Autonomous Precision Agriculture Through Integration ofWireless Underground Sensor Networks with Center Pivot

Irrigation SystemsI

Xin Donga, Mehmet C. Vurana, Suat Irmakb

aCyber-Physical Networking Lab, Department of Computer Science and Engineering,University of Nebraska-Lincoln, Lincoln, NE 68588

bBiological Systems Engineering Department, University of Nebraska-Lincoln, Lincoln,NE 68588

Abstract

Precision agriculture (PA) refers to a series of practices and tools neces-sary to correctly evaluate farming needs. The accuracy and effectiveness ofPA solutions are highly dependent on accurate and timely analysis of thesoil conditions. In this paper, a proof-of-concept towards an autonomousprecision irrigation system is provided through the integration of a cen-ter pivot (CP) irrigation system with wireless underground sensor networks(WUSNs). This Wireless Underground Sensor-Aided Center Pivot (WUSA-CP) system will provide autonomous irrigation management capabilities bymonitoring the soil conditions in real time using wireless underground sen-sors. To this end, field experiments with a hydraulic drive and continuous-move center pivot irrigation system are conducted. The results are used toevaluate empirical channel models for soil-air communications. The exper-iment results show that the concept of WUSA-CP is feasible. Through thedesign of an underground antenna, communication ranges can be improvedby up to 400% compared to conventional antenna designs. The results alsohighlight that the wireless communication channel between soil and air issignificantly affected by many spatio-temporal aspects, such as the locationand burial depth of the sensors, soil texture and physical properties, soilmoisture, and the vegetation canopy height. To the best of our knowledge,

IThis work is supported in part by the National Science Foundation CAREER AwardCNS-0953900, U.S. Geological Survey Award 2010NE209B, and UNL Water Center. Theauthors would like to thank William Rathje for his support during the experiments atClay Center, Nebraska.

Email addresses: xdong@cse.unl.edu (Xin Dong), mcvuran@cse.unl.edu (MehmetC. Vuran), sirmak2@unl.edu (Suat Irmak)

Preprint submitted to Elsevier April 4, 2012

this is the first work on the development of an autonomous precision irriga-tion system with WUSNs.

Keywords: Cyber-physical systems, Underground electromagneticpropagation, Wireless Underground Sensor Networks, Precision Agriculture

1. Introduction

With the growth of the world population, the corresponding increase inthe demand for food encourages production agriculture into a new generationof practice called precision agriculture (PA). PA techniques focus on theexistence of in-field variability of natural components, including chemicalleaching, runoff, drainage, water content, nutrients, and soil components [6,7]. The goal is to utilize new technologies, such as GPS, satellites, aerialremote sensing and sensors to assess the variations in a field more accurately.Accordingly, farming practices, including sowing, irrigation and fertilizermanagement, and pest control, can be scheduled autonomously accordingto the assessment of the field.

The accuracy and effectiveness of this autonomous PA system dependon the timely evaluation of the field. Irrigation scheduling requires theknowledge of “when” and “how much” water to apply to optimize cropproduction. Effective irrigation management for irrigation systems requiresthat soil water status be accurately monitored over time in representativelocations in the field. For optimum yield, soil water in the crop root-zonemust be maintained between desirable upper and lower limits of plant avail-able water. Proper irrigation management will help prevent economic losses(yield quantity and quality) caused by over or underirrigation; movementof nutrients, pesticides and other chemicals into the groundwater and otherwater bodies; and wasting water resources and energy consumption. De-termination of soil water status for irrigation management using hand-feelmethod is practiced in the absence of accurate and low cost soil moisturesensors. The hand-feel method does not provide quantitative soil water sta-tus; rather it provides a qualitative indication of soil water status and issubject to the person’s ability to feel the soil. To improve irrigation man-agement, quantitative knowledge of soil water status deep in the soil profileis necessary, but not possible with the hand-feel method. Any error in thehand-feel method will cause significant errors in determination of irrigationwater requirement [8].

The objective of irrigation management is to establish a proper timingand amount of irrigation for greatest effectiveness. Proper irrigation man-

2

agement will minimize yield loss due to crop water stress, maximize yieldresponse to other management practices, and optimize yield per unit of wa-ter applied. These factors contribute to farm profitability. Irrigation man-agement that results in either excessive or inadequate water application cansignificantly reduce the potential for profitability. Proper irrigation manage-ment will help to reduce the potential for runoff and reduce soil erosion andpesticide movement into surface and ground water. Thus, irrigation systemscoupled with proper in-situ soil moisture monitoring for irrigation schedulinghas significant advantage in terms of water saving as well as improving cropyields and yield quality over unmanaged systems where irrigation decisionsare not based on quantitative soil moisture indicators [9].

Unfortunately, traditional soil measurement techniques may not be fea-sible in providing real-time data, and hence may not satisfy the require-ments of PA. Most of the traditional soil moisture sensors need to be in-stalled early in the growing season and need to be removed before harvest,adding additional time and labor requirements. To this end, Wireless Un-derground Sensor Networks (WUSNs) have recently been investigated forunattended soil monitoring [1, 4, 16, 17, 23]. These networks consist ofwirelessly-connected underground sensor nodes that communicate throughsoil. For the autonomous PA, WUSNs provide an adaptable and efficientcyber-physical system (CPS) that provides timely information of the soilcondition with high granularity [15]. Compared with satellites and aerialremote sensing, WUSNs can provide more direct and precise informationabout some of the soil conditions. In addition, the operation cost of WUSNsis far less than these other techniques. Unlike wired sensor networks, whichneed to be deployed and removed frequently during the process of planting,WUSNs are deployed in the ground at a safe depth and do not interfere withagriculture machinery operations, such as tillage practices.

In this paper, we present a proof-of-concept for a cyber-physical sys-tem application called Wireless Underground Sensor-Aided Center Pivot(WUSA-CP) irrigation. In this application, an irrigation solution called acenter pivot system [13] is used as a physical mobile structure to commu-nicate with buried underground sensors to receive soil moisture conditiondata. To this end, we investigate the unique challenges towards such anautonomous irrigation system. One major challenge is the unique channelproperties between underground and aboveground wireless entities. Unlikeconventional over the air communication, the properties of soil, especially therelative permittivity, significantly impact behavior of electromagnetic wavepropagation. We first analyze the channel models of this hybrid WUSNsand then two antenna designs are evaluated in a corn field to verify the

3

Figure 1: Basic components of a center pivot (CP) system.

channel model and the concept of WUSA-CP. Meanwhile, the asymmetry ofthe communication over distance and the burstiness of the packet error rateare analyzed. The results are promising for implementing cyber-physicalsystems for the purpose of autonomous precision agriculture.

