireless underground communications; underground sensing; subsurface antenna
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Salam, A. An Underground Radio Wave Propagation Prediction Model for Digital Agriculture. Information 2019, 10, 147.

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