Progress 10/01/13 to 09/30/14
Outputs
Target Audience: 1. Graduate and undergraduate students: 1). Classroom teaching and lab exercises: provide students basic knowledge and skills on the design of wireless sensor network technology for crop production 2). Hands-on projects: provide students opportunities to apply theoretical knowlege to real-world design practices 2. Crop producers and county extension officers: 1) Workshops and meetings: present and demonstrate the current status on the technology development 2) Field days: demonstrate the developed technology in field set-ups 3. Researchers and scientists 1) Technical conferences: Presentations on the technology development and deployment 2) Journal publications 3) Lab and field visits: invite researchers and scientists to visit the lab and field experimental sites. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? 1. Graduate student training A PhD student conducted the research. She designed and completed all the experiments, conducted data analysis, and developed the models. She graduated in 2013. 2. Visiting students Several short-term visiting students and professional participated the experiments and data analysis which exposed them with the new technology and systematic experiment design. How have the results been disseminated to communities of interest? The results of the research were disseminated through: 1. Technical conferences for crop scientists and researchers 2. Giving presentation for scientists in crop science, electrical engineering, and computer sciences, etc. 3. Demonstrate the developed system to extension officers 3. Demonstrate the developed system to high school students and teachers during annual 4H activities. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Experiments of 2.4GHz radio propagation were designed and conducted as corn plants grew. Antenna height and growth stage of corn were considered as important factors. Antenna height including three levels was classified by the area of Fresnel zone. First level of antenna height was corresponding to variable Fresnel zone as the changes in plant height. Second level of antenna height kept 60% Fresnel zone clear during the development of corn plants. The third level of antenna height was set to close to 0 Fresnel zone. Growth stage as the second factor included V6, V8, V10, V12, VT and R5. The first experiment was 3*5 factorial structure from V6 to VT corresponding to vegetation growth stage of corn plant. The second experiment was 2*2 factorial structure including the first second antenna levels corresponding to VT and R5 because these the plant height did not change at these two growth stages. Fundamental path loss modeling was developed under these treatments. There were three repetitions for each treatment. Each repetition was to training path loss model along a transmission direction in a corn field. Three repetitions were corresponding to three transmission directions: angle, column, and row. A path loss model was established when antenna was set at antenna height level 2.The results indicated that there was no significant difference effect on mean of path loss exponent from growth stages, and antenna height has a significant effect on mean of path loss model from V6 to VT. This implied plant did not have significant effect on the speed of energy decay, but the area of Fresnel zone played an important effect on energy decay. The compare developed model to free space model showed the developed model was better to predict path loss than free space model.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2013
Citation:
Yu, X., P. Wu, N. Wang, W. Han, and Z. Zhang. 2013. Development of a new
wireless sensor network communication. Journal of Computers. 8(10): 2455-2460
- Type:
Journal Articles
Status:
Published
Year Published:
2013
Citation:
Li, Z. N. Wang, T. Hong. 2013. RF Propagation Patterns at 915 MHz and 2.4 GHz Bands for In-field Wireless Sensor Networks. Transactions of ASABE. 56(2):787-796.
- Type:
Journal Articles
Status:
Published
Year Published:
2013
Citation:
Yu, X., P. Wu, Z. Zhang, N. Wang, W. Han. 2013. Electromagnetic Wave Propagation in
Soil For Wireless Underground Sensor Networks. Progress in Electromagnetics
Research M. 30(11): 11-23.
- Type:
Journal Articles
Status:
Published
Year Published:
2014
Citation:
Li, Z., N. Wang, A., P. Taher, C. Godsey, H. Zhang, X. Li. 2011. Practical deployment of an in-field soil property wireless sensor network. Computer Standards & Interfaces. 36(2):278-287.
- Type:
Book Chapters
Status:
Published
Year Published:
2013
Citation:
Chapter 14 Worksite Management and Automation. Agricultural Automation: Fundamentals
and Practices, Agricultural Automation: Fundamentals and Practices, Editor: Francis J.Pierce, Qin Zhang, Taylor & Francis Group, ISBN-10: 1439880573, ISBN-13:9781439880579.