The rest of this paper is organized as follows: In Section 2, center pivotsystem and irrigation methods are explained, in addition to the character-istics of WUSNs. Related studies are also discussed in Section 2. In Sec-tion 3, the channel models for the underground-to-aboveground link and theaboveground-to-underground link are described. In Section 4, the hardware,the program and the methodology used in the experiments are described.The setup of the experiments for the integration of CP system with WUSNis described in Section 5 and the empirical results are discussed in Section 6with a numerical analysis of the channel model and communication quality.Finally, the paper is concluded in Section 7.

2. Background and Related Work

In this section, we first provide a background on center pivot irriga-tion systems in Section 2.1 and then, WUSNs are described in Section 2.2.Finally, in Section 2.3, related work is discussed.

2.1. Center-Pivot Systems

In agriculture, efforts to reduce operating costs while maintaining andimproving crop yields have been consistently made. One of the efforts tomore uniformly apply irrigation water is sprinkler irrigation with a centerpivot (CP) system [6, 13], which improves the efficiency of water use as

4

well as of energy use. In Fig. 1, a CP is shown, which consists of severalsegment pipes (spans) supported by trusses and mounted on wheels withsprinklers (nozzles) placed along its length [3, 6, 13]. One end of the pipelineis connected to a pivot element at the center of the irrigating area that iscalled pivot point. The machine moves in a circular pattern. The water isfed through the pivot and sprinkled out as the machine moves.

CP irrigation systems have the advantage over other traditional or con-ventional gravity (furrow) or surface irrigation methods that the potentialfor deep percolation and surface run-off losses can be minimized or elimi-nated in a given field when the CP irrigation system is managed well. TheCP systems requires much less (e.g., up to 40%) water application than thesurface irrigation methods. Minimizing the water losses can increase theavailability of water for crop transpiration and enhance the crop water pro-ductivity. Another significant advantage of the CP irrigation system is thatthe nutrients can be applied directly onto the crop canopy through the sys-tem (chemigation) [6]. Chemigating certain fertilizers, nutrients, herbicide,insecticide, and pesticide to the canopy directly can enhance the absorp-tion by the crop leaves as compared with the ground (granular) applicationmethod. This would enhance the efficacy of the chemicals/nutrients, result-ing in improved crop productivity.

Due to the cost of CP, it is usually employed to irrigate large areas: from3.5 to 65 ha [6, 13]. A CP for an area of 22 ha (220, 000m2) at the South Cen-tral Agricultural Laboratory (SCAL) at the University of Nebraska-Lincolnis used as a testbed for our experiments.

There are two ways to control the amount of water applied to a fieldfrom a CP. One is to control the travelling speed of a CP. For the same flowrate, a higher travelling speed of the CP leads to a smaller amount of waterapplied to the field. The second method is to use electronically controllednozzles to adjust the flow rate of the sprinklers. Because of the simplicity,high accuracy, and low cost (no need to change nozzles), the first optionis often used. To further improve the efficiency of a CP, it is desirable toinput real-time soil evaluation data into the system. Thus, water use can beadjusted according to the soil condition at a specific location. This is exactlywhat a cyber-physical system can provide to realize precision agriculture.

Wireless underground sensors can also be utilized for failure detection.In current CP solutions, it takes a considerably long time to detect a mal-function in the machine that causes some nozzles to stop irrigating. Mea-surement of flow rate at the nozzle discharge point could also potentiallyprovide information about whether a given nozzle is applying the designedflow rate of water. However, this would require installing very expensive and

5

o

Figure 2: An example of a precision agriculture cyber-physical system, WUSA-CP, basedon Wireless Underground Sensor Networks (WUSNs). A WUSN can employ 3 kinds ofcommunication: Underground-to-underground (UG2UG), Underground-to-aboveground(UG2AG), and Aboveground-to-underground (AG2UG).

very sophisticated and extremely accurate and sensitive miniature flow me-ters at each nozzle discharge point, which would not be feasible in practicalapplications with the current available technologies. To this end, under-ground sensors can provide warnings and alerts if adequate soil moistureincrease is not detected when a CP passes over a certain location within thefield. These alerts can be provided to farmers immediately. Therefore, thedamage caused by insufficient irrigation can be mitigated or avoided.

To implement such WUSA-CP as an efficient PA solution, a communi-cation infrastructure for the CP and the underground sensors needs to beimplemented to provide real time soil condition information. To this end, inthis study, wireless underground sensors are employed to provide the com-munication infrastructure. In the next section, this wireless undergroundcommunication technology is presented and its challenges are discussed.

2.2. Wireless Underground Sensor Networks

A WUSN is mainly formed by underground sensor nodes. However, thenetwork still requires aboveground nodes for additional functionalities suchas data retrieval, management, and relaying. Therefore, considering thelocations of the sender and the receiver, three different communication linksexist in WUSNs, as shown in Fig. 2:

• Underground-to-underground (UG2UG) Link: Both the sender and thereceiver are buried underground and communicate through soil [16].UG2UG links can be employed for multi-hop information delivery.

• Underground-to-aboveground (UG2AG) Link: The sender is buried inthe soil profile and the receiver is located above the ground [17]. Mon-

6

itoring data are transferred to aboveground relays or sinks throughthese links.

• Aboveground-to-underground (AG2UG) Link: An aboveground sendernode sends messages to underground nodes [17]. This link is used formanagement information delivery to the underground sensors.

For the realization of WUSA-CP, only UG2AG and AG2UG links areconsidered in this work. Although sensors may be buried in different regionsof the soil, typical WUSN applications will require that the buried sensorsbe deployed at two specific regions: the topsoil and the subsoil regions.The topsoil region refers to the top 30 cm of soil, or the root growth layer,whichever is shallower and the subsoil region refers to the region below thetopsoil, i.e., usually the 30–100 cm region [16]. Accordingly, both cases ofthe deployment of underground nodes are illustrated in Fig. 2.

Whenever possible, a shallower deployment (topsoil) is preferable dueto the shorter length of the soil path and, thus, smaller signal attenuation.However, for the PA scenario, plowing and similar mechanical activitiesoccur exactly at the topsoil region and higher burial depths in the rootrange of crops are required. In other words, PA applications are mainlyrelated to subsoil WUSNs. Thus, in our experiments with corn crops, the35 cm burial depth is defined as the best value to satisfy the applicationrequirements.

2.3. Related Work

Wireless underground sensor networks are an important extension of thetraditional WSNs. Their concept and challenges have been introduced in [1].In [5, 27], we develop a theoretical channel model for the UG2UG link at the300–900MHz frequency range and empirical evaluations of UG2UG commu-nications are reported in [16], where Mica2 motes [28] are used. The resultsin [16] confirm our previous theoretical findings. In [21], the connectivityissues between underground and aboveground nodes are investigated.

