Carolina at Charlotte, Charlotte, North Carolina 28233. 2Undergraduate Research Assistant, Civil and Environmental Engineering Department, Clarkson.
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2619
Underground Wireless Sensor Networks Using 2nd Generation RF Transceivers Raka Goyal1, S.M. ASCE, Rose Kennedy2, Brandon Kelsey3, Matthew Whelan4, A.M. ASCE and Kerop Janoyan5, P.E., M. ASCE 1
Graduate Research Assistant, Civil and Environmental Engineering Department, University of North Carolina at Charlotte, Charlotte, North Carolina 28233 2 Undergraduate Research Assistant, Civil and Environmental Engineering Department, Clarkson University, Potsdam, New York 13699 3 Graduate Research Assistant, Civil and Environmental Engineering Department, Clarkson University, Potsdam, New York 13699 4 Assistant Professor, Civil and Environmental Engineering Department, University of North Carolina at Charlotte, Charlotte, North Carolina 28233 5 Associate Professor and Executive Officer, Civil and Environmental Engineering Department, Clarkson University, Potsdam, New York 13699
ABSTRACT: The introduction of low-cost chip transceivers has driven the exploration of applications extending wireless sensing technology below the ground. The fundamental challenge with wireless underground sensor networks (WUSNs) however is that the high dielectric permittivity of the soil leads to significant absorption losses during electromagnetic (EM) wave propagation. The underground wave is also subject to attenuation due to phenomena such as multi-path fading, reflection and refraction, effectively limiting the node-to-node communication distance to just a few meters. Although some analytical and empirical models have been proposed to characterize the underground RF channel, validation of these models using commercial off-the-shelf wireless sensing hardware has been limited. This research aims to better characterize the performance of WUSNs in different soils, under varying soil conditions, and with different transceiver configurations. Parametric analysis of the influence of carrier frequency, data rate and modulation format on underground EM wave propagation is included. The experiment includes controlled laboratory tests in uniform soil with prepared moisture content and density as well as an in-situ field deployment. Presented are laboratory and field testing data conducted with 2nd generation wireless sensor networks, commonly used in terrestrial applications, buried in underground environments. An extensive matrix of tests is conducted to examine the influence of both radio characteristics, including modulation format, carrier frequency, and data rate, as well as locational characteristics, including transmission distance and depth of burial, on the received signal strength of the transmitted packets. Statistical analysis of the experimental data acquired in the laboratory and field is found to be consistent and indicate that carrier frequency, data rate, and transmission distance most strongly influence the received
Page 1 Geo-Congress 2014 Technical Papers
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2620
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
signal strength. In this study, the maximum permissible node-to-node transmission distance is not reached in either the laboratory or field testing, and thus the results indicate that node-to-node transmission distances of several meters can be reliably achieved in granular soils. INTRODUCTION Wireless underground sensor networks investigated in this study are those in which all the transmitting and receiving devices are buried in the ground and signal transmission is through the soil medium. These are different from hybrid underground-terrestrial networks where some of the devices are located above the ground and the signal propagates partially through air, or underground networks such as those located in coal mines, sewers or subways, where the signal propagation is completely through the air. Given the various advantages like surface invisibility, ease of large spatial distribution sampling due to lack of wires, and lower cost, WUSNs can be used in a variety of applications including soil condition monitoring of agricultural and sports fields, structural health monitoring of buried infrastructure and utilities, earthquake and landslide disaster management, improved mine safety, and covert border protection. However, development of WUSNs has proved to be challenging because soil is not as ideal of an electromagnetic (EM) wave propagation medium as air. EM waves in the common ultrahigh frequency (UHF) range utilized by commercial off the shelf terrestrial networks suffer significant absorption losses due to the dielectric properties of soil. These losses vary with soil properties as well as characteristics of the propagating wave. Although the feasibility of underground EM communication has been established, limited experiments have been carried out thus far to validate the available transmission models. Furthermore, much of the work done to-date has used first-generation chip transceiver hardware with limited performance characteristics, so additional experimental work is warranted to effectively characterize the underground EM channel for reliable design of modern WUSNs. BACKGROUND Most of the models for underground EM wave propagation are developed by modifying the Friis free space path loss equation by introducing additional path loss terms to account for signal attenuation through the soil due to various factors. Thus the basic link budget equation is given by = where
+
+
−
−
,