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Progress 10/01/11 to 09/30/12
Outputs
OUTPUTS: Previous research has found that plant canopy has significant impacts on radio propagation, hence, affects the design and development of wireless sensor network (WSN) for in-field monitoring. In 2011 and 2012, we conducted theoretical analysis and simulations on the radio propagation behavior during corn growth stages. Empirical radio propagation models were used during the analysis and the least antenna heights for transmitter and receiver were suggested. In the spring and summer of 2012, we conducted field experiment to determine short-range radio-wave propagation and path-loss characteristics in corn field. Multiple sensor nodes, each with a 2.4GHz radio, were used to form a WSN. The impact factors on radio propagation considered in the experiment included the distance between the transmitter and receiver (T-R distance), the antenna height of a transmitter, the antenna height of a receiver, the height of corn, the direction of transmission, and the corn growth stages. The data were collected during the corn growth stages of V6, V8, V10, V12, and VT. The received signal strength indication (RSSI) and path-loss rate were used to describe the quality of service (QoS) of the wireless communication. A linear path loss model was established which had two independent variables of path loss exponent and a path loss coefficient. The two variables were determined based on linear analysis of field data at various corn growth stages. The results showed that the path loss model was obtained when 60% Fresnel zone was clear. The path loss exponent of this model was larger than that of free space model. The radio signals attenuated faster even if the diffraction was ignored in a corn field. Therefore, under the situation of line-of-sight communication, the free space model was still not suitable for analyzing radio propagation behavior in a high-density corn field. In addition, the experimental result indicated the path loss was significant when diffraction played an important role in radio propagation. The transmission direction did not show a significant effect on path loss. Therefore, when a WSN is deployed in a corn field, the height of node and the distance between a transmitter and a receiver are more important than the direction. The work conducted was presented in 2012 ASABE Annual International Meeting. One graduate student passed the research proposal with the preliminary experimental data. Two papers were currently being prepared for refereed journals. PARTICIPANTS: PI/PD: Ning Wang, Associate Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Supervising the overall project and students working on the project. Haixia Li, PhD Students, Biosystems and Agricultural Engineering: Field data collection Aaron Franzen, Research Engineer and PhD Student, Biosystems and Agricultural Engineering: Field installation and data collection, publication preparation Justin O'Neal: Undergraduate students, Biosystems and Agricultural Engineering: help on field data collection Michael Chavez: Undergraduate students, Biosystems and Agricultural Engineering: help on field data collection TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Wireless sensor network (WSN) has found its applications in environmental monitoring due to its nearly unlimited installation flexibility, outstanding mobility and reduced maintenance complexity. It has particular benefits on real-time data access when combined with cellular network or Internet. The sensor nodes in the WSN can be left in a field for un-supervised intensive data collection. Successful agricultural WSN implementations have demonstrated improved understanding of field status in a real-time fashion. A big challenge on developing the in-field WSN was that the radio signals for wireless communications were strongly affected by vegetation and environment. The theoretical radio propagation models such as the free space model and the plane earth model were not effective enough to describe radio propagation at this specific carrier frequency under crop field conditions. During the design and deployment of a WSN, tedious propagation measurements are always needed to estimate the received signal strength and other communication performance. Hence, the models, which are able to determine path-loss during data transmission and radio propagation coverage, will be useful to design and deploy a reliable, high-performance WSN.
Publications
- Li, H. and N. Wang. 2012. Fixed Frequencies Radio Wave Propagation and Attenuation in Corn Fields, ASABE Paper No. 121337304. The 2012 ASABE Annual Meeting, July 29-August 1, 2012, Dallas, Texas, USA. Li, H. and N. Wang. 2012. Analysis on Radio Propagation and Attenuation in Corn Fields, Transactions of ASABE. Under preparation. Li, H. and N. Wang. 2.4 GHz Radio Propagation Characteristics at Specific Corn Growth Stages. Transactions of ASABE. Under preparation.