Few WUSN experiments have been performed to date. In [18], the chal-lenges in realizing WUSN experiments are discussed. Some of the aspects tobe considered when developing a WUSN testbed, such as node qualificationtest, standard RF measurement and the interference among undergroundsensors, are provided. In [4], a WUSN based on customized sensor nodes(SoilNet) that operate at 2.4GHz is developed for real-time soil water con-tent monitoring. A 5−9 cm burial depth is considered and a theoreticalmodel for the UG2AG link for this burial depth is developed. However,

7

the shallow burial depth is not suitable for the applications in agriculture,considering the plowing practice in the field. In [24], an ultra widebandelliptical antenna [14] is proposed for underground communication, and theadvantages of this scheme are highlighted. The same type of antenna is alsoused in our experiments. It is revealed that this elliptical antenna is notperfectly suitable for our application and a new design, a circular planarantenna achieves better performance. A UG2AG theoretical model is pro-posed in [25] and experimental results are provided. Communication rangesof 30m and 150m are reported for burial depths of 40 cm and 25 cm, respec-tively. The long communication range is a benefit of using a very high gain(+10 dB) antenna above the soil. However, only UG2AG communicationlinks are considered in [25], without the investigation of the AG2UG links.In [17], empirical analysis of the UG2AG and AG2UG links is provided andthe effects of antenna design, burial depth, and soil moisture are discussed.

While the above results illustrate the feasibility of WUSN applications,a solution for autonomous irrigation applications has yet to be developed.In [19], our first step in integrating a WUSN with a center pivot irrigationsystem is presented. In this paper, we extend this work by providing a proof-of-concept for such a solution with subsoil deployment, i.e., burial depthdeeper than 30 cm. The characteristics of both UG2AG and AG2UG linksare analyzed. Furthermore, a circular antenna scheme is developed to extendthe communication ranges to practical values of 50m for the realization ofWUSA-CP.

3. Channel Model for Soil-Air Communication

For the realization of WUSA-CP, reliable links between undergroundsensors and the CP should be provided. To this end, in this section, wepresent an empirical channel model for AG2UG links and UG2AG links.The model is then evaluated by field experiments in Section 6.

In [5], a channel model for the underground-to-underground (UG2UG)communication is developed. However, for soil-air communication, thismodel cannot be directly used due to the impacts of soil-air interface andpropagation through air. For both AG2UG and UG2AG links, the chan-nel consists of two parts, the underground portion (soil medium) and theaboveground portion (air medium). The model is shown in Fig. 3. Giventhe horizontal distance, dh, the height of the AG antenna, ha, and the burialdepth of the UG nodes, hu; the length of both portions can be calculated.Accordingly, the received signal strength at the receiver, Pr, is given as

Pr = Pt +Gt +Gr − (Lug(dug) + Lag(dag) + L(R,→)) , (1)

8

Figure 3: The channel model for the system.

where Pt is the transmit power, Gt and Gr are the antenna gains at thesender and the receiver, respectively. Lug(dug) and Lag(dag) are the loss atthe underground and the aboveground portions, respectively, while L(R,→)

is the refraction loss based on the propagation direction, →, i.e., ag2ug orug2ag.

The underground and aboveground losses in (1) are given as [21, 27]:

Lug(dug) = 6.4 + 20 log dug + 20 log β + 8.69αdug , (2)

Lag(dag) = −147.6 + 10η log dag + 20 log f , (3)

respectively, where η is the attenuation coefficient in air, f is the operationfrequency, β is the phase shifting constant, and α is the attenuation constant.Both losses depend on the propagation paths, dug and dag logarithmically.The attenuation coefficient in air, η, is higher than 2 due to the impacts ofground reflection. Meanwhile, in the underground path, the impacts of soilproperties on attenuation are captured by the last two terms in (2), whereα and β are given as

α =2πc

λ0

√√√√√µrµ0ε′ε02

√1 +

(ε′′

ε′

)2

− 1

, (4)

β =2πc

λ0

√√√√√µrµ0ε′ε02

√1 +

(ε′′

ε′

)2

+ 1

, (5)

9

in which ε′ and ε′′ are the real and imaginary parts of the effective soilpermittivity.

The higher permittivity of soil compared to that of air results in reflec-tion and refraction of signals that are incident to the soil-air interface. Morespecifically, signals can penetrate through soil-air interface only if the inci-dent angle is small. For UG2AG propagation, only the waves with smallincident angle (θt in Fig. 3) will transmit to air. In other words, for theUG2AG channel, the waves propagate vertically in the soil and resemble anew source at the air-soil interface. For the AG2UG channel, the refractedangle is near to zero and the propagation in soil is also vertical. For bothchannels, the underground portion of communication distance can be ap-proximated as dug ' hu, where hu is the burial depth and the aboveground

portion is dag =√

h2a + d2h, where ha is the height of the AG node and dh is

the horizontal distance between nodes.The refraction loss, L(R,→), in (1) for the AG2UG link can be found as

[10]:

LR,ag2ug = 20 logn cos θi + cos θt

4 cos θi, (6)

where θi is the incident angle, θt is the refracted angle and n is the refractiveindex of soil, which is given by

n =

√√ε′2 + ε′′2 + ε′

2. (7)

For the AG2UG link, we consider the maximum power path where the inci-dent angle, θi → 0. Hence, LR,ag2ug can be approximated as

L(R,ag2ug) ' 20 logn+ 1

4. (8)

For the UG2AG link, the signal propagates perpendicularly from a higherdensity medium to a lower density one. Hence we consider all energy isrefracted and L(R,ug2ag) = 0.

By using (2)–(8) in (1), the received signal strength can be found. Byrearranging the terms in (1), it can be represented as:

Pr = −10η log dag − κ− c , (9)

where

κ = 8.69αdug + 20 log β + L(R,→) , (10)

c = −Pt −Gt −Gr + 6.4 + 20 log dug − 147.6 + 20 log f . (11)

10

Table 1: Soil parameters used in the experiments.

Depth Texture Sand Silt Clay

0-20cm Silt Loam 17 55 2820-60cm Silt Clay Loam 16 46 38

Particle density Bulk density

2.66g/cm3 1.3g/cm3

UG Node 1

UG Node 2

UG Node 3

UG Node 4

UG Node 5

UG Node 6

UG Node 7

UG Node 8

Figure 4: Testbed for the experiments: An aboveground (AG) node is installed on thecenter pivot and 8 underground (UG) nodes are buried along its path.

Note that in (9), κ depends on the soil properties through α (4), β (5),and n (7). On the other hand, the term c is a function of the deployment andradio parameters such as burial depth, transmit power, antenna, and oper-ation frequency. The term c can be calculated pre-operation but due to theuncertainties in soil properties at a particular location and in attenuation inair, the parameters η and κ in (9) should be empirically determined. In Sec-tion 6, these parameters will be empirically determined and the experimentresults will be compared with the model in (9).