(1)
= signal power at the receiver in dBm = transmit power in dBm =gain of the transmitter antenna in dB = gain of the receiver antenna in dB = free space path loss due to spherical divergence of the wavefront =20 log , where
Page 2 Geo-Congress 2014 Technical Papers
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2621
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
= distance between the transmitter and the receiver in meters = freespace wavelength of the carrier frequency, and = signal attenuation in dB due to absorption by soil and water This equation was first used to demonstrate the feasibility of underground EM wave propagation (Akyildiz and Stuntebeck, 2006). The path loss estimated using this equation compared well with actual measured values in different soil mixtures for frequencies in the 0.3-1.3 GHz range in previous experiments to characterize the soil dielectric constant in terms of its material properties (Peplinski et al., 1995). As expected, soils with coarser particle size content, like sand, produced lesser path loss than those with finer particle size content, like silt and clay. In subsequent research, a Modified Friis model was developed which accounted for the effect of critical soil properties like density, temperature, water content, and particle size on the soil dielectric constant, and hence the EM wave propagation through it. Experimental results and empirical models support the general relationships that the signal attenuation increases with carrier frequency, transmission distance, and soil volumetric water content (Vuran and Akyildiz, 2010). Other phenomena that affect underground EM wave propagation include multipath fading, reflection, refraction as well as ambient noise from power lines, lightning, electric motors and other intentional or unintentional radiators. The multipath fading phenomena occurs due to scattering of the radio frequency signal by obstructions such as rocks and tree roots, and is more common near the ground surface. Reflection and refraction also occur at transition surfaces when the wave travels from one medium to another, such as from soil to air or from soil to water (Akyildiz and Stuntebeck, 2006). A number of models have been developed to account for these effects (Vuran and Akyildiz, 2010; Bogena et al., 2009; Chaamwe et al., 2010), however very few experimental studies have also been done to characterize the underground channel with chip transceivers operating in the UHF band. Stuntebeck et al. (2006) performed experiments with MicaZ motes operating at 2.4GHz carrier frequency, while Silva and Vuran (2009) used Mica2 motes operating at 433MHz. The latter study concluded that reliable internode communication upto 1 m could be achieved with 10dBm (10mW) transmit power. However, it is important to note that the Mica family of motes was developed with first generation Chipcon transceivers that have since been surpassed by platforms offering significant improvements in output power, sensitivity, and selectivity, which might greatly improve the performance underground. The current study uses the Synapse RF300 wireless module for the 915 MHz frequency band. This off-the-shelf platform incorporates the Silicon Labs Si1000 wireless microcontroller, which yields up to -121dBm sensitivity depending on the data rate and modulation format employed. Furthermore, it has an integrated frontend RF amplifier to provide maximum power of 20dBm (100mW), which produces a potential link budget of 141dB, compared to 94dB for the MicaZ and 103dB for the Mica2 motes. Different configurations with varying depths and distances as well as different data rates, carrier frequencies and modulation formats are investigated for their effect on signal transmission. The results are used to validate the existing models as well as to estimate the maximum permissible single-hop communication
Page 3 Geo-Congress 2014 Technical Papers
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2622
achievable with modern transceiver hardware in an ideal soil.
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
LABORATORY TESTING Laboratory tests were conducted in the structural high-bay laboratory of the Energy Production and Infrastructure Center at the University of North Carolina at Charlotte. The test setup, as shown schematically in Figure 1 and photographed in Figure 2, is comprised of a concrete pit filled loosely with uniform concrete sand (ASTM C33 fine aggregate) in which four fiberglass tubes are embedded at a distance of 1 m from each other. In this study, fiberglass tubes were selected to house the transceivers instead of paper/plastic in earlier studies, since the fiberglass does not attenuate the RF signal. The 3 cm internal diameter fiberglass tubes each hold a Synapse RF-300 node with onboard, omnidirectional, quarter-wave chip antenna. This chip antenna has a small form factor (7mm x 2mm x 0.8mm) suitable for embedded instruments but introduces a -4.0dBi average gain on both the transmitter and receiver paths ( + −8 . Embedded software for the Synapse nodes was developed inhouse to facilitate bi-directional communication of messages and collection of received signal strength indication (RSSI) from each packet. In addition to the Synapse RF nodes, a Decagon 5TE sensor was embedded at the center of the pit for measuring soil dielectric constant, temperature, and electric conductivity of the bulk soil. A Matlab script was developed to autonomously operate the master node and record signal strength measurements from the remote nodes as well as soil property measurements from the Decagon sensor.
