The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam

Liang, Min-Chih; Chen, Hung-En; Tfwala, Samkele S.; Lin, Yu-Feng; Chen, Su‐Chin

doi:10.3390/s23083795

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…The emergence of Wireless Sensor Network (WSN) technology has opened up new opportunities for data-intensive observations in ecological and hydrological research, as well as environmental monitoring. Initially, WSNs were deployed to compensate for the lack of space observation data in models used for research on ecological and hydrological processes in watersheds [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. This limitation had previously hindered the accurate construction of modeling and simulation techniques [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Zhang,

Hou,

Huang

2023

Sensors

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This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m3/m3) of LESTKF was achieved with a 20 km observation search radii and 0.01 m3/m3 observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.

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Section: Introductionmentioning

confidence: 99%

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Zhang,

Hou,

Huang

2023

Sensors

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Radio Frequency Energy Harvesting for Underground Sensor Nodes: Possibilities and Challenges

Mahenge,

Sinde,

Dida

et al. 2024

IEEE Access

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Radio frequency energy harvesting (RFEH) is considered an optimal and environmentally friendly solution for energizing sensor devices and prolonging the lifetime of wireless sensor networks (WSNs). Despite being studied and experimented in several environments where WSNs are used, studies and experiments related to RFEH in underground wireless systems are limited to near-field wireless power transfer (WPT), measurement of received signal strength, and current conduction. The goal of this study is to examine the possibilities and challenges of actualizing RFEH in wireless underground sensor networks (WUSNs). A radio-frequency (RF) spectral survey was conducted, and a comparison was performed with similar surveys conducted worldwide to determine the generally available ambient RF energy. Using the aboveground to underground (AG2UG) RF communication model, the signal path loss was analyzed under varying conditions. By relating the ambient RF power and AG2UG signal path loss, it was found nearly impossible to harvest ambient RF energy with the harvesting antenna buried within the soil, as the best-case environment will require a rectenna with sensitivity of at least -62.75dBm. However ambient RF energy can be harvested when the harvesting antenna is in free space, while the other components are underground and will require a high sensitivity of at least -40 dBm. Another possibility for underground RFEH is the use of a dedicated WPT device located 1m above the ground, transmitting at 20 dBm with the RF energy harvester 30 cm below the soil surface with a sensitivity of at least -30 dBm.INDEX TERMS Internet of Underground Things (IoUT), Radio frequency energy harvesting (RFEH), wireless power transfer (WPT), wireless underground sensor networks (WUSNs).

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Smart Geotechnics: Enhancing Infrastructure Resilience with IoT and AI

Firoozi,

Firoozi

2024

JCEU

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The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) presents transformative opportunities for geotechnical engineering, fundamentally reshaping the monitoring and maintenance of infrastructure. This paper delves into the synergistic application of IoT sensors and AI algorithms to facilitate real-time monitoring, predictive maintenance, and risk management, significantly enhancing the resilience and sustainability of critical infrastructure such as bridges, tunnels, and foundations. Through a rigorous examination of theoretical frameworks, a review of pertinent literature, and detailed case studies, the study underscores the substantial benefits of these technologies, including improved operational efficiency, enhanced safety, and reduced environmental impact. Additionally, it addresses the predominant challenges of data security, system integration, and scalability, and suggests future research directions and policy considerations to overcome these barriers. The paper advocates for the broader adoption of smart geotechnics, highlighting its crucial role in advancing sustainable and resilient infrastructure in the era of smart cities.

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The Application of Wireless Underground Sensor Networks to Monitor Seepage inside an Earth Dam

Cited by 3 publications

References 45 publications

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

Radio Frequency Energy Harvesting for Underground Sensor Nodes: Possibilities and Challenges

Smart Geotechnics: Enhancing Infrastructure Resilience with IoT and AI

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