4. System Architecture

To provide a proof-of-concept for an autonomous irrigation system andexplore the challenges associated within, experiments are carried out with433MHz Mica2 [28] sensor nodes in the South Central Agricultural Lab-oratory (SCAL), Clay Center, Nebraska. The analysis of the soil texture,particle density, and bulk density of the site where the center pivot is located,is shown in Table 1 according to laboratory analysis [29].

The experiment setup is illustrated in Fig. 4, where an abovegroundnode (AG node) is installed on the arm of a center pivot irrigation system,which is located in a corn field. The height of the AG node is 2.5m. Eightunderground nodes are buried in the corn field in a circle at a burial depthof 35 cm. This depth is specified to be safe from agricultural machinery

11

operations such as tilling. The center pivot is operated in both clock-wiseand counter clock-wise directions and the communication between the AGand the UG nodes is established when the CP is within the communicationrange.

4.1. Hardware Architecture

In addition to the attenuation caused by soil, electromagentic wavespropagating through soil also results in a reduction in the wavelength [17,24]. More specifically, the wavelength, λ, in the soil is related to the phaseshifting constant, β in (5) as λ = 2π/β. The dependency of wavelength onsoil properties calls for antenna designs that are tailored to the changes inλ.

The effective soil permittivity, ε, which defines the phase shifing, β, andattenuation, α, constants, is strongly impacted by factors such as soil mois-ture, soil type, salinity, and soil structure. This property is modeled bythe Peplinski semi-empirical dielectric mixing model for the 0.3−1.3GHzband [12]. The Mica2 nodes used in the experiments operate at 433MHz.Based on this frequency and the minimum and maximum measured VWCvalues observed in the experiment site, the underground antenna needs towork at a wavelength between 30 cm and 69 cm, corresponding to a frequencyrange of 1−1.8GHz in free space. In other words, to communicate with aradio that operates in the 433MHz in air, an underground node should havean antenna that is matched to the 1−1.8GHz range. The dynamic range,within which an underground antenna needs to operate, calls for widebandantenna designs for the underground nodes. Since the large structure of theCP allows, high-gain antennas are used for the aboveground node.

In the experiments, two antenna schemes are used: the first schemeincludes a Full-Wave (FW) dipole antenna for the AG node and a SingleEnded Elliptical Antenna (SEA) [14] for the UG nodes [17]. The dipoleantenna has a gain of 3 dB. The second scheme includes a Yagi antenna forthe AG node and a circular planar antenna for the UG nodes. The Yagiantenna has a higher directivity with the maximum gain of 10 dB. Both theSEA antenna and the circular planar antenna are customized according tothe operation range in this application.

4.2. Software Architecture

For the experiments, a TinyOS application is developed to carry outseveral experiments without the need to reprogram the sensor nodes. Atransmit power level of 10 dBm is used for all experiments. In each transac-tion, 100 packets are sent from the AG node to the UG node and from the

12

AG

Node

UG

Node

Hello WaitSend

HELLO

Receive

HELLOIdle

Reply

HELLO

Send

AG2UG data

Receive

UG2AG data

Receive

UG2AG data

Interval

Wait

Write to

Memory

Write to

Memory

Send

UG2AG data

Interval

Wait

Batch End

Figure 5: The program structure for the experiments.

UG node to the AG node, independently. The size of the test packet is 37bytes and the interval between two packets is 100ms.

The program structure is shown in Fig. 5. When a UG node receivesa test packet from the AG node, it extracts the timestamp of the packet,which is stored in the flash memory along with the AG node ID and thecalculated received signal strength (RSS). On the other hand, when an AGnode receives a test packet from the UG node, it reads its own clock to getthe timestamp. The timestamp, UG node ID, and the RSS are also storedin the flash memory. Thus, only the local time of the AG node is used inthe experiments so that the nodes do not need to be synchronized.

After an experiment, the experiment data are read out of the flash mem-ory of each node. The timestamp of the packet is used to calculate the loca-tion of the AG node during the travel of the CP, given its speed. Henceforth,the relative distance of the communication between the AG node and theUG node is obtained. Since the speed of the CP is very slow (0.704m/min),a transaction of the 100 packets is considered as sent from the same location.

5. Experimental Setup

To prevent the effects of significant differences related to the transceiverand antenna of each individual Mica2 node, qualification tests have beenperformed before each experiment [18]. Accordingly, through-the-air tests,which consists of 200 packets of 30 bytes, are performed to (1) determinecompliant nodes and (2) confirm that the battery level of a node is above a

13

safe limit. A node is labeled compliant with a given set of nodes if (1) itspacket error rate (PER) varies within 10% of the average PER calculatedfor the set of nodes and (2) its RSS average varies, at maximum, ±1 dB fromthe average RSS for the set of nodes. The safe limit for the battery levelhas been determined as 2.5V.

Five different experiments were realized with different conditions of soilmoisture and vegetation canopy, as listed below.

• Static-Dry : An UG node and an AG node were located at the cornfield without CP. The crops had been harvested, hence, the effects ofthe vegetation canopy can be neglected. The volumetric water content(VWC) [6] was found to be 16.6%.

• Static-Wet : In this case, field was wet and no vegetation canopy waspresent. The volumetric water content (VWC) was found to be 22.7%.

• CP-Crop-SEA-Vert : This experiment was conducted with CP andat a time when the corn crops reached their maximum height, 2.85m.Therefore, the wireless communication was subject to the effects of thecanopy. The SEA and FW antennas were adopted, and the SEA an-tenna was vertically placed. The volumetric water content was 22.7%.

• CP-Crop-SEA-Hori : This experiment was also conducted underthe influence of the corn crops. However, the SEA antenna on the UGnode was placed horizontally. The volumetric water content was 32%.

• CP-Circular-Yagi : This experiment was conducted after the har-vest. The second antenna scheme with the circular planar antennaand the Yagi antenna was employed. The circular planar antennas arehorizontally placed and the volumetric water content was 32%.

The Static-Dry and Static-Wet were exploited to analyze the impact ofthe soil moisture. The other experiments were conducted with the centerpivot setup as shown in Fig. 4. The travel speed of center pivot was setto its maximum (43o per hour) to analyze the worst-case scenario. At thecircumference where the UG nodes were buried, this speed corresponds to0.704m/min.

6. Experiment Results

Apart from providing a proof-of-concept for autonomous agricultureapplications, the experiments reveal important insights into the effects of

14

0 100 200 300 400 500

No

Yes

Time (min)

Com

mun

icat

ion

with

a U

G n

ode

1 8 7 6 5 4 3 2 1

Figure 6: Timeline for a complete travel of the center pivot for Experiment CP-Crop-SEA-Hori (CW direction).