a)
Page 4 Geo-Congress 2014 Technical Papers
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2623
b) FIG.1. WUSN Laboratory Experimental Setup; a) Cross-section of test pit, b) Elevation view of test pit and sensor configurations
a)
b)
FIG. 2. Photographs of Laboratory Experimental Setup; a) Prior to Placement of Sand, b) Nearly Complete Placement of Sand The matrix of sensor placements investigated can be interpreted from the indexing in Figure 1b. Within this figure, squares a, b, and c designate the location of the three remote nodes in plan distances of 1 m, 2 m, and 3 m, respectively, from the master node. The sensor depths investigated are denoted by circles 1, 2, and 3, which correspond to depths of 0.3m, 0.6m, and 0.9m from the ground surface, respectively. Prior to testing in the soil, a set of readings was obtained to characterize the free-air transmission performance for each of the node-to-node transmission distances. In this study, effects of carrier frequency, data rate, and modulation format were studied experimentally, in addition to the effect of placement in the soil. A typical measurement cycle consisted of a request issued by the master node to each remote node, sequentially, for 100 packets of data which are then received without
Page 5 Geo-Congress 2014 Technical Papers
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2624
acknowledgement from the master and logged into a measurement file. Each request was accompanied by instructions for a specific combination of carrier frequency (905MHz, 915MHz, or 925MHz), data rate (2.5kbps, 25kbps, or 250kbps), and modulation format (Frequency-Shift Keying (FSK) or Gaussian Frequency-Shift Keying (GFSK)) that both the remote and master node would program their transceiver for prior to packet transmission. In total, the matrix of radio settings and positioning of the three nodes results in 54 combinations of carrier frequency, data rate, modulation format, and transmission distance for each measurement cycle. Measurement cycles were repeated for six spatial configurations of the sensor nodes at different depths: 1111, 2111, 2222, 3111, 3222 and 3333, where the first number in each cycle refers to the depth of the master node according to the index in Figure 1b and the remaining numbers correspond to the depth of the remote nodes. For example, the notation 2111 denotes a measurement cycle with the master node positioned at 0.6 m depth and each of the remote nodes at a 0.3 m depth. As a result of including depth of the nodes as an additional variable in the experimental program, the complete test program consisted of 324 combinations of carrier frequency, data rate, modulation format, plan transmission distance, and depth of sensors. During all test cycles, the soil sensor was programmed to return measurements at the end of each signal transmission. Samples of soil from various locations in the pit were also collected to determine gravimetric moisture content of soil by the oven-drying method. The sand in the pit was weighed prior to filling to determine its bulk density. The average gravimetric water content of soil was determined to be 5.61% and the bulk density 0.38 g/cm3. The volumetric water content (VWC) being a product of the above two values turned out to be 0.021 cm3/cm3 (Bardet, 1997). The soil sensor returned the dielectric constant as 1.24 for the soil throughout the duration of the experiment. The dielectric constant (K = 1.24) and the bulk density (ρb = 0.38 g/cm3) yield a VWC of 0.076 cm3/cm3 using =
√
.
. .
.
(2)
.
(Malicki et al., 1996). The soil sensor returned measurements for the soil electric conductivity as zero and temperature as 21° Celsius throughout the experiment. FIELD TESTING Field deployment tests were conducted within in-situ well compacted soil in Potsdam, NY. A soil sample was obtained at 45cm depth and classified in the laboratory as poorly graded sand with silt. The soil sample collected at the site produced a bulk density of 1.428 g/cm3. The gravimetric water content was assessed in the laboratory as 11.7%, which yields 16.7% volumetric water content. Similar to the laboratory testing, fiberglass tubes were used to contain the sensor nodes. A series of three fiberglass tubes were installed in holes augered into the soil in a triangular arrangement shown in Figure 3. A Decagon GS3 soil sensor was buried at a 45cm depth to measure the dielectric constant, temperature, and electrical conductivity of the soil throughout the testing.
