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Wireless Underground Sensor Networks: System in Support of Future Agriculture OPEN ACCESS

[+] Author and Article Information
Mehmet C. Vuran

e-mail: mcvuran@cse.unl.edu

Xin Dong

e-mail: xdong@cse.unl.edu
Cyber-Physical Networking Laboratory,
Computer Science and Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588

Kurt Preston

Office of Research,
University of Nebraska-Lincoln,
Lincoln, NE 68588
e-mail: kpreston4@unl.edu

Manuscript received March 20, 2013; final manuscript received May 15, 2013; published online July 31, 2013. Assoc. Editor: Shaurya Prakash.

J. Nanotechnol. Eng. Med 4(2), 020906 (Jul 31, 2013) (3 pages) Paper No: NANO-13-1021; doi: 10.1115/1.4024767 History: Received March 20, 2013; Revised May 15, 2013

World population growth results in a grand challenge to develop new and more sustainable agricultural methods. Wireless underground sensor networks (WUSN) are an example of how nano and microsensors may be used in the future to monitor and optimize agricultural production. This short communication examines the recent advancements toward the realization of wireless underground sensor networks and a few key challenges that can be addressed by the improvements in nanotechnology.

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The current World population of around 7 × 109 people is predicted to reach around 9 × 109 by midcentury. Thus, the ability to optimize local environments toward sustainable, productive food production becomes an issue of increased relevance. The recent improvements in micro-electro mechanical systems (MEMS) have paved the way for wireless underground communication and underground sensor networks [1]. These solutions will play an increasing role in monitoring the agricultural systems [2]. Using the data collected and transmitted from microscale underground sensors and the evolving nanoscale sensors, the goal is to improve plant health, implement increasingly efficient irrigation and pest management solutions, and ultimately improve yield with less ecosystem burden. In addition to agriculture, wireless underground sensor networks can be used in a large class of applications including underground pipeline monitoring [3], border and perimeter patrol [4], and sandstorm monitoring [5].

The typical WUSN architecture, within the context of agricultural applications (Fig. 1), integrates heterogeneous components including monitoring nodes, infrastructure nodes, central monitoring, and mobile sinks [2,6]. Monitoring nodes, mainly located underground, are equipped with on-board processor, memory, wireless transceiver, and sensors such as temperature, soil moisture, and pH. The infrastructure nodes provide connectivity between the monitoring network and the rest of the WUSN. In an agricultural network, these nodes can be located on certain parts of the field such as posts, fences, irrigation systems, or houses and include environmental sensors such as humidity, temperature, and wind for additional monitoring. Infrastructure nodes can also serve as sinks for the underground monitoring nodes. The information collected by the WUSN is sent to the monitoring central, which can reside close to the WUSN or be located in the cloud. Finally, a mobile sink could be a smartphone that is used by the users to collect information from the network and send commands. Moreover, a mobile sink can be attached to mobile elements such as unmanned vehicles to provide an autonomous operation.

The lossy soil medium in WUSNs results in unique challenges for underground wireless communication. In a typical WUSN, three different communication links exist based on the locations of the transmitter and the receiver as shown in Fig. 1. The underground-to-underground (UG2UG) link occurs when both the sender and the receiver are buried underground [7]. The communication quality in this link is tightly coupled with the properties of the soil and more specifically, with the soil moisture. While soil moisture results in large-scale variations, the UG2UG channel is significantly robust to noise and multipath effects, and hence, exhibit close-to-deterministic characteristics compared to over-the-air communication channels [7]. In addition, for deployments close to the surface, UG2UG channel contains three main paths: the direct path between the sender and the receiver, the reflected path that includes waves that reflect from the soil–air interface, and the most interesting lateral waves [8]. The lateral waves propagate almost perpendicularly from a sender toward the air, refract in the interface, propagate through the soil–air interface and penetrate back into the soil and is found to significantly improve UG2UG communication links [8-11].

In addition to UG2UG linke, underground-to-aboveground (UG2AG) and aboveground-to-underground (AG2UG) links are established when one of the communication parties is buried underground and the other is in air. UG2AG links are used to transfer monitoring data from the soil to infrastructure, while AG2UG links are essential for network management. Due to the differences in attenuation in air and soil as well as reflection and refraction at the soil–air interface, AG2UG links exhibit different characteristics than the UG2AG links [12].

