Modeling and measurements for wireless communication networks in underground mine environments

Measurement 149 (2020) 106980

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Modeling and measurements for wireless communication networks in underground mine environments Alok Ranjan a,⇑, H.B. Sahu a, Prasant Misra b a b

Department of Mining Engineering, National Institute of Technology, Rourkela, India TCS Research & Innovation, Tata Consultancy Services Ltd., Bangalore, India

a r t i c l e

i n f o

Article history: Received 9 November 2017 Received in revised form 13 June 2019 Accepted 23 August 2019 Available online 26 August 2019 Keywords: Electromagnetic wave Propagation Channel model Wireless networks Noisy environment Underground mines Mine monitoring

a b s t r a c t One of the crucial problems being faced by wireless system designers is experimental understandings of the electromagnetic (EM) wave propagation in high stress environments, such as underground mines and tunnels, so that reliable performance can be achieved. This is driven by the fact of limited availability of real-time data of operational underground mines with different measurement considerations. This paper reports extensive experimental studies and proposes a modified multimode based channel model for wireless communication networks in underground mine environments. The experimental campaigns were carried out in an underground coal mine considering significant use cases viz. line of sight (LOS), no line of sight (NLOS), across the curves and bends, in noisy environments, straight tunnels, and near the face (extraction area). We then validated the proposed channel model with the experimental measurements. The validation results showed reliably a good agreement with a model accuracy of 94.60%, 91%, 90.98% for the straight tunnel, along the belt conveyor and near face respectively. The proposed model outperforms the state-of-the-art channel model for mine workings with model accuracy differences of 60.1%, 60.45% and 58.56% for straight tunnel, along the belt conveyor and near face respectively. A model accuracy of 78.31% is achieved across the curvature measurement scenario. Hence, the proposed channel model can be accepted for wireless communication system design and deployment planning in underground mines to provide a reliable two-way wireless communication and coverage. Furthermore, detailed link analysis of wireless sensor networks in mine workings is also discussed considering packet reception rate with respect to distance and received signal strength. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Underground mines are high-stress environments. They are not only harsh under regular operating conditions, but also have a significant risk of accidents that can damage mine infrastructure, cause loss of lives, and disrupt communication. Reliable communication networks are, therefore, essential for successful mine operation under regular conditions, and is also vital to the success of emergency response and rescue operations. Underground mines are extensive labyrinths and generally consist of a large network of long and narrow tunnels arranged at various depth levels below the ground surface. The length and widths of the mine tunnels vary depending upon the type of mine and thickness and orientation of ore body. Communication in underground mines have traditionally been functionalized through wired or leaky-feeder cable systems. They are known to perform well under normal operating conditions, but get severely crippled, and ⇑ Corresponding author. E-mail address: [email protected] (A. Ranjan). https://doi.org/10.1016/j.measurement.2019.106980 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.

dysfunctional post structural deformations resulting from roof falls, side wall collapses, fires and explosions, etc. While various protection methods have been devised to improve their operational robustness [1]; they are expensive, make maintenance more difficult, and increase system complexity. Wireless communication systems are a promising alternative to overcome many of these limitations. The propagation characteristics of radio waves in underground mines are significantly different than terrestrial settings; and can be attributed to the unique morphology of mine galleries such as roughness of sidewalls, disorientation in ore body, curvature and bends, gallery dimension, and cross-cuts, etc., [2–5]. Motivated by the need to accurately capture the structural effect of the physical space on the propagation characteristics of radio waves, we propose a reliable channel model for underground mines. The major contributions of this paper are as follows:
To propose a reliable channel model particularly for an operational underground mine environment

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Address and characterize the effects of long-range tilt of the ore body and surface wall roughness on the radio signal behavior inside mine workings
Report and validation of the proposed channel model with different experimental use cases
Maximum coverage ranges between two wireless sensor nodes for different use cases The remainder of this paper is organized as follows. In Section 2, the current state of the art is discussed followed by a description of the different experimental measurement considerations and study area in Section 3. It is then followed by the proposed multimode channel model in Section 4. In Section 5, a detailed analysis of the radio signal behavior in underground mine tunnels compared with the real-time data and their validations are presented. We then wrap up the findings and conclusion drawn from this study in Section 6. 2. Related work Channel modeling and analysis of radio propagation in tunnel environments has been studied for many decades, where the focus has been to develop the theoretical underpinnings for robust and reliable communication systems that can operate under both normal and emergency situations. An approach to model the electromagnetic (EM) signal propagation in tunnel environment is to consider the environment as oversized lossy dielectric where waveguide effects come into the picture at certain frequencies. Hence the wavelength must be smaller than the transverse dimension of the tunnel [6]. A seminal work in this direction was performed by Emslie et al. [7] where they modeled the propagation characteristics of ultrahigh frequencies (UHF) under waveguide effect. This model characterizes the signal behavior in the far region, but does not consider antenna parameters and high order propagation modes that have a significant impact on the signal propagation behavior. Zhang et al. [8], and Klemenchits and Bonek [9] overcome the previous model’s limitation and address the antenna parameters through a ray tracing approach. However, this technique needs a fairly detailed infor-