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Progress 10/01/10 to 09/30/11
Outputs
OUTPUTS: In 2011, our group concentrates on the development of new routing and topology algorithms for wireless sensor network (WSN) used in agricultural applications. A multi-hop WSN was established in a corn field to monitor soil moisture at multiple locations. The system consisted of multiple wireless nodes, a gateway, and a server. When they were turned on, the wireless nodes and the gateway were initialized, respectively. The wireless nodes automatically established a topology structure based on the developed algorithm and waited for command packets from the gateway. The gateway hourly sent out a command packet, received data packets from the wireless nodes, and transmitted these data to the server by a GPRS modem. Radio propagation is affected by distance and direction between two wireless nodes. Generally, the ability of communication decreases when the distance between the two wireless nodes increases. To cover a large area such as crop fields, more nodes are needed to be distributed, which leads the increase of overall cost of the system. Hence, a routing algorithm was developed to establish and implement the network considering not only the transmitting distance of wireless nodes, but also the variations of field conditions, such as soil properties. The algorithms are still under testing in lab and will be tested on crop field in next crop grown season. An experimental procedure was developed on testing radio propagation behavior in corn fields. Preliminary tests were conducted in spring 2010. The results were mainly used to optimize the experiment procedures for the testing in spring 2012. This test results will help WSN users to design the topology of WSN used in corn fields. From summer 2011, a series of experiments was designed and conducted to study the potentials of underground WSN. Soil samples at different soil moisture levels were prepared. The hardware and software of two sensor nodes was developed and tested. The results showed that soil moisture had big impact on radio propagation. The radio wave with lower frequency showed better quality of transmission. Further testing is still undergoing. The work conducted was presented in the 2011 ASABE annual meeting. One Book Chapter in "Agricultural Automation" related to the work was completed. PARTICIPANTS: PI/PD: Ning Wang, Associate Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Supervising the overall project and students working on the project. Haixia Li, PhD Students, Biosystems and Agricultural Engineering: Field data collection, Algorithm development and testing, paper writing Randy Taylor, Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Help on field experiment set up Hailin Zhang, Professor, Plant and Soil Sciences, Oklahoma State University: Help on soil sample analysis Aaron Franzen, Research Engineer and PhD Student, Biosystems and Agricultural Engineering: Field installation and data collection, publication preparation TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Wireless sensor network (WSN) is one of the most promising technologies with nearly unlimited installation flexibility, outstanding mobility and reduced maintenance complexity. It has particular benefits for field physical property monitoring since a WSN allows real-time data access when combined with cellular network or Internet. The sensor nodes in the WSN can be left in a field for un-supervised intensive data collection. Successful agricultural WSN implementations have demonstrated improved understanding of field status in a real-time fashion. However, unlike in other areas, the adoption of WSN technology in crop production is slow. One of the hurdles is the high cost of deploying a large amount of wireless sensor nodes. To efficiently and effectively design WSNs, field conditions can be used as a parameter in the network topology design. The deployment of WSN sensor nodes based on the variations of field conditions can reduce the cost and simplify the data handling and processing. Hence, the outcome of this study will have significance impact on the promotion of WSN technology in crop production applications.
Publications
- Chapter 14 Worksite Management and Automation. 2011. Agricultural Automation. Editor: Qin Zhang and Francis J. Pierce. Taylor & Francis Group LLC.
- Li, H. and N. Wang. 2011. Development of Multihop Wireless Sensor Network Measurement System in Wheat Field. ASABE Paper No. 1110829. The 2011 ASABE Annual Meeting, August 8-10, 2011, Louisville, Kentucky, USA.
- Zhang, H. N. Zhang, N. Wang, Q. Yang, J. Hu., 2011. A General Agricultural Information Management Architecture for Distributed Wireless Sensor Network. ASABE Paper No. 1110583. The 2011 ASABE Annual Meeting, August 8-10, 2011, Louisville, Kentucky, USA
- Wang, N. 2011. Practical Deployment of an In-Field Soil Property Wireless Sensor Network. The international workshop on Agriculture Water Efficient Use in Arid Regions (AWEU), Yangling, Shaanxi, China. August18 -20, 2011.