−10 −5 0 5 10

Node 1

Node 2

Node 3

Node 4

Node 5

Node 6

Node 7

Node 8

Distance to the UG node (m)

770 s

1025 s

10 s

1122 s

259 s

321 s

1338 s

1115 s

(a) CCW direction

−10 −5 0 5 10

Node 1

Node 2

Node 3

Node 4

Node 5

Node 6

Node 7

Node 8

Distance to the underground node (m)

1109 s

1465 s

10 s

1646 s

413 s

80 s

1748 s

1207 s

(b) CW direction

Figure 7: Communication between the AG node, installed on the center pivot, and theUG nodes (CP-Crop-SEA-Hori) (Range in terms of horizontal distance).

the inter-node distance on the UG2AG and AG2UG channel performances.Next, we discuss the experiment results and evaluate the channel model inSection 3 using the empirical data. In addition, the asymmetry in the chan-nel, burstiness of packet errors, and the effects of the vegetation canopy andsoil moisture on communication are discussed.

6.1. Communication Range

The communication window, which is the time that a UG node and theAG node communicate with each other is first analyzed. We present theresults of the CP-Crop-SEA-Hori experiment in Fig. 6. Other experimentshave similar results. Since the speed of the center pivot system is 43o perhour, the total travelling time of one circle of the system is 8.37 hours. Asillustrated in Fig. 6, the total communication time of all the nodes is 1.33

15

hours, or 16% of the total travelling time. Also visible in the figure is thevariation of the communication window among different nodes. The longestcommunication time is about 29 minutes whereas the shortest time is only10 seconds.

The communication window of each node is shown in detail in Fig. 7,where the communication range is shown for each UG node in terms ofhorizontal distance and the time covered by the AG node while in motion.A negative distance represents the cases where AG node is approaching theUG node, whereas a positive value represents the cases where the AG nodeis moving away from the UG node.

As shown in Fig. 7, the variation among the nodes is significant. Thebest case is Node 2 in the CW direction experiment, where the communi-cation starts at 7.8m before the AG node is above the UG node and endsat 12.8m after the AG node passes the UG node. Thus, the total distanceis 20.6m. The worst case is node 6 in both experiments, where the to-tal distance is 0.11m. Thus, the communication window is only 0.5% ofthe best case. In the CCW direction experiment (Fig. 7(a)), the averagecommunication distance for all the nodes is 8.75m, with standard devia-tion δCCW = 5.73. Meanwhile, the average communication distance in theCW direction experiment (Fig. 7(a)) is 11.27m, and the standard deviationδCW = 8.19.

The relatively similar communication distances in both the CCW caseand the CW case reveal that the communication quality is more related tothe conditions at the specific location than the movement of the CP. The fac-tors contributing to this variation include the irregularity of the soil surface.Ideally, the interface of two media (soil and air) is flat. However, because ofthe plowing and the roots of crops, the soil surface is irregular, which affectsthe dispersion of the electromagnetic waves. In a real application, even witha careful installation of the nodes, a change on the soil surface above an UGnode can still occur as a result of the agricultural machinery activities.

In the experiments, the UG nodes are buried so that the AG node isdirectly above a UG node when it is at the location of that UG node. Thisdeployment results in some of the nodes (e.g. node 2) buried away from thecrop canopy while other nodes are buried among the canopy (e.g. node 7).This irregularity is also visible in the results among different nodes sincethe crops have a negative impact on the propagation of the electromagneticwave. This phenomenon is known as canopy effect and will be analyzedempirically in Section 6.5.

The received signal strength (RSS) over the distance between the UGnode and the AG node is shown in Fig. 8 and Fig. 9. The results of the

16

−15 −10 −5 0 5 10 15−100

−90

−80

−70

−60

−50

Distance to the node (m)

RS

S (

dBm

)

Node 2Node 5Node 7

(a)

−15 −10 −5 0 5 10 15−100

−90

−80

−70

−60

−50

Distance to the node (m)

RS

S (

dBm

)

Node 2Node 5Node 7

(b)

−15 −10 −5 0 5 10 15−100

−90

−80

−70

−60

−50

Distance to the node (m)

RS

S (

dBm

)

Node 2Node 5Node 7

(c)

−15 −10 −5 0 5 10 15−100

−90

−80

−70

−60

−50

Distance to the node (m)

RS

S (

dBm

)

Node 2Node 5Node 7

(d)

Figure 8: Effects of the horizontal inter-node distance on RSS (SEA+FW antennas): (a)AG2UG link (CW direction), (b) UG2AG link (CW direction), (c) AG2UG link (CCWdirection), (d) UG2AG link (CCW direction).

CP-Crop-SEA-Hori experiment are depicted in Fig. 8(a) and Fig. 8(b) forthe AG2UG link and the UG2AG link, respectively for the CW directionand in Fig. 8(c) and Fig. 8(d) for the CCW dirction1. Similarly, the resultsof the CP-Circular-Yagi experiment are shown in Fig. 9(a) and Fig. 9(b).As discussed before, the results show variation among nodes.

The circular planar and Yagi antenna pair extends the communicationrange by 364–400% compared to the SEA and FW antenna pair. The max-imum achievable communication range with the circular planar and Yagi

1The results of the CP-Crop-SEA-Vert experiment, which are presented in [19], are notrepeated here since CP-Crop-SEA-Hori results outperform because of the better antennaplacement.

17

−60 −40 −20 0 20−120

−110

−100

−90

−80

−70

−60

−50

Distance to the node (m)

RS

S (

dBm

)

Node 1Node 3

(a)

−60 −40 −20 0 20−120

−110

−100

−90

−80

−70

−60

−50

Distance to the node (m)

RS

S (

dBm

)

Node 1Node 3

(b)

Figure 9: Effects of the horizontal inter-node distance on RSS (Circular+Yagi antennas):(a) AG2UG link (b) UG2AG link.

antenna pair is 65m compared to the distance of 14m with the SEA andFW antenna pair. For the worst case, it is 40m versus 8m. This is due tothe fact that (1) the planar antenna has a lower return loss (−10 dB) com-pared with the SEA antenna (−3 dB) when buried underground and (2) theYagi antenna has a higher gain. It is also shown that the communicationdistance is not symmetric for the circular planar and Yagi antenna pair.This is mainly caused by the directivity of the Yagi antenna. For the SEAand FW pair, the asymmetry is analyzed in Section 6.3. Next, we evaluatethe channel model presented in Section 3 using the experiment results.

6.2. Numerical Analysis of the Channel Model

In this section, the model in (9) is evaluated using the experiment results.The empirical estimates of the attenuation in air, η∗, and the soil-dependentcomponent, κ∗, in (9) are found using minimum mean square (MMSE) esti-mation2. The soil dependent component is then compared to the model in(10). The constant c in (11) is found to be 13.57 dB for the CP-Crop-SEA-Hori experiment and 3.57 dB for the CP-Circular-Yagi experiment since theYagi antenna gain is Gr = 10dB.