mation of the environment [10] and becomes computationally intensive for modeling longer tunnels. The alternate direction implicit (ADI) method [11] captures the wireless propagation characteristics for longer tunnels to a good extend, but still does not consider the higher order modes. To address the propagation behavior of higher order modes near the transmitter antenna in a tunnel environment, Sun et al. [12], proposed a multimodal method. The model characterizes the radio signal near the source antenna as well in the far region. Further, the proposed work is validated with the published data carried out in the transportation tunnel. However, the environmental features considered for the study is not exactly applicable for operational underground mines where surface wall roughness and longrange tilt of the ore body significantly caused high attenuation [13,14]. To understand the signal behavior in real tunnels Fuschini and Falciasecca [15], proposed mixed rays and mode based approach for the modeling of radio waves in road and rail tunnels. They considered the wall roughness and presence of objects in the tunnels which could have affected the signal propagation. Nevertheless, the typical features of an underground mine tunnels were not considered in their study. Recently in [16], it is reported that the ray tracing and modal methods both are mathematically same. In another recent works of the authors Zhou and Jacksha [17,18] the radio signal behavior at UHFs in tunnels based on modal and FDTD methods are presented and discussed. However, the experiments were carried out in a railroad tunnel which was not operational, and the typical infrastructures were either not present or removed. Moreover, the authors considered that the signal behavior of radio waves in rail/ road tunnels and near cross-junctions in underground mine tunnels is same, but we have shown in our study that this is not true in the case of underground mines. A synopsis of some of the major works related to channel modeling and measurements carried out in the tunnel environments is listed in Table 1. It gives a brief overview of the current existing channel models. References [8,19–23] reports the signal behavior in tunnel environment with considerations of smooth wall, less complex infrastructures, mild curves and more importantly the wider width and higher heights than an operational underground mine tunnel.

Table 1 Synopsis of the Current Existing Channel Models. Reference

Tunnel Environment

Modeling Technique

Comments

[7] [8] [9] [12] [14]

Mine environment Road tunnels Road tunnel Road/transport Underground mines

Waveguide Ray tracing Statistical Multimode Statistical

[15] [17]

Rail/transport Rail road

Ray tracing and mode based Ray tracing and modal method

[18] [19] [20] [21] [23] [24] [25] [26] [27] [28]

Concrete tunnel Road tunnel Railway tunnel Transportation tunnel Road tunnel Experimental mine Experimental mine Experimental mine Experimental mine Circular transport tunnel

[29] [30] [31] [32] [33] [34]

Longwall operational mine Operational mine Longwall operational mine Operational mine Road and underground mines Operational mine

FDTD Statistical Waveguide Statistical Hybrid Statistical Statistical Statistical Statistical Boundary wall conditions using fourier transform Two slope Free space and wave guide Hybrid model Cascade impedance Wave guide Statistical

Lower order modes only Numerically complex for longer tunnels Analysis of antenna positions Validation with published data Considered and analyzed the effects of tilt on signal propagation. However, no validation was performed Analysis of small sized objects and their significance Assumed same behavior of radio waves in road tunnels and cross-cuts in mines No infrastructure considerations Not suitable for mine environments Not suitable for mine environments Delay and path loss analysis Introduced the concept of break point Channel capacity of MM wave PDP analysis Mentioned surface roughness significane Narrowband measurements Analysis of antenna positions LOS only LOS only No details about proposed theoretical model Considered wall roughness Mentioned uneveness of the ore body significance Discussed the effects of cross-junctions and wide openings