Recently, an underground antenna was tested in field experiments at different depths and soil moisture levels [2]. Bandwidth analysis indicated that the return loss at the operation frequency is below 10 dB (a well-accepted design threshold) for all the burial depth and soil moisture values. Compared to over-the-air antennas, the designed antenna increases the communication distance as the design considers soil properties instead of those of air. In experiments, underground nodes were buried at 40 cm depth and an aboveground node with a directional Yagi antenna is employed on a center pivot. The communication range of the designed antenna extends to 55–80 m leading to a 250–580% increase compared to the state-of-the-art [2].

This result was achieved by recognizing that soil also alters the characteristics of a buried antenna and impacts RF wave attenuation. Due to the higher permittivity of soil, the wavelength of an electromagnetic wave changes when it propagates between air and soil. The permittivity of soil varies over time due to the changes in soil moisture. As a result, the wavelength of a given electromagnetic wave varies with time. Moreover, the reflection from the soil–air interface disturbs the current disattribution on the antenna surface. Therefore, an underground antenna should be designed with these factors being considered.

In addition to physical layer considerations, network-level challenges also emerge in WUSNs. The performance of medium access protocols in soil, especially the effectiveness of request to send and clear to send (RTS/CTS) schemes on hidden terminal problem in WUSNs become of key interest for subsurface network operation. In fact, the hidden terminal problem becomes particularly severe when multiple underground nodes communicate due to the different communication ranges of the underground-to-underground and underground-to-aboveground channels. Research reveals that without appropriate channel access control the asymmetry in channels can result in significant packet losses. On the other hand, a robust RTS/CTS scheme can increase the communication success from 32% to 76% [13]. Despite this improvement, RTS/CTS schemes alone cannot guarantee successful multi-access operation due to the high variance in channel quality with changing environmental conditions. Thus, further research in this area is needed.

In addition, water in soil increases its conductivity, and hence the attenuation of electromagnetic waves. Therefore, the attenuation in soil is a function of its moisture. When a WUSN is deployed, the quality of the communication channels varies over time due to the variation of soil moisture [12]. Therefore, a well-connected network in dry soil conditions can become severely disconnected when the soil moisture increases (e.g., it rains). Simulations show that a well-connected underground network (98% of the nodes are in the same giant component) when volumetric water content (VWC) is 10% can be disconnected (less than 5% of the nodes are in the same component) when VWC increases to 25% [14]. In other words, the underground nodes form small disconnected clusters. Therefore, in WUSNs, aboveground nodes must be deployed to improve the connectivity [15]. Since the underground-to-aboveground channel has a much longer communication distance, the aboveground nodes act as bridges to connect the underground network components together. As an example, simulations show that when nine aboveground nodes are randomly deployed in a field, for VWC = 20%, the percentage of the connected underground nodes increases from 15% to 72%. However, the network connectivity still varies over different soil moisture values, and hence strategies to improve connectivity while reducing energy consumption are needed.

Finally, there is an opportunity to make these systems increasingly robust with energy harvesting. Exchanging batteries of underground devices is highly costly and hence, remote recharging options are desired. To this end, two approaches can be employed: wireless power transfer and ambient energy harvesting. Wireless power transfer methods use electromagnetic induction, radiation, or electromagnetic resonance techniques [16-19] and have been investigated for mobile and above ground applications. Recent models for vibration energy harvesting in underground applications show that the maximum harvestable power is a function of the magnitude and frequency of vibration force, depth of the harvester, soil material, and energy harvester characteristics. Field experiments show that underground vibration energy harvesting is theoretically feasible to result in sustainable systems [20]. Practical solutions are needed to provide sustainable energy to underground systems based on existing vibration sources. Moreover, novel approaches are required to develop wireless power transfer solutions for underground environment.

Despite the challenges that remain, wireless underground communication provides promising applications in environmental and crop monitoring. Antenna, network designs, and energy provisioning are challenging but tractable problems. With the use of cost-effective and efficient MEMS, these solutions will be vital to increasing the sustainability of food production for future populations. Furthermore, evolving nanoscale sensor and transceiver solutions will enable minatuarization of these solutions along with new capabilities that surpass current solutions.

This work is supported by an NSF CAREER award (CNS-0953900), USGS (2010NE209B), and UNL Water Center. The authors would like to thank Dr. Suat Irmak for his valuable comments throughout the development of the experiments and William Rathje for his support during the experiments at Clay Center.

