Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table

Long-term Monitoring of Soil Surface Deformation with RFID

International audiencePassive Radio-Frequency Identification (RFID) has been used to monitor landslide displacement since approximately 5 years. This method allows soil displacement estimation at a high spatio-temporal resolution, and at a relatively low cost. In perspective of the previous years, this paper proposes to summarize the various challenges encountered with the longterm outdoor RFID localization method, and presents solutions that were implemented to overcome these challenges. Finally, displacement results from three monitored sites are shown in order to validate the implemented solutions

Kalman Smoothing for better RFID Landslide Monitoring

International audienceThe use of Radio-Frequency Identification (RFID) in Earth Sciences has been growing in the recent years, notably for landslide monitoring using phase-of-arrival localization schemes. In this article, an Extended Kalman Filtering approach is presented to exploit RFID phase data for landslide displacement monitoring. The filtering is based on a stochastic Langevin equation for the state-space model, introducing a heuristic coupling based on the mechanical continuity of the landslide material. This helps correct measurement biases and deal with missing data in the tracking of multiple tags. The Kalman state covariance matrix is a useful indicator of the tags localization quality. It can be exploited to discriminate true displacements from multipathinduced artifacts. Phase unwrapping is performed implicitly through the state model

Kalman Smoothing for better RFID Landslide Monitoring

International audienceThe use of Radio-Frequency Identification (RFID) in Earth Sciences has been growing in the recent years, notably for landslide monitoring using phase-of-arrival localization schemes. In this article, an Extended Kalman Filtering approach is presented to exploit RFID phase data for landslide displacement monitoring. The filtering is based on a stochastic Langevin equation for the state-space model, introducing a heuristic coupling based on the mechanical continuity of the landslide material. This helps correct measurement biases and deal with missing data in the tracking of multiple tags. The Kalman state covariance matrix is a useful indicator of the tags localization quality. It can be exploited to discriminate true displacements from multipathinduced artifacts. Phase unwrapping is performed implicitly through the state model

Long-term Monitoring of Soil Surface Deformation with RFID

International audiencePassive Radio-Frequency Identification (RFID) has been used to monitor landslide displacement since approximately 5 years. This method allows soil displacement estimation at a high spatio-temporal resolution, and at a relatively low cost. In perspective of the previous years, this paper proposes to summarize the various challenges encountered with the longterm outdoor RFID localization method, and presents solutions that were implemented to overcome these challenges. Finally, displacement results from three monitored sites are shown in order to validate the implemented solutions

Long-term Monitoring of Soil Surface Deformation with RFID

International audiencePassive Radio-Frequency Identification (RFID) has been used to monitor landslide displacement since approximately 5 years. This method allows soil displacement estimation at a high spatio-temporal resolution, and at a relatively low cost. In perspective of the previous years, this paper proposes to summarize the various challenges encountered with the longterm outdoor RFID localization method, and presents solutions that were implemented to overcome these challenges. Finally, displacement results from three monitored sites are shown in order to validate the implemented solutions

RFID landslide monitoring : long-term outdoor signal processing and phase unwrapping

Localization of passive Radio-Frequency Identification (RFID) tags has been used to monitor landslide surface displacement since 5 years. This method, applied on slow displacements lower than 1cm per day, allows a high spatiotemporal resolution at a relatively low cost. With the feedback of the previous years, this paper proposes to summarize the various challenges encountered with the long-term outdoor RFID localization method, and presents data-processing solutions that were implemented to overcome these challenges. We propose a complex-smoothing unwrapping algorithm, a multi-frequency merging operation, as well as multi-tag and multi-antenna phase combining method. The concept of an unwrapping reference guide is presented and applied with groups of tags showing coherent displacements, or with absolute reference measurements. These approaches allow a higher data availability up to 38% for one site over multiple years, and a better phase unwrapping. Earth surface displacement monitoring with RFID proves to be a robust and accurate solution, with four equipped sites across France and Switzerland

Long-term Monitoring of Soil Surface Deformation with RFID

International audiencePassive Radio-Frequency Identification (RFID) has been used to monitor landslide displacement since approximately 5 years. This method allows soil displacement estimation at a high spatio-temporal resolution, and at a relatively low cost. In perspective of the previous years, this paper proposes to summarize the various challenges encountered with the longterm outdoor RFID localization method, and presents solutions that were implemented to overcome these challenges. Finally, displacement results from three monitored sites are shown in order to validate the implemented solutions

Kalman Smoothing for better RFID Landslide Monitoring

International audienceThe use of Radio-Frequency Identification (RFID) in Earth Sciences has been growing in the recent years, notably for landslide monitoring using phase-of-arrival localization schemes. In this article, an Extended Kalman Filtering approach is presented to exploit RFID phase data for landslide displacement monitoring. The filtering is based on a stochastic Langevin equation for the state-space model, introducing a heuristic coupling based on the mechanical continuity of the landslide material. This helps correct measurement biases and deal with missing data in the tracking of multiple tags. The Kalman state covariance matrix is a useful indicator of the tags localization quality. It can be exploited to discriminate true displacements from multipathinduced artifacts. Phase unwrapping is performed implicitly through the state model

Long-term Monitoring of Soil Surface Deformation with RFID

International audiencePassive Radio-Frequency Identification (RFID) has been used to monitor landslide displacement since approximately 5 years. This method allows soil displacement estimation at a high spatio-temporal resolution, and at a relatively low cost. In perspective of the previous years, this paper proposes to summarize the various challenges encountered with the longterm outdoor RFID localization method, and presents solutions that were implemented to overcome these challenges. Finally, displacement results from three monitored sites are shown in order to validate the implemented solutions