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Moreover, these tunnels are ideal for the rail/road transportation. Although significant contributions to the channel modeling and measurements in the tunnel and high stressed environments like mine tunnels have been reported; to the best of the knowledge of the authors, there is not any literature available which reports and validate multimode based channel model considering different measurement use cases and typical features of an operational underground mine tunnel. Additionally, most of the experimental works reported in the literature are either limited to experimental mines [24–27] or in subway tunnels (listed in Table 1). 3. Study area and experimental setup To understand the radio signal behavior in confined spaces like underground mine tunnels/workings, a measurement campaign was carried out in an operational underground coal mine. The mine named Nandira Colliery comes under the control of Mahanadi Coal Fields Limited (MCL), a subsidiary of Coal India Limited (CIL). It is situated in the Talcher area of Angul district, Odisha, India. The mine tunnel width is 4 m and 3 m in height and has room and pillar mining method to extract the coal seams. The studied mine is a gassy mine of degree 1 (classified on the basis of methane emission rate). In a particular shift of mine operation, nearly 200 miners work inside the mine workings. We considered different use cases to carry out the measurements. The detailed information of the measurement scenario is listed in Table 2. 3.1. Experimental setup inside underground mine tunnel We carried out measurements at different levels of the underground mine workings to analyze the radio propagation characteristics considering different use cases as listed in Table 2. In the present study, 3 TelosB motes operating at 2.4 GHz signal frequency and a laptop to log the captured data were used. Out of 3 sensor nodes, one node was used as a master node connected to the laptop to pass the command to the another node working as a transmitter (TX) and the last node was used as the receiver (RX). For all measurement considerations, the TX was static. However, the RX was non-stationary and relocated to different locations along the axial distance of the mine tunnel in a fixed distance strategy as depicted in Fig. 1. Tripods were used to mount the sensors. The TX and RX mounting positions are mentioned in Table 2. Rail tracks, power lines, water pipes, roof bolting and stitching, and pipes having small diameter are as a part of complex infrastructure inside the mine workings. The measurements were

Fig. 1. Experimental setup inside mine gallery.

continued to record the signal strength at the RX unless and until the node stopped receiving the packets transmitted by the TX node. It was repeated for every use case presented in this study. This strategy helped to understand the maximum communication range between two wireless sensor nodes in the mine galleries. Also, such study might be useful to choose the node’s features such as transmission power, and antenna gain, etc., working as the coordinator or gateway node to further pass on the collected data to the sink node or base station at the surface. 4. Multiomode waveguide model for underground mine environment We considered an underground mine tunnel as a hollow rectangular lossy dielectric waveguide with an equivalent cross-sectional dimensions of width 2 w and height 2 h as shown in Fig. 2. We assumed that the origin of the coordinate system is set in the center of the mine tunnel cross-section. Here, the horizontal axis

Table 2 Different use cases for experimental measurements. Use Case

Description

TX-RX Position

Straight tunnel

The tunnel was a straight tunnel and was a line of sight (LOS) case between the transmitter and receiver There was a belt conveyor passing through at a distance of 42 m from the transmitter which caused for partial LOS up to few meters and was a continuous source of noise in the mine environment Measurement were carried out along the belt conveyor to analyze the effects of noise on the radio signal strength and packet loss Since the extraction area posed different set of challenges for work compared to normal passage such as high humidity, poor ventilation, less height, random roof bolting for support and etc.; we also considered this case and performed measurements near the face of mine working where coal extraction was going on To study the effects of bends on the signal propagation, measurements were performed across the sharp curve having a turn of 70 degrees and was a cross junction The TX-RX was mounted on the side walls

at tunnel centre and 1.7 m above the floor

Straight tunnel with belt conveyor

Along the belt conveyor Near face

Across the curvature

On side walls

at tunnel centre and 1.7 m above the floor

Tx-RX was kept 1 m away from the side wall and mounted 1.7 m above the floor at tunnel centre and 1.7 m above the floor

at tunnel centre and 1.7 m above the floor

Both TX-RX were mounted 1 m and 2 m above the floor

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Fig. 2. A cross section of the waveguide model with dielectric walls.

x is along the width of the mine tunnel and y-axis along the vertical i.e. height of the tunnel and z-axis along the tunnel length. Let the electrical parameters for floor/ceiling, side walls, and mine air is K h ; K v , and K a respectively and are defined as:

K h ¼ 0 h þ K v ¼ 0 v þ K a ¼ 0 a þ

rh

j2pf0

rv

j2pf0

ra

j2pf0

ð1Þ

mp  np  x þ wx :cos y þ wy 2w 2h

ð4Þ

where wx ¼ p=2 if m is odd and wx = 0 if m is even; wy ¼ p=2 if n is even and wy ¼ 0 if n is odd. It is assumed here that the electric field due to co-polarized components plays a dominant role over the cross-polarized components, hence can be ignored [36]. The mode intensity of each and every mode depends mainly on the excitation of source, which can be calculated by applying the geometrical optical (GO) model [37] in the transmitting plane. The EM field distribution in the excitation plane is, in fact, the overall weighted sum of the field posed by all the significant modes and can be found by summing up all modes in mine tunnel (x, y, z) as follows:

ðamn þjbmn Þ:z C mn :Eeigen m;n ð x; yÞ :e

ð5Þ

where the mode intensity C mn on the transmitting plane of the mine tunnel is given as [12]:

