In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing

Wu, Xiaopei; Liu, Mingyan

doi:10.1109/ipsn.2012.6920949

Cited by 45 publications

(79 citation statements)

References 26 publications

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“…Compressive sensing can be used as an efficient sampling strategy. In [31], the author considered the problem of monitoring soil moisture with a wireless sensor networks. With compressive sensing, they achieve high accuracy at no more than 10% of the traditional sampling rate.…”

Section: Related Workmentioning

confidence: 99%

Efficient background subtraction for real-time tracking in embedded camera networks

Shen

Liu

et al. 2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

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Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

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Section: Related Workmentioning

confidence: 99%

Efficient background subtraction for real-time tracking in embedded camera networks

Shen

Liu

et al. 2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

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“…Recent work has been done on smart or reactive monitoring, where the network responds to important events, such as precipitation by increasing the monitoring frequency for the event duration [Cardell-Oliver et al, 2005, Wu andLiu, 2012].…”

Section: Soil Moisture Sensingmentioning

confidence: 99%

“…As mentioned in section 2.1, research has been conducted on programing soil sensors to automatically respond to events or soil conditions. For example, systems that automatically increase monitoring in response to precipitation events [Wu and Liu, 2012] or automated variable rate irrigation systems [Hedley and Yule, 2009, Kim et al, 2009, Vellidis et al, 2008, Yunseop et al, 2008.…”

Section: Soil Moisture For Crop Growth Modelingmentioning

confidence: 99%

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

Phillips

Newlands

Liang

et al. 2014

Computers and Electronics in Agriculture

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Determining the best way to efficiently use limited water resources, for food and energydedicated crops, has become crucial due to the rise in extreme events (floods/droughts) and higher variability in rainfall attributed to global climate change. Changing climate conditions will require new crops to be adapted to a changing agricultural environment. Reliable information on seasonal trends in crop growth and evapotranspiration with associated uncertainty/confidence ranges is crucial to guide the development of new crops and management strategies to cope with future climate. Given that crop growth is strongly coupled to soil moisture, developing reliable growth curves requires a detailed understanding of soil moisture at the field-scale. Typically, it is impractical to collect soil samples to adequately assess soil moisture that represents both spatial distribution at the field-scale and temporal dynamics on the scale of a growing season (e.g. 110 days for cereals). A novel way to address soil moisture monitoring challenges is through an integrated, agro-ecosystems-level approach using an integrated sensing system that can link data from multiple platforms (wireless sensors, satellites, airborne imagery, near real-time climate stations). Assimilated data can then be fed into predictive models to generate reference crop growth curves and predict regionally-specific yield potentials. However, integrated sensing requires interagency cooperation, common data processing standards and long-term, timely access to data. Large databases need to be reusable by various organizations and accessible in the future, with comprehensive metadata. During the 2012 growing season a feasibility study was conducted which involved measuring field-scale soil moisture with sensor network technology. The experiment utilized radially-distributed sensors for tracking in-season soil moisture data was collect using both automated in-situ sensors and hand-held sensors. Box-plots of soil moisture data was collected with hand-held revealed an early season wet soil moisture regime and late season dry soil moisture regime. The data sampled on July 5, 2012 was selected for ii geostatistical analysis. Bayesian kriging models and Bayesian kriging models with polynomial trends using different combinations prior distributions for the range and nugget were tested. Models with first order polynomial trend, a reciprocal 2 prior distribution for the range and a reciprocal prior distribution for the nugget fit tended to predict the sample distributions the best. Soil moisture was predicted at a set of random point using ordinary kriging, universal kriging, the two Bayesian kriging models and the Bayesian kriging models with first and second order polynomial trends. Overall, the universal kriging and Bayesian trend models predicted similar data distributions. OpenGIS-compliant services and standards were utilized to provide long-term access to sensor data and construct corresponding metadata. Sensor Model Language, an inter-operable metadata format, ...

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“…The IEEE 802.15.4 based ZigBee (ZigBee Alliance, 2004) and a ZigBee-like propriety networking protocol, XMesh (Crossbow, 2007a), among others, ported to some of the node hardware platforms, have made significant impact on a broad range of applications. Over a few years, many applications of WSN have appeared in areas such as building automation (ZigBee Alliance, 2006), (Kazmi et al, 2014), health care (ZigBee Alliance, 2008), defense and surveillance (He et al, 2006), (Sun et al, 2011), precision agriculture (Burrell et al, 2004), (Tim et al, 2007), (Keshtgari and Deljoo, 2012), (Wu et al, 2015), disaster management (Ahmad et al, 2011), transportation systems (Katiyar et al, 2011), (Felemban and Sheikh, 2014), and others (Li and Xiong, 2013), (Mehdipour et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Control and Assessment

Pahuja¹,

Verma²,

Uddin³

2017

Journal of Biosystems Engineering

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Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Keywords IntroductionThe wireless sensor network (WSN) is one of the upcoming, multidisciplinary, and pervasive computing technologies of the 21st century (Estrin et al., 2002). With its wide range of application potential, it is invading environments and bridging the gap between the real physical world of sensory data and the digital world of computers and internet. WSN is achieving the high-end goal of creating real sensor webs, which may be called the "next-generation internet" (Zhao and Guibas, 2004). A typical WSN consists of a number of sensor nodes, spatially distributed into an application environment, that cooperatively monitor the phenomenon of interest and communicate data to the gateway node via a low-power true-mesh multihop routing for end user access (Sohraby et al., 2007). Each sensor node is embedded with sensing, computing, and wireless communication capabilities to form a data collection network. The utility of WSN is further enhanced by integrating it with an actuator network...

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In-situ soil moisture sensing: Measurement scheduling and estimation using compressive sensing

Cited by 45 publications

References 26 publications

Efficient background subtraction for real-time tracking in embedded camera networks

Efficient background subtraction for real-time tracking in embedded camera networks

Integrated sensing of soil moisture at the field-scale: Measuring, modeling and sharing for improved agricultural decision support

An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Control and Assessment

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