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Progress 10/01/09 to 09/30/10
Outputs
OUTPUTS: During the wheat growth season in the spring of 2010, we repeated the field experiment to determine short-range radio-wave propagation and path-loss characteristics in wheat field. Similar experiment design as that in 2009 was used including field setup, equipment installation, data collection, data analysis, and path-loss prediction model development. The impact factors considered in the experiment included the distance between the transmitter and receiver (T-R distance), the transmitter height, the receiver height, the canopy height, the carrier frequencies, and gain of transmitters. Path-loss rate and packet delivery rate were used to describe the quality of service (QoS) of the wireless communication network. Based on the data collected in 2010, the trends discovered in 2009 on pass-loss rate and package deliver rate were consistent throughout the wheat growth season. The path-loss prediction models were refined based on the new data. Due to the high cost of gateway units (Stargate, Crossbow, CA) used in 2009 and 2010, we looked for a cheaper solution. A single-board microcontroller was identified with sufficient onboard resources to handle all the tasks required for the gateway unit. The programs based on TinyOS were developed and transferred to the new gateway unit. The gateway had been installed in the field for further tests. Energy consumption has always been a limit factor for WSN applications. However, in agricultural field, many available energy resources can be used such as solar, wind, water flow, etc. Hence, we also initiated experiment on exploring energy harvesting methods. An experiment platform was established. It will be tested the whole year from January 2011. The work conducted was presented in several technical conferences. Two papers were published in scientific journals. One Book Chapter on WSN applications in agricultural applications was completed. One paper was submitted to refereed journals. PARTICIPANTS: PI/PD: Ning Wang, Associate Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Supervising the overall project and students working on the project. Randy Taylor, Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Help on field experiment set up Hailin Zhang, Professor, Plant and Soil Sciences, Oklahoma State University: Help on soil sample analysis Chad Godsey, Assistant Professor, Plant and Soil Sciences, Oklahoma State University: Help on field setup and data analysis Zhen Li, MSc Students, Biosystems and Agricultural Engineering: Field experiment design and installation, Data collection and analysis, Model development, publication preparation Aaron Franzen, Research Engineer and PhD Student, Biosystems and Agricultural Engineering: Field installation and data collection, publication preparation Haixia Li, PhD Students, Biosystems and Agricultural Engineering: Field data collection Kevin Stunkel: Undergraduate students, Biosystems and Agricultural Engineering: help on field data collection Peyman Taher, MSc Student, Computer Science: maintain a database and a webserver TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Wireless sensor network (WSN) is one of the most promising technologies with nearly unlimited installation flexibility, outstanding mobility and reduced maintenance complexity. It has particular benefits for field physical property monitoring since a WSN allows real-time data access when combined with cellular network or Internet. The sensor nodes in the WSN can be left in a field for un-supervised intensive data collection. Successful agricultural WSN implementations have demonstrated improved understanding of field status in a real-time fashion. A big challenge on developing the in-field WSN was that the radio signals for wireless communications were strongly affected by various factors, such as system configuration, vegetation, and environment. The theoretical radio propagation models such as the free space model and the plane earth model were not effective enough to describe radio propagation at this specific carrier frequency under crop field conditions. Packet reception rate (PRR) is a major measure to the quality of services (QoS) in a WSN design. It is highly related to the strength of received signals and path-loss when transmission power is known. During the design and deployment of a WSN, tedious propagation measurements are always needed to estimate the received signal strength and other communication performance. Hence, the models, which are able to determine path-loss during data transmission and radio propagation coverage, will be useful to design and deploy a reliable, high-performance WSN.
Publications
- Li, Z., N. Wang, T. Hong, A. Franzen, and J. Li. 2010. Closed-loop drip irrigation control using a hybrid wireless sensor and actuator network. SCIENCE CHINA-Information Sciences (October 1): 1-12.
- Li, Z., N. Wang, and T. Hong, 2010. Radio Path-loss Modeling for a 2.4 GHz In-field Wireless Sensor Network. Transactions of ASABE, 53(2): 615-624.
- Chapter 7 Wireless Sensor Networks in Agriculture and Food Industry. 2010. Robotics and Automation In The Food Industry. Editor: Darwin Caldwell. Woodhead Publishing Ltd.
- Wang, N., Z. Li, A. Franzen, and P. Taher. 2010. Wireless Sensor Network Technology for Rapid Evaluation of Spatial Soil Property Distribution. International Workshop on Intelligent Equipment for Precision Agriculture and Airborne Remote Sensing and Measurement. December 3-4, 2010. College Station, TX, USA.(Invited Speaker)
- Li, Z., T. Hong, N. Wang, A. Franzen. 2010. Precision Orchard based on Wireless Multi-media Sensor and Actuator Network. The 2010 International Symposium on Wireless Sensor Network in Agriculture. November 28-19, Beijing, P.R. China.
- Li, Z., N. Wang, T. Hong. 2010. Data Transmission Performance for 2.4GHz In-field Soil Property Monitoring Wireless Sensor Network. Paper No. 1008576. The 2010 ASABE Annual Meeting, June 21-24, 2010, Pittsburg, Pennsylvania.