ð2Þ ð3Þ

1 X 1 X m¼1 n¼1

C m;n ¼

where h ; v , and a are the relative permittivity and rh ; rv , and ra are the respective conductivity of the horizontal, vertical mine tunnel walls, and mine air respectively. The signal of frequency for propagation in tunnel is denoted as f 0 . The magnetic permeability parameter is considered to be the same and equal to that of free space l0 . In a dielectric lossy tunnel waveguide, the wave number pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi of tunnel space can be defined as K ¼ 2pf 0 l0 0 a . The transmitter (Tx) and receiver (Rx) in the tunnel space are located at T(x0, y0, z0) and R(x, y, z) respectively. We also assumed that the source of excitation is vertically polarized (y-directed), the electromagnetic (EM) field distribution of vertically polarized hybrid modes aggregated at any position (x, y, z) inside the mine tunnel, can be calculated by solving Maxwell’s equations for the considered cross-sectional dimensions and the material of the mine tunnel in terms of eigenfunctions as follows [35,36,12]:

Eeigen m;n ð x; yÞ ’ sin

ERx ðx; y; zÞ ¼

mp  np  E0 p qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  mp 2  np 2ffi :sin 2w x0 þ wx :cos 2h y0 þ wy wh 1  2wK  2hK ð6Þ

Also, the phase shift coefficient bmn and attenuation amn are given as [36]:

bm;n ¼

am;n ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi mp2 np2 K2   2w 2h 1  mp 2 1 1  np 2 Kh Re pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi þ Re pffiffiffiffiffiffiffiffiffiffiffiffiffiffi w 2wK K v  1 h 2hK Kh  1

ð7Þ

ð8Þ

4.1. Combining the mine wall roughness and tilt of the ore body Sun et al. [12] developed and proposed the multimode waveguide model, which addressed the research gap of channel modeling approaches in the past for wireless communication in tunnel environments. But the proposed model shortfalls to capture and consider the typical features (-i.e. long range tilt variations and side wall roughness of mine tunnels) of underground operational mines. In addition, the proposed model was validated with the published data in which the experiment was performed in transportation tunnels, but not in operational underground mines. It should be noted here that an operational underground mine tunnel differs from the ideal road/rail tunnel in EM wave propagation characteristics assumed in the multimode waveguide model. This is physically attributed due to the presence of mine wall roughness and long-range tilt of the ore body [38]. In a very simple word a mine wall roughness can be defined as the variations in the surface level to the mean level of mine wall surface whereas a tilt of the ore body in the context of underground mine tunnels can be defined as the variations in angular range between the mine tunnel’s centre line and the mine tunnel walls due to the undulations of coal seams [39]. It is shown in Fig. 3(a) and (b). Fig. 3(a) is a plan

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at any coordinate in the underground mine tunnel space. It is shown later in the results and discussion section (Section 5) that how the predicted received power varies considering with and without loss due to side wall roughness and tilt of the ore body. Considering h as the root mean square roughness and is Gaussian distributed, h as the root mean square tilt in degree for underground mine galleries, and k as the wavelength of the operating frequency, then the loss due to surface roughness Lroughness ðdBÞand tilt of the ore body Ltilt ðdBÞ are given as [7]:

  1 1 2 Lroughness ¼ 4:343p2 h k þ 4 :z 4 2w 2h Ltilt ¼

4:343p2 h2 z k

ð9Þ

ð10Þ

Now, consider a transmitter Tx, transmitting at power Pt with gain in antenna Gt , then at any coordinate (x,y,z) the predicted received power at the receiver with antenna gain of Gr can be calculated as

Precv ðx; y; zÞ ¼ PðtransmitÞ Gt Gr

2 1X ðamn þjbmn Þ:z C m;n :Eeigen m;n ðx; yÞ:e E0 m;n ð11Þ

Therefore, total path loss in an undergorund mine tunnel space can be calculatd as,

PLtotal ðdBÞ ¼ PLmodal þ Lmine

Fig. 3. Illustration of surface roughness and long range tilt of the ore body in an underground mine tunnel.

view of the roughness and tilt and Fig. 3(b) is a digital illustration of the study area having surface roughness and long-range tilt of the ore body. Large et al. [7] performed theoretical analysis to understand the impact of typical features of underground mine galleries on wireless propagation behavior. Furthermore, the study highlighted the significance of these features on the wireless communication characteristics. However, the study was limited to the characterization of lower order modes only i.e. natural propagation. Moreover, the proposed model does not consider higher intensity propagation modes in the tunnel environment. The analysis of the impact of surface wall roughness considering the higher order modes of the signal propagation in coal mine tunnels is also discussed in [40,41]. Nevertheless, the experimental understandings of attenuation caused by the mine wall roughness on the received signal as a function of the distance between the TX and RX are not analyzed. Since researchers in the past attempted to analyze and report the significance of side wall roughness and tilt of the ore body, collective and combined analysis of these unique mine features on wireless signal characteristics in underground mines have not been studied well. In addition, experimental validations of such theoretical channel modeling approach will complement the reliability to such models. In this work, we address these research gaps. In particular, we consider the side wall roughness and long-range tilt of the ore body, which is a practical scenario from operational underground mines. Our proposed channel model characterizes both lower order and higher order EM waves. We combined this analysis with the multimode channel modeling approach and further validated the theoretical results with the experiments performed in real time operational underground mines. Therefore, the proposed channel model will be helpful to estimate the signal strength