The results are shown in Tables 2 and 3 for both the UG2AG andAG2UG links. The comparison with the model is shown in Table 3. Thesoil-dependent component, κ, is also found based on the Peplinski model

2In the rest of the paper, the empirical estimates are indicated by a superscript ∗ todistinguish from the model parameters

18

Table 2: Channel model parameters

node η∗ κ∗ MSE

CP-Crop-SEA-Hori (UG2AG)

Approaching2 4.67 34.27 2.325 4.21 29.52 5.947 2.68 48.11 1.33

Departing2 4.05 29.7 1.525 5.25 34.19 3.707 3.30 47.42 1.51

CP-Crop-SEA-Hori (AG2UG)

Approaching2 5.11 23.87 1.545 4.48 24.43 5.947 3.15 44.38 3.10

Departing2 3.53 31.52 2.035 5.58 27.35 3.577 3.84 42.43 4.47

CP-Circular-Yagi (UG2AG)1 5.10 72.06 4.693 4.91 60.65 3.22

CP-Circular-Yagi (AG2UG)1 5.62 73.02 4.603 5.34 63.91 3.34

Table 3: Comparison of the average result to the model calculation

η∗ κ∗ MSE κ

CP-Crop-SEA-Hori (UG2AG) 3.16 40.78 5.41 48.59

CP-Crop-SEA-Hori (AG2UG) 3.29 38.46 5.84 55.50

CP-Circular (UG2AG) 5.01 66.36 7.24 48.59

CP-Circular (AG2UG) 5.48 68.47 6.33 55.50

[12] and the soil parameters in Table 1.It can be observed from the MSE columns in the Table 2 that the at-

tenuation model in (1) captures the characteristics of both links with lowerror (max MSE is 5.94). However, there is considerable variation in modelparameters between different locations. The variation of the attenuationcoefficient, η, is mainly due to the plants in the field, which cause reflec-tion and attenuation in air. The variation of κ is caused by different valuesof α, β and LR,→ in different locations. These values are determined by

19

the effective soil permittivity, ε, which depends on the soil characteristicssuch as volumetric water content (VWC), bulk density, and soil composi-tion. The variations in the values illustrate that even in the same field, thecharacteristics of soil vary from location to location.

It can also be observed in Table 3 that the model prediction of κ agreeswell with the empirical results from the CP-Crop-SEA-Hori experiments.However, the accuracy of the model is limited for the CP-Circular-Yagiexperiment. As the CP moves, the gain of the Yagi antenna, Gr, becomesa function of distance due to its high directivity. In addition, the modelresult is based on a soil measurement at a single point in the field andthe spatial variance in soil properties leads to an error in model prediction.Overall, the channel model provides a sufficiently accurate estimate of thewireless communication but the results highlight the importance of semi-empirical models. This is mainly due to the high uncertainty caused by thesoil properties and the agriculture field.

6.3. Asymmetry of the Communication over Distance

In Section 3, it is shown that the RSS values not only vary among thenodes at different locations, but also change when the AG mote is at differentsides of the UG nodes. Even when the horizontal distances to the UG nodeare the same, the RSS values are different depending on whether the AGnode is approaching the UG node or departing the UG node. For the circularplanar and Yagi antenna pair (Fig. 9(a) and Fig. 9(b)), this is mainly causedby the directivity of the Yagi antenna. However, for the SEA and FWantenna pair (Fig. 8(a)–Fig. 8(d)), the propagation patterns of the antennasare symmetric. Thus, RSS values are expected to be symmetric. To analyzethis phenomenon of the SEA and FW antenna pair, another experimentwith the CPS moving in the opposite direction is conducted, and the resultsare shown in Fig. 8(c) and Fig. 8(d) for both the UG2AG and AG2UG links,respectively.

It is observed that the communication quality can be asymmetric in bothUG2AG and AG2UG cases. For instance, in the CCW direction experiment(Fig. 8(c) and Fig. 8(d)), node 5 communicates with the AG node withinonly 3m when the AG node approaches it. However, when the AG nodedeparts node 5, communication is possible as far as 11m. In addition, theRSS values can be different even when the absolute distances to the UGnode are the same but the AG node is at different sides of the UG node. Fornode 2, in the CCW direction (Fig. 8(c)), when the distance is −5.65m, theRSS value is −73.6 dBm while when the distance is 5.4m, the RSS value is−78 dBm. Meanwhile, in the CW direction (Fig. 8(a)), when the distance is

20

−10 −5 0 5 10 15−95

−90

−85

−80

−75

−70

−65

−60

Distance to the UG node (m)

RS

S (

dBm

)

CW samplesCCW samplesCW fitting curveCCW fitting curve

(a)

−15 −10 −5 0 5 10−95

−90

−85

−80

−75

−70

−65

−60

Distance to the UG node (m)

RS

S (

dBm

)

CW samplesCCW samplesCW fitting curveCCW fitting curve

(b)

Figure 10: The communication asymmetry by comparing the experiment results of theCW direction and CCW direction: (a) Node 2, UG2AG link, (b) Node 5, UG2AG link.

−5.65m, the RSS value is −78.9 dBm, and when the distance is 5.4m, theRSS value is −72.7 dBm. To find the reason for this asymmetry, we comparethe results from the CW and CCW directions.

The RSS values of the UG2AG links of node 2 and node 5 are depictedin Fig. 10. In each sub-figure, polynomial curve fitting is conducted to thesample data and the fitting curves are also shown in the figures. Fromthe figures, it is shown that in the CCW direction experiment, for node 5,communication is better when the AG node is departing it. However, inthe CW direction experiment, the communication is better when the AGnode is approaching it. This observation leads to the conclusion that theasymmetry of the communication quality is mainly caused by the conditionsof the environment rather than the movement of the AG node. Severalfactors cause this asymmetry such as the irregularity of the soil surface andthe impact of the crops. In addition, non-perfect propagation patterns andthe placement of the antenna also contribute to communication asymmetryover distance.

6.4. Burstiness of the Packet Error

To fully understand the characteristics of communication quality, theburstiness of the packet error is analyzed in this section. During the experi-ments, when the AG node and the UG node establish a communication, 100packets are sent from both sides and the interval between two consecutivepackets is 100ms. We analyze the number of consecutive lost packets dur-ing two successfully received packets as the metric for the burstiness of thepacket error.

The average burstiness of the packet error over distance is shown in

21

−15 −10 −5 0 5 10 151

2

3

4

5

6

Distance to the node (m)

Ave

rage

num

ber

of c

onse

cutiv

e lo

st p

acke

ts

Node 2Node 5Node 7

(a) AG2UG communication

−15 −10 −5 0 5 10 151

2

3

4

5

6

Distance to the node (m)

Ave

rage

num

ber

of c

onse

cutiv

e lo

st p

acke

ts

Node 2

Node 5

Node 7

(b) UG2AG communication

Figure 11: The average burstiness of packet error over distance (CW direction).