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Progress 10/01/08 to 09/30/09
Outputs
OUTPUTS: In 2009, we focus on solving key issues on applying wireless sensor network (WSN) technology for in-field applications. A series of experiments were conducted which included: analysis of the in-field low power consumption rate, short-range radio-wave propagation and path-loss characteristics; test of the in-field sensor nodes' communication performance; and modeling of the in-field signal attenuation for received signal strength (RSS) prediction. In-field soil property monitoring wireless sensor network prototypes were developed and deployed to verify the path-loss prediction models' capability. Characteristics and requirements of in-field soil property monitoring were analyzed. Feature impact factors on in-field radio propagation were identified. Statistical models were developed to predict path-loss of wireless radio signals transmitted in wheat fields. The results from this work may provide a theoretical basis and technical support to the design and development of WSN technology for in-field environmental monitoring and variable rate operations. The work conducted was presented in several technical conferences and three manuscripts were submitted to refereed journals. An MSc thesis was also completed. PARTICIPANTS: PI/PD: Ning Wang, Assistant Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Supervising the overall project and students working on the project. Randy Taylor, Professor, Biosystems and Agricultural Engineering, Oklahoma State University: Help on field experiment set up Hailin Zhang, Professor, Plant and Soil Sciences, Oklahoma State University: Help on soil sample analysis Chad Godsey, Assistant Professor, Plant and Soil Sciences, Oklahoma State University: Help on field setup and data analysis Xianlin Li, Assistant Professor, Computer Science, Oklahoma State University: provide advices on WSN development Zhen Li, MSc Students, Biosystems and Agricultural Engineering: conduct field experiment and data analysis; prepare publications Haixia Li, PhD Students, Biosystems and Agricultural Engineering: help on field data collection Kevin Stunkel: Undergraduate students, Biosystems and Agricultural Engineering: help on field data collection Peyman Taher, MSc Student, Computer Science: maintain a database and a webserver TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Wireless sensor network (WSN) is one of the most promising technologies with nearly unlimited installation flexibility, outstanding mobility and reduced maintenance complexity. It has particular benefits for field physical property monitoring since a WSN allows real-time data access when combined with cellular network or Internet. The sensor nodes in the WSN can be left in a field for un-supervised intensive data collection. Successful agricultural WSN implementations have demonstrated improved understanding of field status in a real-time fashion. In this project, multiple wireless sensor nodes with a carrier frequency of 2.4GHz were deployed in an experimental wheat field to measure soil moisture, temperature, and electrical conductivity (EC). A big challenge on developing the in-field WSN was that the 2.4GHz radio signals for wireless communications were strongly affected by various factors, such as system configuration, vegetation, and environment. The theoretical radio propagation models such as the free space model and the plane earth model were not effective enough to describe radio propagation at this specific carrier frequency under crop field conditions. Packet reception rate (PRR) is a major measure to the quality of services (QoS) in a WSN design. It is highly related to the strength of received signals and path-loss when transmission power is known. During the design and deployment of a WSN, tedious propagation measurements are always needed to estimate the received signal strength and other communication performance. Hence, the models, which are able to determine path-loss during data transmission and radio propagation coverage, will be useful to design and deploy a reliable, high-performance WSN.
Publications
- Li, Z. 2009. Development of Wireless Sensor Network Technology for Soil Property Monitoring. MSc. Thesis. Oklahoma State University.
- Li, Z., N. Wang, A. Franzen, P. Taher, C. Godsey, H. Zhang, X. Li. Development of An In-Field Soil Property Monitoring System Using Wireless Sensor Network. Computer and Electronics in Agriculture. Elsevier. Submitted in September 2009.
- Li, Z., N. Wang, T., Hong. 2009. Development of a Radio Propagation Model for Agricultural Wireless Sensor Network. Computer and Electronics in Agriculture. Elsevier. Submitted in October 2009.
- Li, Z., N. Wang, A. Franzen, A. K. Venkateshwaran, C. Godsey, X. Li. Development of a Wireless Sensor Network for Field Soil Moisture Monitoring. CIGR E-Journal. Submitter in September 2009.
- Wang, N., Z. Li, A. Franzen, P. Taher, 2009. Development of Wireless Sensor Network for Precision Agriculture Applications. The 2009 CIGR International Symposium of the Australian Society for Engineering in Agriculture, Brisbane, Queensland, Australia, September 13-17, 2009.
- Li, Z., N. Wang, A. Franzen, P. Taher, X. Li. 2009. In-Field Soil Profile Property Monitoring System Based on a Hybrid Sensor Network. ASABE Paper No: 096191. The 2009 ASABE Annual Meeting, June 21-24, 2009, Reno, Nevada.
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