ð12Þ

where Lmine ðdBÞ is additional path loss due to the unique features of mine tunnel and can be calculated using the Eqs. (9) and (10). Hence, Lmine ¼ Ltilt þ Lroughness and PLmodal ðdBÞ ¼ P transmit  Prec where Prec can be calculated using Eq. (11). 5. Results and discussions 5.1. Simulation analysis Based on the proposed channel model, we in this section present the simulation analysis and have shown the effects of longrange tilt variations and surface wall roughness on the radio propagation inside underground mine workings. The simulation parameters considered for this analysis is same as that of real operational underground mine tunnels, such as width, height, surface wall roughness (0.5 inch), antenna gains, and transmitter-receiver positions. For the real-time field measurements carried out in the Nandira colliery, and simulation analysis; the transmitter power was 0 dBm with an antenna gain of 2 dBi. The variations in the angular range that is tilt, h of the ore body were assumed 1 degree. For both the vertical and horizontal walls of the mine tunnel, the relative permittivity was considered 50 and the ra;v ;h was 0:001 S/m for the analysis. From Fig. 4, it was observed that the long-range tilt of the ore body due to undulation of coal seams and surface wall roughness affected the signal propagation significantly. The multimode waveguide model proposed in [12] is reflected and shown in Fig. 4(a). From Sub-Figures of 4(b)–(d) it can be seen that the typical features considered in this study is crucial and need to be analyzed properly for reliable communication system design for such confined environments. 5.2. Experimental measurements and model validations To determine the prediction accuracy of the EM field distribution by proposed channel model in an underground mine tunnel; appropriate propagation measurements were carried out in a coal

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Fig. 4. A collective analysis of the effetcs of wall roughness and tilt of the ore body on the multimode waveguide model.

mine of MCL. All measurements were performed using the TelosB motes with an omnidirectional antenna following vertical polarization pattern with a gain of 2 dB. As discussed in Section 3, the different measurement use cases were considered, and propagation data were collected in a coal mine of MCL. This section details the validation of the proposed multimode model for wireless communication networks in operational underground mine environments. Further, an analysis of maximum coverage ranges between two wireless sensor nodes is also discussed. A summary

of the statistical analysis of proposed channel model and experimental measurements is listed in Table 3. 5.2.1. Straight empty tunnel The tunnel features and description of this case are listed in Table 2. From Fig. 5, it is observed that the graph of the average received signal at the receiver and the predicted signal strength of the proposed model has almost same characteristics as a function of the distance between TX and RX. For this use case,

Table 3 Statistical analysis of experimental measurements and proposed model (experimental-theoretical). Measurement Case

Mean error (dBm)

Relative RMSE (%)

Std. Deviation of Error (dBm)

Proposed Model Accuracy (%)

Model Accuracy (%) of Sun et al. [12]

Straight tunnel Straight tunnel with belt conveyor Along the belt conveyor Near Face Across Curvature TX-RX on Sidewall and 1 m above from the floor TX-RX on Sidewall and 2 m above from the floor

0.866 0.3778 4.3325 1.9239 15.7259 4.4839 7.6276

5.39 5.24 8.64 9.01 21.68 9.28 10.63

3.49 3.72 3.13 5.95 5.39 6.49 3.30

94.60 94.75 91.35 90.98 78.31 90.71 89.37

34.50 34.30 49.98 32.42 26.85 40.40 36.15

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Fig. 5. Straight tunnnel.

the maximum communication range between the two WSN nodes were 132 m, and beyond that, the receiver stopped receiving data packets from the transmitter. However, slight variations in the values of the proposed model and experimental data are observed for a distance of 103–112 m. This small variation in values are caused due to the movement of miners in between the measurements. 5.2.2. Straight tunnel with running belt conveyor Compared to the graph of 5, the predicted signal strength and measurement values in Fig. 6 has the same behavior in the near region of the transmitter. However, a slight declined curve was observed in the graph for a distance of 36 m–48 m. It is worthy to mention here that the belt conveyor was running at the distance of 42 m from the transmitter position and worked as a source of noise added to the mine tunnel environment. Therefore noise generated by the running belt conveyor affected the signal propagation, which caused for poor signal strength compared to the normal straight tunnel scenario.