Page 6 Geo-Congress 2014 Technical Papers
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
a)
2625
b) FIG 3.WUSN Field Experimental Setup; a) Plan view of Sensor Layout, b) Elevation view of sensor depths
All testing was performed consistent with a typical measurement cycle described for the laboratory testing. During field testing, all nodes were positioned at a fixed depth of 53.3 cm below the ground line. To achieve average signal strength readings for transmission distances of 0.89m, 1.22m, and 1.52m, the master node was first positioned at location 2 to communicate with remote nodes at locations 1 and 3. Then the master node was repositioned to location 3 and communication would be established with a remote node positioned at location 1. All modulation formats, carrier frequencies, and data rates were examined at each specified distance to create a set of 54 unique combinations. During testing, the Decagon GS3 sensor reported a dielectric constant of 1.26, null electric conductivity, and a constant temperature of 14.9oC. RESULTS AND DISCUSSION Experimental data from the laboratory and field testing were independently analyzed statistically using the Minitab commercial software package. In these analyses, the average received signal strength over the 100 packet data collections were examined with respect to the modulation format, data rate, carrier frequency, and distance. For the laboratory testing, the effect of depth of burial was also examined. For the laboratory data, a balanced general linear model was developed using mean signal strength values for a total of 162 combinations corresponding to configurations 1111, 2222 and 3333. Analysis of variance of signal strength was carried out with verification of normality assumptions. The model had an adjusted R-squared value of 98.78% which demonstrates an overall good fit. For the field data, a similar balanced general linear model was developed with an adjusted R-squared value of 98.93%. Plots showing interaction effects of the variable factors associated with the laboratory testing and field test are presented in Figures 4 and 5, respectively. From these graphs, it is apparent that modulation format (FSK or GFSK) does not significantly affect the signal strength. Likewise, from the laboratory testing, it can be deduced that the depth of burial produced some effect on signal strength but that the effect was irregular with data rate, carrier frequency, and distance. In the laboratory testing, the signal strength was maximized for the 25kbps data rate, 905MHz carrier frequency, 1m node-to-node distance, and 0.9m depth. Consistent with these laboratory results, the signal strength was maximized for the 25kbps data rate, 905MHz carrier
Page 7 Geo-Congress 2014 Technical Papers
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2626
frequency, and 0.89m node-to-node distance in the field testing. The short transmission distance associated with these optimal sets is not surprising, but the data rate is unexpected since the receiver sensitivity of the chip transceiver improves with lower data rate. Additionally, the center frequency of the chip antenna is 915MHz, which indicates that the attenuation of signal strength with carrier frequency is a more significant effect than the frequency characteristics of the antenna in this case study. The decrease in signal strength with increased carrier frequency and distance corroborates theoretical expectations and the findings of earlier research. The interaction plots show that most of the factors are independent of each other except depth and distance. The P-values for interactions of carrier frequency with all other factors are also less than 0.05 indicative of significant interaction effect. The independent effects of the radio settings and relative location are presented in Figure 6. This figure presents the mean received signal strength associated with each factor for both the laboratory and field test data. The trends in these graphs emphasize that all factors except modulation format and depth of burial affect the signal strength significantly. The independent effects are consistent between the laboratory and field measurements with the primary difference being only consistently lower signal strength in the field data. This is attributed to the higher bulk density of the in-situ soil, which is associated with increased signal attenuation. Interaction Plot for Lq (dBm) Data Means 2.5
25.0
250.0
905
915
925
0.3
0.6
0.9
1
2
3 -30
Modulation
-45 -60 -30
Data Rate
-45 -60 -30
Carrier Freq.
-45 -60 -30
Depth
-45 -60
Modulation(1-FSK, 2-GFSK) 1 2 Modulation(1-FSK, Data2-GFSK) 1 Rate(kbps) 2 2.5 25.0 Modulation(1-FSK, 250.02-GFSK) Data Carrier 1 Rate(kbps) Frequency(MHz) 2 2.5 905 Modulation(1-FSK, 25.02-GFSK) 915 250.0 925 1 Data Carrier 2 Depth(m) Rate(kbps) Frequency(MHz) 0.3 2.5 0.6 905 25.0 0.9 915 250.0 925
Distance
FIG. 4. Interaction Effect of Modulation Format, Data Rate, Carrier Frequency, Depth and Distance on Laboratory WUSN Link Quality
Page 8 Geo-Congress 2014 Technical Papers
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2627
Interaction Plot for LQ(dBm) Data Means 2.5
25.0
250.0
905
915
925
0.9
1.2
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Modulation
1.5 -40 -60 -80
Data Rate
-40 -60 -80
Carrier Freq.