Akyildiz, I. F., and Stuntebeck, E. P., 2006, “Wireless Underground Sensor Networks: Research Challenges,” Ad Hoc Netw. J., 4, pp. 669–686. [CrossRef]
Dong, X., Vuran, M. C., and Irmak, S., 2012, “Autonomous Precision Agriculture Through Integration of Wireless Underground Sensor Networks With Center Pivot Systems,” AdHoc Netw. J. (accepted).
Sun, Z., Wang, P., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., and Akyildiz, I. F., 2011, “MISE-PIPE: Magnetic Induction-Based Wireless Sensor Networks for Underground Pipeline Monitoring,” Ad Hoc Netw. J., 9(3), pp. 218–227. [CrossRef]
Sun, Z., Wang, P., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., and Akyildiz, I. F., 2011, “Border-Sense: Border Patrol Through Advanced Wireless Sensor Networks,” Ad Hoc Netw. J., 9(3), pp. 468–477. [CrossRef]
Wang, P., Sun, Z., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., and Akyildiz, I. F., 2011, “On Network Connectivity of Wireless Sensor Networks for Sandstorm Monitoring,” Comput. Netw., 55(5), pp. 1150–1157. [CrossRef]
Tooker, J., Dong, X., Vuran, M. C., and Irmak, S., 2012, “Connecting Soil to the Cloud: A Wireless Underground Sensor Network Testbed,” Demo presentation in IEEE SECON’12.
Silva, A. R., and Vuran, M. C., 2009, “Empirical Evaluation of Wireless Underground-to-Underground Communication in Wireless Underground Sensor Networks,” Proceedings of IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS'09), pp. 231–244.
Dong, X., and Vuran, M. C., 2011, “A Channel Model for Wireless Underground Sensor Networks Using Lateral Waves,” Proceedings of IEEE Globecom’11.
Tiusanen, M. J., 2005. “Attenuation of a Soil Scout Radio Signal,” Biosyst. Eng., 90(2), pp. 127–133. [CrossRef]
Tiusanen, M. J., 2008, “Wireless Soil Scout Prototype Radio Signal Reception Compared to the Attenuation Model,” Precis. Agric., 10(5), pp. 372–381. [CrossRef]
Bogena, H. R., Huismana, J. A., Meierb, H., Rosenbauma, U., and Weuthena, A., 2009, “Hybrid Wireless Underground Sensor Networks: Quantification of Signal Attenuation in Soil,” Vadose Zone J., 8(3), pp. 755–761. [CrossRef]
Silva, A. R., and Vuran, M. C., 2010, “Communication With Aboveground Devices in Wireless Underground Sensor Networks: An Empirical Study,” Proceedings IEEE International Conference on Communications (ICC’10), pp. 1–6.
Dong, X., and Vuran, M. C., 2012, “Empirical Analysis of the Hidden Terminal Problem in Wireless Underground Sensor Networks,” Proceedings of International Conference on Wireless Communications in Unusual and Confined Areas (ICWCUCA’12).
Dong, X., and Vuran, M. C., 2013, “Environment Aware Connectivity in Wireless Underground Sensor Networks,” Proceedings of IEEE INFOCOM’13.
Tooker, J., and Vuran, M. C., 2012, “Mobile Data Harvesting in Wireless Underground Sensor Networks,” Proceedings of IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (IEEE SECON’12).
Castelvecchi, D., 2006, “Wireless Energy May Power Electronics: Dead Cell Phone Inspired Research Innovation,” MIT TechTalk, 51(9), p. 1. Available at http://web.mit.edu/newsoffice/2006/techtalk51-9.pdf
Karalis, A., Joannopoulos, J., and Soljacic, M., 2008, “Efficient Wireless Non-Radiative Mid-Range Energy Transfer,” Elsevier Ann. Phys., 323, pp. 34–48. [CrossRef]
Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J. D., Fisher, P., and Soljacic, M., 2007. “Wireless Power Transfer via Strongly Coupled Magnetic Resonances,” Sci. J. 6, 317(5834), pp. 83–86. [CrossRef]
Kurs, A., Moffatt, R., and Soljacic, M., 2010, “Simultaneous Mid-Range Power Transfer to Multiple Devices,” Appl. Phys. Lett., 96, p. 044102. [CrossRef]
Kahrobaee, S., and Vuran, M. C., 2013, “Vibration Energy Harvesting for Wireless Underground Sensor Networks,” Proceedings of IEEE International Conference on Communications (ICC’13).
Copyright © 2013 by ASME
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References