ities near the face [42]. Therefore, the safety of miners working in such extreme conditions is a prime concern. Hence, need to be studied. The face was comparatively short in height than the normal tunnel heights (straight tunnel/passageways). The height was 2.5 m only and had a random distribution of the roof bolting covering ceiling and side walls as well. There were metallic strips also at some locations which worked as a support system for side walls and roof. The TX location was a cross junction, and a belt conveyor was running at a distance of 20 m left to the direction of the transmitter in other passage. The belt conveyor was a continuous source of noise in the mine environment during measurements. The TX and RX were kept at tunnel center and 1.7 m above from the floor. The graph for the experimental results against the predicted values from the proposed model is plotted and can be seen in Fig. 7. From Fig. 7, considerable deviations in the predicted signal strength and the experimental results were observed near the transmitter. However, there is a good agreement for the proposed modified model and the experimental results in the far distance of the transmitter. The variations in the signal strength and reduced maximum communication range mainly influenced due to the following factors: 1. the tunnel height was comparatively low than the normal tunnel such as straight tunnel reported in the previous sub-section, 2. the location of the transmitter was a cross-junction which caused for significant multipath and 3. during initial measurements, a few miners were carrying materials using metallic bucket. This metallic bucket and miners movement could have caused for the shadowing of the radio signals and reduced intensity at the RX. Therefore, the intensity of higher order modes faded faster than the normal order modes, hence caused for a flat fading over the distance between TX and RX.

5.2.3. Near face The extraction area, i.e., the face of the mine workings is quite different than the normal passageways in terms of humidity, ventilation, temperature, hazardous gas concentration, poor lighting and available work space, etc. We considered to study the signal analysis in this area as well, because most of the miners working in a particular shift of operation are involved in different mining activ-

5.2.4. Effects of curvature on propagation An underground mine tunnel has cross-cuts, junctions, and multiple passes to join different working levels [43]. These caused for change in tunnel shapes such as sharp bends and curves [34]. Since the reliable coverage of wireless networks is crucial for such confined spaces; understandings of the signal behavior across the curve is significant. We, therefore carried out such study by placing the TX and RX at the tunnel center and 1.7 m above the floor in the curved tunnel with a sharp bend of 70 . The placement strategy of this use case was such that it covered both LOS and NLOS. The first 9 m from the transmitter was a case of LOS and after that it was NLOS. The measurement results and predicted values are plotted and depicted in Fig. 8. The signal strength at the receiver experienced downfalls because of the effects of cross-cuts and sharp turn which further

Fig. 6. Straight tunnel with running belt conveyor.

Fig. 7. Near face.

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than other measurement scenario except measurement performed in curved tunnel.

Fig. 8. Effects of curvature.

caused for NLOS. From the graph, it was observed that wider openings of junctions and cross-cuts have strong influence than the normal width such an example can be seen in Fig. 7. 5.2.5. Along the belt conveyor Underground mine tunnels are dynamic in nature and their topology changes over time. There are different mining instruments and infrastructure present inside the mine galleries. The noise produced by these instrument affects the signal propagation significantly [1]. We also performed signal behavior analysis in the noisy environment. The belt conveyor was used for the transportation of the extracted ore to the surface in the studied mine. It was a continuous source of noise in the mine tunnel. The belt conveyor is passing through the tunnels connecting different levels of the mining area and 1 m above from the floor. We placed the TX and RX 1 m away from the side walls along the width and 1.7 m above from the floor. The measurements were carried out along the belt conveyor. The signal strength and the fading behavior can be seen in the Fig. 9. Both the predicted values and the measurements have same characteristics as a function of the distance between the TX and RX. However, fluctuations in the measurement values were noticed because of the real-time environment than the simulated one. It was observed that the signal fading behavior for this use case is linearly correlated with the distance and follows a lognormal distribution phenomena. However, the maximum communication range for this use case was 51 m only which was far lower

Fig. 9. Noisy environment.

5.2.6. On the side walls of the mine tunnel In an underground mine, wireless communication networks have a range of applications such as monitoring of noxious gas concentration, two-way voice communication, tracking of mine personnel and equipment, and ventilation on demand, etc., [43,1]. It is an obvious research interest that where to mount the sensor for the reliable and optimized performance of wireless communication devices. This analysis helps to understand the maximum coverage ranges between two nodes and others. It is worthy to mention here that the node placement may also vary from application to application such as in some of the application like tracking and early warning systems the device may be used by the miners or mounted on the cap lamp. Also, a node may be mounted on the side walls for monitoring the hazardous gas concentrations. Given such interesting research, measurements were performed considering two use cases: 1. TX-RX mounted on the side walls and 1 m above from the floor and 2. TX-RX mounted on the side walls and 2 m above from the floor. The graphs for both the use cases are plotted in Fig. 10. It was observed that the signal strength for case 1 is better and has less fluctuations phenomena compared to the case 2, i.e., transmitter-receiver mounted 2 m above the floor. For case 2, a sharp fall in signal strength was observed due to the scattered waves caused by surface wall roughness and floor, hence caused for the reduced electric field of the direct component. Additionally, for both the cases, at 15-m distance, there was a

Fig. 10. Average received power of TX-RX on the side walls of the mine tunnel.

