Towards the Internet of Underground Things: A Systematic Survey

02/11/2019 ∙ by Nasir Saeed, et al. ∙ 0

This paper provides recent advances in the area of Internet of Underground Things (IoUT) with emphasis on enabling communication technologies, networking issues, and localization techniques. IoUT is enabled by underground things (sensors), communication technology, and networking protocols. This new paradigm of IoUT facilitates the integration of sensing and communication in the underground environment for various industries such as oil and gas, agriculture, seismic mapping, and border monitoring. These applications require to gather relevant information from the deployed underground things. However, the harsh underground propagation environment including sand, rock, and watersheds do not allow the use of single communication technology for information transfer between the surface and the underground things. Therefore, various wireless and wired communication technologies are used for underground communication. The wireless technologies are based on acoustic waves, electromagnetic waves, magnetic induction and visible light communication while the wired technologies use coaxial cable and optical fibers. In this paper, state-of-art communication technologies are surveyed, and the respective networking and localization techniques for IoUT are presented. Moreover, the advances and applications of IoUT are also reported. Also, new research challenges for the design and implementation of IoUT are identified.

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I Introduction

The population of the world will increase by 31 % in 2050, and therefore will require more natural resources and food to survive. In the next three decades with such increase in population, 71 % more resources are required. This ever-increasing demand for resources needs novel technologies to improve the underground exploration for natural resources and to produce more crop. The subsurface environment and agricultural lands provide various natural resources such as earth minerals, fossil fuels, metal ores, groundwater, and food. To efficiently use all of these resources, Internet of Underground Things (IoUT) is an enabling technology which can provide smart oil and gas fields, smart agriculture fields, and smart seismic quality control. However, implementation of IoUT is a challenging task due to the harsh underground environment which requires low power and small size underground sensors, long-range communication technology, efficient networking solutions, and accurate localization techniques.

The research on channel characteristics for IoUTs is rich where the primary communication sources are electromagnetic waves (EM), acoustic waves, magnetic induction (MI), mud pulse telemetry, coaxial cable, and fiber optics. The significant difference between the in air internet of things and IoUT is the communication media where the sensors (underground things) are buried and communicate through the soil. Due to the heterogeneous nature of the soil which consists of sand, rock, and watersheds, communication through it is more challenging. Watersheds have a notoriously bad impact on EM waves-based underground communication systems (UGCSs) by limiting its transmission range. Among the other alternatives, acoustic waves-based UGCSs provide an extended transmission range but suffers from low data rates, i.e., in few bits per second. To improve the data rate, MI has been investigated in the past decade which can provide high-speed underground communication but has low transmission range and depends on the orientation of the transmitter and receiver coils. Mud pulse telemetry is also an enabling technology for data transmission from the down-hole to the surface which uses pressure pulses for information coding. The maximum data rate of 3-6 bits per second can be achieved with mud pulse telemetry which is low and needs to be further investigated for providing high bandwidth communication. Wired telemetry which includes coaxial cable and fiber optics is another alternative for UGCSs which provide data rate up to 57,600 bits per second at the expense of extra cost and complexity. Table I compares various communication technologies for IoUT.

Parameters EM Acoustic Mud pulse MI Wired
Transmission Range few meters In hundred of meters In hundred of meters In tens of meters In hundred of meters
Attenuation High High Medium Low Low
Interference High Medium Medium Low Low
Installation cost Medium Medium Low Medium Low
Data rate In tens of bps In tens of bps In tens of bps In Kbps In Mbps
Applications Agriculture, seismic exploration, and down-hole telemetry Seismic exploration, buried pipeline monitoring, and down-hole telemetry Down-hole telemetry Down-hole telemetry Down-hole telemetry and buried pipeline monitoring
TABLE I: Comparison of underground communication technologies for IoUT.

Fig. 1: Organization of the survey.

I-a Related Surveys

There are quite few survey articles published that cover various issues of IoUTs. For example, the work in [1] presents the EM waves based IoUTs for precise agriculture. Moreover, [1] also reviews the academic testbeds and commercial solutions for precise agriculture. In [2], the authors give an overview of MI-based underground wireless sensor networks and present challenges and applications. The authors in [3], present the recent advances and challenges for wireless sensor networks in the oil and gas industry. The contributions of this article relative to the existing literature on IoUT is summarized as follows:

  • Compared to existing papers for IoUTs, this paper provides a deeper understanding of the all relevant communication technologies, networking solutions, and localization techniques which can be used to implement various IoUT-based applications.

  • The existing surveys only presents the literature on EM and MI-based underground wireless communication networks. However, we also collect the research on acoustic, mud-pulse telemetry, visible light, and wired-based communication technologies for various IoUT applications.

  • We survey the key challenges to implement IoUT and explore the relationship between IoUT, big data analytics, cloud, and fog computing.

I-B Survey Organization

The main focus of the paper is to review channel modeling, networking, and localization methods for the current sensing and communication technologies used in IoUT. However, for some of the techniques such as acoustic, visible light communications, and mud-pulse telemetry, the networking, and localization problem is still an open research problem. Fig. 1 illustrate the organization of the survey. In Section II, we present the literature on EM-based communication and networking solutions for IoUTs. Sections III and IV cover acoustic and mud pulse-based IoUTs respectively. MI, visible light, and wired based solutions are presented in section V, VI, and VII respectively. Section VIII discusses the advances and challenges of IoUTs. Finally, section IX summarizes and concludes the survey.