0 20 40 60 80 100

0.4

0.5

0.6

0.7

0.8

0.9

1

Number of consecutive lost packets

CD

F

AG2UG Cumulative

UG2AG Cumulative

Figure 12: The burstiness of packet error as the percentage of the number of consecutivemissed packets.

Fig. 11 for the CP-Crop-SEA-Hori experiment. In the figure, each pointindicates the average length of the consecutive packet errors. It is shownthat the burstiness of the packet error is not related to the distance. Eventhough node 5 has a higher fluctuation in both the AG2UG and the UG2AGcases, the results do not have trend over distance. This observation agreeswith our findings in [17] that the transitional region of the undergroundcommunication is very small compared with over-the-air communication.Thus, during the communication period, the packet error rate is quite stable.

The distribution of the burstiness of the packet error is shown in Fig. 12.The probability of high burst error decreases sharply as the number of con-secutive lost packets increases. However, the curves have a long tail. Formost cases, only one packet is lost between two successfully received pack-

22

ets. For the AG2UG link, more than 50% of the packet losses are singlepacket losses. For the UG2AG link, the burstiness is higher as only 33% ofthe packet losses are single packet losees. In both cases, 98.56% of the lostpackets have a burst length of 10 or less, where as the maximum burst errorsize is as many as 94 packets.

In Fig. 12, it can also be observed that the performance in the AG2UGcommunication is slightly better than that in the UG2AG communication.As shown before, in the AG2UG communication, the case of one packetlosing among all the lost packets is 17% more than the case in the UG2AGcommunication. Moreover, the average number of consecutive lost packetsin AG2UG is 2.25, while in UG2AG it is 2.89.

6.5. Effects of Canopy and Soil Moisture

The growth of the crop causes an increase in the vegetation canopy andinfluences wireless communication [22]. In addition, soil moisture has anadverse effect on communication [1, 16]. In the following, we discuss theeffects of these physical factors on communication based on the Static-Dry,Static-Wet, and CP-Crop-SEA-Vert experiments as explained in Section 4.The Static-Dry experiment is performed in dry soil without canopy whereasStatic-Wet, and CP-Crop-SEA-Vert are performed in wet soil without andwith the canopy, respectively. In Fig. 13, the resulting RSS and PER valuesfor both AG2UG and UG2AG links are shown for a horizontal inter-nodedistance of 3m.

In Fig. 13, these results are presented in terms of RSS and PER values forboth AG2UG and UG2AG links. The average RSS, the RSS variance, andthe PER values are shown for each experiment. For the horizontal inter-nodedistance of 3m for the experiments, the PER values are very small (below5%) and no meaningful comparison can be done using the PER values.However, the values of RSS show the expected attenuation differences forthe 3 scenarios. As shown in Fig. 13, the Static-Dry experiment results in thesmallest signal attenuation because the soil moisture is smaller (16.6%) andthe vegetation canopy effects can be neglected. The Static-Wet experimentis an intermediate scenario, without the canopy effects, but with a highersoil moisture (22.7%). Finally, scenario CP-Crop-SEA-Vert is the worst casewith 6 dB decrease of the RSS in both links. This is due to the fact thatboth canopy and soil moisture effects contribute to the signal attenuation.

The attenuation caused by the vegetation canopy can be investigated bycomparing the results from Static-Wet and CP-Crop-SEA-Vert in Fig. 13.For both AG2UG and UG2AG links, canopy results in a 3 dB increase in

23

�✁✂✁✄☎✆✝✞✟ �✁✂✁✄☎✆✠✡✁ ☛☞✆☛✞✌✍✆�✎✏✆✑✡✞✁✒✓✔

✒✓✕

✒✓✖

✒✓✗

✒✓✘

✒✓✙

✒✓✚

✒✓✓

✒✓✛

✒✓✜

✒✛✔

✒✛✕

✒✛✖

✒✛✗

✢✣✣✤✥✦✧★

✩✪✖✏✪ ✫✄✬✭

✏✪✖✩✪ ✫✄✬✭

✮✯✰✱✲✳

✮✯✰✱✴✵✶✳

✮✯✰✱✲✵✷✳

✮✯✰✱✷✵✸✳

✮✯✰✱✴✵✸✳

✮✯✰✱✴✳

✹✺✻✼✽✾✿✾❀

❁❂ ❃❄❅❂❆❇

✹✺✻✼❈❈✿❉❀

❁❂ ❃❄❅❂❆❇

✹✺✻✼❈❈✿❉❀

✻❄❅❂❆❇

Figure 13: Effects of the vegetation canopy and the volumetric water content (VWC) fora horizontal inter-node distance of 3m.

attenuation. This result agrees with previous studies [7, 22] and is importantfor the development of environmental-aware networking solutions.

The attenuation caused by the variation in soil moisture can be inves-tigated comparing the results from Experiments Static-Dry and Static-Wetin Fig. 13. The volumetric water content (VWC) of the soil varies from16.6% (Static-Dry) to 22.7% (Static-Wet). Comparing the results from Ex-periments Static-Dry and Static-Wet, for both AG2UG and UG2AG links,the RSS difference is 3 dB. More specifically, the increase of 6.1% in theVWC causes an increase of 3 dB in the signal attenuation for the scenarioof these experiments.

Soil moisture is one of the most important parameters to be consideredin wireless underground communication. Depending on the length of thesoil path that the signal must traverse, the mentioned negative VWC effectcan be very strong, as demonstrated in our previous experiments [16, 17].The impact of these results on the WUSA-CP design is critical.

7. Conclusion

In this work, we develop a novel system through the integration of centerpivot systems with wireless underground sensor networks, i.e., WUSA-CP,for autonomous precision agriculture (PA). The system gathers soil infor-mation, such as soil moisture, from a WUSN in real time to automaticallycontrol the CP for precision irrigation. As a proof-of-concept, empiricalexperiments are realized in a real-life corn field and the lower layer com-munication characteristics are evaluated. A channel model for the system

24

is also provided. To the best of our knowledge, this is the first work thatprovides insight into the integration of a PA irrigation system with WUSNs.

Through empirical analysis, we show that an efficient autonomous PAsolution is feasible using wireless underground sensor nodes. However, sucha solution needs to consider the impacts of the soil properties on communica-tion quality and design the antenna accordingly. Furthermore, in practice,factors such as soil moisture, soil irregularity have salient impacts on thewave propagation and communication performance.