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man hole (used for taking shelter in case of wagon/vehicle movement) followed by irregular cross-sectional dimensions for up to 9 m, hence caused for the complete blind spot between the transmitter and receiver which resulted in a rapid signal loss at the receiver. 5.3. Analysis of packet reception rate In wireless networks, packet reception rate (PRR) can reflect the link quality of the network. However, it is reported that the PRR

9

has low correlation with the distance function over received signal strength (RSSI). Due to the consistency of the RSSI over distance, it is considered as an agile link estimator of the wireless networks [44]. Therefore, for our analysis RSSI is selected to estimate the link quality over the distance between transmitter and receiver. For every measurement case, 200 packets were transmitted at intervals of 100 ms, and their corresponding RSSI values were recorded. The packet consists of 140 bytes of which 90 bytes of payload, 24 bytes MAC header, 20 bytes for IP header and 6 bytes for physical header. The mean PRR was obtained for every experimental case as

Fig. 11. Experimental Analysis of Packet Loss Rate.

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a function of RSSI. Fig. 11 shows the PRR in terms of percentage as a function of RSSI and distance between the TX-RX. It was observed from the experiments that the PRR is reliably good for RSSI values equal to or larger than 82 dBm, whereas below this RSSI value the PRR decreases rapidly for every measurement cases. Therefore we conclude from these experiments that for underground mines, wireless link should have an RSSI equals to or larger than 82 dBm (for a transmitter transmitting at power 0 dBm) and can be considered as a good link which also agrees with the observations made and reported in reference [45]. It is to be noted that the reliable link may vary for the transmitter which transmits at a different power which has not been considered in our study. 5.4. Discussions Reliable operation of wireless communication and monitoring devices are a prime concern to achieve the objectives of work safety and enhanced mine productivity [46]. However, the unique set of challenges posed by the underground mine workings restricts the optimized performance of wireless communication devices. Channel modeling and experimental validations could be significantly helpful to bridge the gap between the understandings of the propagation characteristics of radio waves in underground mine tunnels and system design to be deployed in such hostile environments. We carried out extensive experiments considering different cases as discussed in previous sections and listed in Table 2. For every use case, measurements were performed till the RX stopped receiving data packets from the TX. Moreover, for every measurement case, 10 measurements were performed for each distance between the TX and RX and their corresponding average RSSI and PRR were recorded. The main idea of this analysis was to report the maximum communication ranges between two wireless sensor nodes (see Fig. 12) so that large-scale deployments and installation plan could be made accordingly. We further aim to understand the proposed model accuracy with respect to the experimental results from underground mines. To achieve this objective, we carried out statistical analysis (theoretical-experimental results) and computed different statistical values on theoretical and experimental data. Mean error, standard deviation of errors, relative mean square error (RMSE) is calculated based on the predicted theoretical values from the proposed model, and the experimental values against each deployment scenarios. Further RMSE value is used to compute the model accuracy and expressed in the percentage. The same procedure is followed to show the model accuracy proposed by Sun et al. [12] also. Table 3 presents the summary of the statistical analysis and other observations from experimental measurements compared to the proposed theoretical model.

Fig. 12. Summary of maximum communication ranges between two sensor nodes for different measurements.

