Evaluation of Wireless Sensor Node for Measuring Slope Inclination in Geotechnical Applications

Furlani, Karen M.; Miller, Patrick K.; Mooney, Michael A.

doi:10.22260/isarc2005/0017

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Current applications of wireless sensors include measurements of earth-slope inclination, landslides, strong ground motion, and soil-structure interactions [11–15]. In the examples cited above the sensors are attached to the exterior of the system of interest and embedded using supplementary frames.…”

Section: Existing Monitoring Techniquesmentioning

confidence: 99%

Subsurface Event Detection and Classification Using Wireless Signal Networks

Yoon

Ghazanfari

Cheng

et al. 2012

Sensors

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Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

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Section: Existing Monitoring Techniquesmentioning

confidence: 99%

Subsurface Event Detection and Classification Using Wireless Signal Networks

Yoon

Ghazanfari

Cheng

et al. 2012

Sensors

View full text Add to dashboard Cite

show abstract

“…Current applications of wireless sensors include measurements of earth-slope inclination, landslides, strong ground motion, and soil-structure interactions [6] [10]. In the examples cited above the sensors are attached to the exterior of the system of interest and embedded using supplementary frames and are designed to measure a specific property (point measurement) and transmit the collected data via wireless communication.…”

Section: Subsurface Geo-sensing and Monitoring Using Wsnsmentioning

confidence: 99%

Challenges of subsurface geo-sensing and monitoring using wireless signal networks

et al. 2012

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Real time global subsurface geo-sensing and monitoring can be achieved using a new concept of wireless sensor networks. The new concept uses the signal strength variation of wireless sensor nodes in the host medium as the main indicator of a subsurface event or change in physical properties of the soil such as water content, density, and formation of discontinuities. The feasibility of implementing the wireless signal networks for subsurface monitoring has been investigated through laboratory simulation experiments which demonstrated that the wireless signal networks is a viable approach for a reliable geo-sensing and monitoring of subsurface hazards. The challenges facing the wireless signal networks for implementation in geo-sensing and subsurface monitoring are discussed in this paper. These challenges are divided into network and geo-media categories and potential solutions to these challenges are offered for most cases.

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“…Bö hm and Haala (2005) reported, however, that accuracy was degraded significantly (to accuracies of 6u, in some cases) with proximity to cars or other sources of electromagnetic fields. The accuracy of digital inclinometers cover a broad range, from 0.013u for geotechnical engineering applications (Furlani et al 2005) up to 0.2u for handheld models.…”

Section: Adapting the System To Mobile Platformsmentioning

confidence: 99%

Creating and delivering augmented scenes

Mower

2009

International Journal of Geographical Information Science

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An augmented scene delivery system (ASDS) creates and distributes annotated cartographic products in real time from captured still imagery. An ASDS applies augmented reality technology to the visualization of unrestricted exterior viewsheds through an adaptive, perspective-based surface resampling model. This paper describes an ASDS testing platform, introduces a client/server model for distributing augmented scenes over the Internet, presents a linear-time algorithm for resampling dense elevation models for perspective imaging and conducts an execution analysis of the algorithm's implementation, demonstrating that augmented scenes can be constructed and delivered efficiently. The paper also presents a migration path to mobile ASDS platforms with a discussion of the remaining technological problems associated with its implementation.

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Evaluation of Wireless Sensor Node for Measuring Slope Inclination in Geotechnical Applications

Cited by 6 publications

References 3 publications

Subsurface Event Detection and Classification Using Wireless Signal Networks

Subsurface Event Detection and Classification Using Wireless Signal Networks

Challenges of subsurface geo-sensing and monitoring using wireless signal networks

Creating and delivering augmented scenes

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