Akyildiz, I. F., and Stuntebeck, E. P., 2006, “Wireless Underground Sensor Networks: Research Challenges,” Ad Hoc Netw. J., 4, pp. 669–686. [CrossRef]
Dong, X., Vuran, M. C., and Irmak, S., 2012, “Autonomous Precision Agriculture Through Integration of Wireless Underground Sensor Networks With Center Pivot Systems,” AdHoc Netw. J. (accepted).
Sun, Z., Wang, P., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., and Akyildiz, I. F., 2011, “MISE-PIPE: Magnetic Induction-Based Wireless Sensor Networks for Underground Pipeline Monitoring,” Ad Hoc Netw. J., 9(3), pp. 218–227. [CrossRef]
Sun, Z., Wang, P., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., and Akyildiz, I. F., 2011, “Border-Sense: Border Patrol Through Advanced Wireless Sensor Networks,” Ad Hoc Netw. J., 9(3), pp. 468–477. [CrossRef]
Wang, P., Sun, Z., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., and Akyildiz, I. F., 2011, “On Network Connectivity of Wireless Sensor Networks for Sandstorm Monitoring,” Comput. Netw., 55(5), pp. 1150–1157. [CrossRef]
Tooker, J., Dong, X., Vuran, M. C., and Irmak, S., 2012, “Connecting Soil to the Cloud: A Wireless Underground Sensor Network Testbed,” Demo presentation in IEEE SECON’12.
Silva, A. R., and Vuran, M. C., 2009, “Empirical Evaluation of Wireless Underground-to-Underground Communication in Wireless Underground Sensor Networks,” Proceedings of IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS'09), pp. 231–244.
Dong, X., and Vuran, M. C., 2011, “A Channel Model for Wireless Underground Sensor Networks Using Lateral Waves,” Proceedings of IEEE Globecom’11.
Tiusanen, M. J., 2005. “Attenuation of a Soil Scout Radio Signal,” Biosyst. Eng., 90(2), pp. 127–133. [CrossRef]
Tiusanen, M. J., 2008, “Wireless Soil Scout Prototype Radio Signal Reception Compared to the Attenuation Model,” Precis. Agric., 10(5), pp. 372–381. [CrossRef]
Bogena, H. R., Huismana, J. A., Meierb, H., Rosenbauma, U., and Weuthena, A., 2009, “Hybrid Wireless Underground Sensor Networks: Quantification of Signal Attenuation in Soil,” Vadose Zone J., 8(3), pp. 755–761. [CrossRef]
Silva, A. R., and Vuran, M. C., 2010, “Communication With Aboveground Devices in Wireless Underground Sensor Networks: An Empirical Study,” Proceedings IEEE International Conference on Communications (ICC’10), pp. 1–6.
Dong, X., and Vuran, M. C., 2012, “Empirical Analysis of the Hidden Terminal Problem in Wireless Underground Sensor Networks,” Proceedings of International Conference on Wireless Communications in Unusual and Confined Areas (ICWCUCA’12).
Dong, X., and Vuran, M. C., 2013, “Environment Aware Connectivity in Wireless Underground Sensor Networks,” Proceedings of IEEE INFOCOM’13.
Tooker, J., and Vuran, M. C., 2012, “Mobile Data Harvesting in Wireless Underground Sensor Networks,” Proceedings of IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (IEEE SECON’12).
Castelvecchi, D., 2006, “Wireless Energy May Power Electronics: Dead Cell Phone Inspired Research Innovation,” MIT TechTalk, 51(9), p. 1. Available at http://web.mit.edu/newsoffice/2006/techtalk51-9.pdf
Karalis, A., Joannopoulos, J., and Soljacic, M., 2008, “Efficient Wireless Non-Radiative Mid-Range Energy Transfer,” Elsevier Ann. Phys., 323, pp. 34–48. [CrossRef]
Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J. D., Fisher, P., and Soljacic, M., 2007. “Wireless Power Transfer via Strongly Coupled Magnetic Resonances,” Sci. J. 6, 317(5834), pp. 83–86. [CrossRef]
Kurs, A., Moffatt, R., and Soljacic, M., 2010, “Simultaneous Mid-Range Power Transfer to Multiple Devices,” Appl. Phys. Lett., 96, p. 044102. [CrossRef]
Kahrobaee, S., and Vuran, M. C., 2013, “Vibration Energy Harvesting for Wireless Underground Sensor Networks,” Proceedings of IEEE International Conference on Communications (ICC’13).

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Grahic Jump Location
Fig. 1

Wireless underground sensor network architecture in an agricultural context

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