We further have shown from the proposed channel model that how the unique features of operational underground mines significantly affected the signal propagation. The proposed model gives an analytical solution for both near and far field of source antenna. It was observed from Fig. 4 that the long-range tilt of the ore body affected the higher frequencies much more than the lower frequencies. However, the surface wall roughness has significant effects on the lower frequencies. From simulation analysis, it is also clear that the typical features of underground mines (long range tilt and side wall roughness) collectively affected the propagation characteristics of radio waves compared to normal tunnel scenario such as rail/road tunnels. The proposed modified multimode model is validated with the experimental results carried out in the operational coal mine. For the use case of straight tunnel, tunnel with running belt conveyor and near face; the proposed model have good agreement with the measurement values and is shown in Figs. 5–7 with model accuracy of 94.60%, 94.75% and 90.98% respectively. In addition, the proposed theoretical model predicts the received signal strength at any mine tunnel coordinate (X, Y, Z) for TX-RX placed on side walls and along the belt conveyor with a model accuracy of 90%. However, the proposed theoretical model fails to capture the same accuracy for the case when TX-RX were kept in a curved tunnel space. The model accuracy for such case was only 78.31% which is far lower compared to other measurement cases. It has been analyzed that the signal fading characteristics near sharp bends and when the receiver has NLOS scenario in the curved tunnel has a significant drop in the received signal strength as well the packet received (see Figs. 8 and 11(d)) at the RX. This may be because of the diffracted waves at the edges of the mine tunnel and also a severe loss in intensity of directly reflected component from the side walls. Hence, to characterize the signal behavior in the curved tunnels and near bends, it is suggested that additional propagation factor need to be considered. Additionally, in a noisy environment such as the use case of along the belt conveyor (see Fig. 9); it is very difficult to control and model actual noise impact on the signal propagation in simulation studies. Nevertheless, to model such scenario more systematic measurements will be required so that strong empirical understandings can be developed. It has been observed that the higher order modes near the transmitter fade fast than, the lower order modes. However, the signal fading behavior in the far region of the transmitter is slow due to the waveguide effects and lower order mode propagation. It was observed from the measurement campaign that the radio signal behavior near cross-junctions and cross-cuts is not the same as of normal straight tunnel (such as rail/road) scenarios. Therefore, our results suggest that the propagation behavior of radio waves in such case is different than the normal tunnel which was assumed to be same in reference [17]. Hence, extra attenuation in the RSSI was observed due to cross-junction and cross-cuts at the receiver. It can be seen by comparing the results of measurement cases straight tunnel, a tunnel with running belt conveyor, near face and effects of curvature on radio propagation. These observations further could be useful for reliable communication links in underground mine workings and analysis of number of nodes required to provide complete coverage in the desired locations. It is crucial to recognize the signal characteristics when the nodes are mounted on the side walls. As it is seen from Fig. 10 that the surface wall roughness and tunnel irregularities for both the cases exhibit more attenuation, hence caused for rapid fluctuations in the signal strength. This observation would be helpful to select the optimum antenna position of wireless communication devices to be used in the monitoring applications. It is also to be noted that for all other measurement cases except along the belt conveyor, TX-RX was placed at the tunnel center and 1.7 m above the tunnel floor. From PRR analysis it has been observed that for a reliable

A. Ranjan et al. / Measurement 149 (2020) 106980

wireless link in underground mine workings; RSSI values should have greater or equals to 82 dBm, transmitting at 0 dBm. However, the real-time scenario such as movement of miners, external noise, and effects of sharp bend caused for reduced PRR (up to 85– 90%) as seen in some cases. 6. Conclusion In this paper, we have proposed a multimode based channel model considering the typical features of operational underground mine tunnels, and further, detailed validations of the proposed model were also discussed considering different experimental cases. We considered the typical structures and infrastructures of the operational underground mine tunnels and performed a range of measurements to characterize the propagation behavior of wireless networks in underground mine tunnels. Based on the proposed channel model, extensive measurements, and validations, our analysis shows that: 1. An operational underground mine tunnel is quite different than the normal rail/road tunnels. Typical features (long range tilt of the ore body and surface wall roughness) of operational underground mine tunnels collectively affected the signal propagation significantly. Hence, it is crucial to consider these additional parameters while designing the communication systems for such confined spaces. 2. Due to the long-range tilt of the ore body and surface wall roughness of underground mine tunnel; the characteristics of EM wave in tunnel experienced significant fluctuation in the signal strength near the antenna due to the combination of higher order modes. However, the fall in the predicted received signal strength at the receiver is gradual in the far region of the transmitter. This is because the higher order modes in the tunnel space go through fast attenuation as a function of distance. 3. From simulation analysis, propagation control factor could be identified such as mine tunnel size, operating frequency, and antenna positions. It has been analyzed that attenuation in the signal strength is mostly affected by the tunnel geometry, and mine tunnel size whereas the electric field distribution among modes is mostly governed by the antenna positions. 4. Cross-cuts, wide openings at junctions caused for additional signal loss at the receiver compared to the normal mine tunnel scenarios. 5. For the case of propagation characteristics across the sharp bend and in the curved tunnel and noisy environment, the EM waves experienced severe loss. Hence, other propagation factors have to be studied for reliable coverage of wireless communication systems. It has also been analyzed that in the case of the noisy environment, it is difficult to have control over real situations. Hence, extensive empirical studies in such noisy mine environment would be helpful to understand the propagation characteristics of radio signals for sustained communication design. 6. From PRR analysis it has been observed that for a reliable wireless link in underground mine tunnels RSSI values should have greater or equals to 82 dBm, transmitting at 0 dBm. However, the real-time scenario such as movement of miners, external noise, and effects of sharp bend could also have some effects on the PRR(reduced up to 85–90 percent success rate) as seen in some cases.

Acknowledgment The authors would like to thank Er. M.K. Patra, Safety Officer, Er. Sunil Singh, Assistant Manager and the officials of Nandira Colliery

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