Impact of propagation medium on link quality for underwater and underground sensors

Horvat, Goran; Vinko, Davor; Vlaovic, Jelena

doi:10.1109/mipro.2016.7522125

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…As stated in Section 2 , no related studies presenting a similar approach with respect to the one of this paper were found. Indeed, many contributions highlighted the relationship between the dielectric properties of soil, mainly depending on VWC, and wireless links quality indicators (e.g., RSSI), but such correlations were not exploited to perform inverse sensing of VWC [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. On the other hand, other studies set up systems for inverse sensing of VWC [ 23 , 24 , 25 ], although such solutions were characterised by system infrastructures and sensing techniques which are much more complicated and expensive than the one proposed in this paper.…”

Section: Results and Discussionmentioning

confidence: 99%

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Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning

Bertocco

Parrino

Peruzzi

et al. 2023

Sensors

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This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of 3.33%, a virtual sensor of 8.67%, and an augmented sensor of 1.84%, which improves down to 1.53% if filtered in post-processing.

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Section: Results and Discussionmentioning

confidence: 99%

“…In addition, the specific effects of soil on LoRa systems have been considered. For example, the correlation between performance drops, inter-nodal distance and soil moisture was analysed in [ 22 ]. Moreover, Refs.…”

Section: Related Workmentioning

confidence: 99%

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning

Bertocco

Parrino

Peruzzi

et al. 2023

Sensors

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“…The impact of the carrier frequency, transmission distance, depth, and modulation type was experimentally tested in [47]- [49] for UGWSNs. The optimum frequency range in 10-100 MHz was identified in [50] for energy harvesting in UGWSNs.…”

Section: A Channel Modelingmentioning

confidence: 99%

Toward the Internet of Underground Things: A Systematic Survey

Saeed

Alouini

Al-Naffouri

2019

IEEE Commun. Surv. Tutorials

101

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This paper provides recent advances in the area of Internet of Underground Things (IoUT) with emphasis on enabling communication technologies, networking issues, and localization techniques. IoUT is enabled by underground things (sensors), communication technology, and networking protocols. This new paradigm of IoUT facilitates the integration of sensing and communication in the underground environment for various industries, such as oil and gas, agriculture, seismic mapping, and border monitoring. These applications require to gather relevant information from the deployed underground things. However, the harsh underground propagation environment including sand, rock, and watersheds do not allow the use of single communication technology for information transfer between the surface and the underground things. Therefore, various wireless and wired communication technologies are used for underground communication. The wireless technologies are based on acoustic waves, electromagnetic waves, magnetic induction and visible light communication while the wired technologies use coaxial cable and optical fibers. In this paper, state-of-art communication technologies are surveyed, and the respective networking and localization techniques for IoUT are presented. Moreover, the advances and applications of IoUT are also reported. Also, new research challenges for the design and implementation of IoUT are identified.

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“…Furthermore, [177][178][179] studies the effect of carrier frequency, transmission distance, type of modulation, and depth for Underground Wireless Sensor Networks (UGWSNs). In [21,180], author proposed the code division multiplexing scheme for improving the battery life and signal-to-noise ratio.…”

Section: Em Channel Modelingmentioning

confidence: 99%

A Survey on Subsurface Signal Propagation

Raza

Salam

2020

Smart Cities

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Wireless Underground Communication (WUC) is an emerging field that is being developed continuously. It provides secure mechanism of deploying nodes underground which shields them from any outside temperament or harsh weather conditions. This paper works towards introducing WUC and give a detail overview of WUC. It discusses system architecture of WUC along with the anatomy of the underground sensor motes deployed in WUC systems. It also compares Over-the-Air and Underground and highlights the major differences between the both type of channels. Since, UG communication is an evolving field, this paper also presents the evolution of the field along with the components and example UG wireless communication systems. Finally, the current research challenges of the system are presented for further improvement of the WUCs.

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Impact of propagation medium on link quality for underwater and underground sensors

Cited by 5 publications

References 22 publications

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning

Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning

Toward the Internet of Underground Things: A Systematic Survey

A Survey on Subsurface Signal Propagation

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