-40 -60 -80
Modulation Format(FSK1 GFSK2) 1 2 Modulation Data Rate Format(FSK1 (kbps) GFSK2) 2.5 25.0 1 250.0 2 Modulation Data RateCarrier Format(FSK1 Frequency(MHz) (kbps) GFSK2) 2.5 905 25.0 1915 250.0 2925
Distance
FIG. 5. Interaction Effect of Modulation Format, Data Rate, Carrier Frequency, and Distance on In-Situ WUSN Link Quality
FIG. 6. Independent Effect of Modulation Format, Data Rate, Carrier Frequency, Depth and Distance on Laboratory WUSN Link Quality
Page 9 Geo-Congress 2014 Technical Papers
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2628
Neither in the laboratory testing nor field test was the maximum permissible node-tonode transmission distance reached. Examining the independent effect of transmission distance, the average signal strength at the 3m transmission distance in the laboratory was -54.6dBm and at the 1.52m transmission distance was -75.2dBm. With the current 2nd generation chip transceiver hardware, the remaining link budget is approximately 66.4dB and 45.8dB, respectively. This indicates that current off-theshelf wireless sensor network hardware can reliably achieve several meters of nodeto-node transmission distance in sand and in sandy soils with silt. This is a significant increase in permissible node-to-node transmission distance reported in prior studies utilizing Mica2 and MicaZ modes. CONCLUSIONS Laboratory and field testing has been conducted with 2nd generation wireless sensor networks, commonly used in terrestrial applications, buried in underground environments. An extensive matrix of tests was conducted to examine the influence of both radio characteristics, including modulation format, carrier frequency, and data rate, as well as locational characteristics, including transmission distance and depth of burial, on the received signal strength of the transmitted packets. Statistical analysis of the experimental data acquired in the laboratory and field were consistent and indicate that carrier frequency, data rate, and transmission distance most strongly influence the received signal strength. Although the maximum permissible node-tonode transmission distance was not reached in either the laboratory or field testing, the results indicate that node-to-node transmission distances of several meters can be reliably achieved in granular soils. Future work will expand the experimental test matrix and develop empirical models to enable underground sensor network topology and routing designs. ACKNOWLEDGMENTS This material is based upon work supported by the National Science Foundation under Grant No. 1055669. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. REFERENCES Akyildiz, I. F., and Stuntebeck, E. P. (2006). “Wireless underground sensor networks: Research challenges.” Ad Hoc Networks, 4, 669-686, Elsevier. Bardet, J.(1997). Experimental Soil Mechanics, Prentice Hall, New Jersey, US. Bogena, H. R., Huisman, J.A., Meier, H., Rosenbaum, U. and Weuthen, A.(2009). “Hybrid Wireless Underground Sensor Networks: Quantification of Signal Attenuation in Soil.” Vadose Zone Journal, 8,755-761. Chaamwe, N., Liu, W., and Jiang, H. (2010).“Wave Propagation Communication Models for Wireless Underground Sensor Networks.” 2010 12th IEEE
Page 10 Geo-Congress 2014 Technical Papers
Downloaded from ascelibrary.org by University of North Carolina At Charlotte on 12/28/14. Copyright ASCE. For personal use only; all rights reserved.
Geo-Congress 2014 Technical Papers, GSP 234 © ASCE 2014
2629
International Conference onCommunication Technology (ICCT), November 11-14, pp. 9-12. Malicki, M. A., Plagge, R. and Roth, C. H. (1996).“Improving the calibration of dielectric TDR soil moisture determination taking into account the solid soil.”European Journal of Soil Science, 47, 357-366. Peplinski, N. R., Ulaby, F. T., and Dobson, M. C. (1995).“ Dielectric Properties of Soils in the 0.3 – 1.3 GHz Range.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No.3, pp. 803-807. Silva, A. R., and Vuran, M. C. (2009).“Development of a Testbed for Wireless Underground Sensor Networks.” EURASIP Journal on Wireless Communications and Networking, Vol. 2010 Article ID 620307, 14 pages. Silva, A. R., and Vuran, M. C. (2009). “Empirical evaluation of wireless underground-to-underground communication in wireless underground sensor networks, in: Proc. IEEE DCOSS’09, Marina Del Rey, CA. Stuntebeck, E. P., Pompili, D., and Melodia, T. (2006). “Wireless Underground Sensor Networks using Commodity Terrestrial Motes.” Georgia Institute of Technology, Atlanta. Vuran, M. C., and Akyildiz, I. F. (2010), “Channel model and analysis for wireless underground sensor networks in soil medium.”Physical Communication, 3, 245-254, Elsevier.
Page 11 Geo-Congress 2014 Technical Papers