Even though through the careful design of antennas, the undergroundcommunication is practical, there are some other challenges needed to be ad-dressed in the future. First, due to the dynamics in soil moisture, the channelquality varies over time. Thus, specific transmission schemes such as trans-mit power adjustment, probabilistic transmission are needed to maintain theconnectivity of the network and also reduce the waste of energy consump-tion. Second, for the reliability of the communication, an energy efficienterror control scheme that is suitable for WUSA-CP is needed to decreasepacket error rate and improve the feasibility of the system. Last but notleast, an intelligent duty cycle adjustment framework is needed to furtherimprove energy efficiency of the system. The duty cycle of the undergroundnodes should adapt to the soil moisture variation and temperature variationof the environment.

8. References

[1] I. F. Akyildiz and E. P. Stuntebeck, “Wireless underground sensornetworks: Research challenges,” Ad Hoc Networks Journal (Elsevier),vol. 4, pp. 669–686, July 2006.

[2] I. F. Akyildiz, Z. Sun, and M. C. Vuran, “Signal propagation techniquesfor wireless underground communication networks,” Physical Commu-nication Journal (Elsevier), vol. 2, no. 3, pp. 167–183, Sept. 2009.

[3] H. M. Al-Ghobari, “Effect of maintenance on the performance of sprin-kler irrigation systems and irrigation water conservation,” Food Sci. &Agric. Res. Center, King Saud Univ., Tech. Rep. Res. Bult. 141, 2006.

[4] H. R. Bogena, J. A. Huismana, H. Meierb, U. Rosenbauma, andA. Weuthena, “Hybrid wireless underground sensor networks: Quan-tification of signal attenuation in soil,” Vadose Zone Journal, vol. 8,no. 3, pp. 755–761, August 2009.

25

[5] X. Dong, and M.C. Vuran, “A channel model for wireless undergroundsensor networks using lateral waves,” IEEE Globecom ’11, Houston,TX, December 2011.

[6] H. D. Foth, Fundamentals of Soil Science, 8th ed. John Wiley & Sons,1990.

[7] J. Giacomin and F. Vasconcelos, “Wireless sensor network as a measure-ment tool in precision agriculture,” in In Proc. XVIII IMEKO WorldCongress - Metrology for a Sustainable Development, Rio de Janeiro,Brazil, September 2006.

[8] S. Irmak, J.O. Payero, D.E. Eisenhauer, W.L. Kranz, D.L. Mar-tin, G.L. Zoubek, J.M. Rees, B. VanDeWalle, A.P. Christiansen, andD. Leininger, “Watermark granular matrix sensor to measure soil matricpotential for irrigation management,” University of Nebraska-LincolnExtension Circular, EC783, 2006.

[9] S. Irmak, and W.R. Rathje, “Plant growth and yield as affected bywet soil conditions due to flooding or over-irrigation,” University ofNebraska-Lincoln Extension NebGuide, G1904, 2008.

[10] C. T. Johnk, Engineering Electromagnetic Fields and Waves, 2nd ed.John Wiley & Sons, Jan. 1988.

[11] L. Li, M. C. Vuran, and I. F. Akyildiz, “Characteristics of undergroundchannel for wireless underground sensor networks,” in Proc. Med-Hoc-Net 07, Corfu, Greece, June 2007.

[12] N. Peplinski, F. Ulaby, and M. Dobson, “Dielectric properties of soil inthe 0.3–1.3 ghz range,” IEEE Transactions on Geoscience and RemoteSensing, vol. 33, no. 3, pp. 803–807, May 1995.

[13] A. Phocaides, Handbook on pressurized irrigation techniques, 2nd ed.Rome, Italy: Food and Agriculture Organization of the United Nations,2007.

[14] J. Powell and A. Chandrakasan, “Differential and single ended ellipticalantennas for 3.1-10.6 Ghz ultra wideband communication,” in Anten-nas and Propagation Society International Symposium, vol. 2, Sendai,Japan, August 2004.

26

[15] L. Sha, S. Gopalakrishnan, X. Liu, and Q. Wang, “Cyber-physical sys-tems: A new frontier,” in Proc. IEEE International Conference on Sen-sor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2008),Taichung, Taiwan, June 2008.

[16] A. R. Silva and M. C. Vuran, “Empirical evaluation of wirelessunderground-to-underground communication in wireless undergroundsensor networks,” in Proc. IEEE DCOSS ’09, Marina Del Rey, CA,June 2009.

[17] A. R. Silva and M. C. Vuran, “Commmunication with abovegrounddevices in wireless underground sensor networks: An empirical study,”in in Proc. IEEE ICC ’10, Cape Town, South Africa, May 2010.

[18] A. R. Silva and M. C. Vuran, “Development of a Testbed for Wire-less Underground Sensor Networks,” EURASIP Journal on WirelessCommunications and Networking, vol. 2010, 2010.

[19] A. R. Silva and M. C. Vuran, “(CPS)2: integration of center pivot sys-tems with wireless underground sensor networks for autonomous pre-cision agriculture,” in ICCPS ’10: Proceedings of the 1st ACM/IEEEInternational Conference on Cyber-Physical Systems, April 2010, pp.79–88.

[20] Z. Sun and I. F. Akyildiz, “Channel modeling of wireless networks intunnels,” in Proc. IEEE Globecom ’08, New Orleans, USA, November2008.

[21] Z. Sun, I. F. Akyildiz, and G. Hancke, “Dynamic Connectivity in wire-less underground sensor networks,” IEEE Transactions on WirelessCommunications, vol. 10, no. 12, pp. 4334–4344, Dec. 2011.

[22] A. Tavakoli, K. Sarabandi, and F. T. Ulaby, “Horizontal propagationthrough periodic vegetation canopies,” IEEE Transactions on Antennasand Propagation, vol. AP-39, no. 7, pp. 1014–1023, July 1991.

[23] M. J. Tiusanen, “Attenuation of a Soil Scout radio signal,” BiosystemsEngineering, vol. 90, no. 2, pp. 127–133, January 2005.

[24] M. J. Tiusanen, “Wideband antenna for underground Soil Scout trans-mission,” IEEE Antennas and Wireless Propagation Letters, vol. 5,no. 1, pp. 517–519, December 2006.

27

[25] M. J. Tiusanen, “Wireless Soil Scout prototype radio signal receptioncompared to the attenuation model,” Precision Agriculture, vol. 10,no. 5, pp. 372–381, November 2008.

[26] J. Tooper and M. C. Vuran, “Mobile Data Harvesting in Wireless Un-derground Sensor Networks,” to appear in Proc. of SECON ’12, Seoul,Korea, June 2012.

[27] M. C. Vuran and I. F. Akyildiz, “Channel model and analysis for wire-less underground sensor networks in soil medium,” Physical Communi-cation, vol. 3, no. 4, pp. 245–254, Dec. 2010.

[28] “Crossbow Mica2, Micaz, and IRIS motes,” http://www.xbow.com.

[29] “Ward Laboratories,” http://wardlab.com.

28