Fig. 2: Applications of EM-based IoUT

Ii EM Waves for IoUT

EM waves are widely used for underground communication and sensing to enable various applications such as smart agriculture [4, 5, 6, 7], seismic exploration [8], and oil and gas reservoirs monitoring [9, 10, 11]. Fig. 2 shows the major applications of EM-based IoUT. In this section, we cover the literature on the channel modeling and networking for EM-based IoUTs.

Ref. Data rate Frequency range Issue addressed Applications Year
[12] - 1-10 MHz Propagation characteristics Seismic/Agriculture 1971
[13] - - Structure of soil effect on EM waves propagation Seismic/Agriculture 1973
[14] and [15] - 3-50 MHz Electrical characteristics of soil Seismic/Agriculture 1974 and 1976
[16] 1-100 bps - EM waves for borehole communications Oil and Gas 1990
[17] - 1-3 MHz Impact of soil and network parameters Agriculture 1990
[18] - 300-500 MHz Impact of soil type Agriculture 2009
[19] - - development of the path loss model Agriculture 2010
[20] - - Test-bed Agriculture 2010
[21] - 0.1-120 THz Channel model Oil and Gas 2012
[22, 23, 24] - below 500 KHz Propagation characteristics Agriculture 2012
[25] - 10-100 MHz Energy harvesting Seismic/Agriculture 2012
[26, 27, 28] - 433 MHz Propagation characteristics Agriculture 2014-2016
[29] 124 Mbps 433 MHz multi-carrier modulation for EM-based IoUT Agriculture 2017
[30] - 97-130 MHz Soil moisture sensing Agriculture 2018
TABLE II: Summary of channel modeling for EM-based IoUT.

Ii-a Channel Modeling

Channel modelling for EM-based underground communications dates back to the early 70’s of the century. James et. al investigated the propagation of EM signals through the earth surface where the frequency range of 1 to 10 MHz was experimentally tested in different types of soil [12]. The authors in [13]

further examined the impact of different layers of the soil on the propagation of EM signals where Fast Fourier Transform was used to find the reflection of the incident signals from a three-layered medium. Furthermore, Lytle

et. al measured the electrical characteristics of the earth medium for the propagation of underground EM signals [14]. The authors in [15] experimentally measured the conductivity of the earth surface at EM frequencies of 3 to 50 MHz at Yosemite national park. William et. al measured high-frequency electromagnetic radiations in the borehole by neglecting the reflections and refraction from the subsurface [31].

In [16], the authors examined EM waves for borehole communications where a data rate of 1 bps was achieved for average conductivity and without using the repeaters while with the use of repeaters, the data rate can reach up to 100 bps. In [17], the authors have shown the impact of soil properties, water content, network topology, and antenna type for the EM waves in the frequency range of 1 to 3 MHz for underground wireless sensor networks (UGWSNs). Consequently, in [18], experiments were conducted in subsoil and topsoil at 300-500 MHz frequencies for buried sensors. Furthermore, attenuation of EM signal for measurement while drilling (MWD) telemetry system was investigated in [32] where the maximum transmission range of 15,000 feet was achieved without using repeaters. Based on the Friis free space path loss model, the authors in [19] provided the formula for the received power in the soil medium as

(1)

where represents the transmit power, and are the transmit and receive antenna gains respectively, and is the path loss in soil medium. and are the free space and underground path loss respectively. Underground path loss is calculated by considering the EM waves propagation characteristics in soil such as operating wavelength and frequency, scattering, and delay distortion. Hence, , where and are attenuation‘s due to transmission loss and wavelength difference of EM signal in soil compared to air respectively. Therefore, is represented in as follows

(2)

where is the Euclidean distance, is the attenuation constant, and is the phase shift constant. Both the attenuation and phase shift constants depend on the dielectric properties of the soil. The dielectric properties of the soil are calculated by using the Peplinski principle as follows [33]:

(3)

where is the complex dielectric constant of soil and water mixture consisting of a real part and an imaginary part respectively. The real part of is given as

(4)

where is the bulk density, is density of solid soil, , is the water volume fraction, and is the empirically determined constants for soil type. The terms and represents mass fractions of sand and clay respectively and their values lies between 0 and 1. The effective conductivity in (4) is given as

(5)

where is the static dielectric constant, is the high frequency limit, is the relaxation time of water, and is the operating frequency [34]. Similarly the imaginary part , where . Consequently, the attenuation constant is given as

(6)

where is the magnetic permeability. Similarly, the phase shift constant is found as

(7)

It is clear from the expression of both the attenuation and phase shift constants that the propagation loss of EM depends on the operating frequency, soil composition, water content, and bulk density. Furthermore, the authors in [19] have investigated the path loss in the presence of two paths between the transmitter and the receiver. The authors have neglected the second path effect in high depth scenarios due to no reflection from the ground surface while for low depth scenario two-path model was considered which is given in as follows:

(8)

The term correspond to the second path loss given as

(9)

where and are the amplitude and phase reflection coefficients respectively, is the wavelength, and is the difference between the two paths. Based on the above channel model, the authors in [20] proposed a testbed for UGWSNs. The authors in [35] also compared the theoretical and measured results for UGWSNs where the above analytical model fits well within 3.45 dBm of the measured data.

In [21] EM waves in Terahertz band (0.1-120 THz) were investigated for oil reservoirs. Although the THz band provides high capacity for UGWSNs, their range is limited to few centimeters. Hence, the concept of low frequency (below 500 KHz) was introduced in [22, 23, 24] to achieve more considerable transmission distance (in tens of meters) for UGWSNs. The impact of the carrier frequency, transmission distance, depth, and modulation type was experimentally tested in [26, 27, 28] for UGWSNs. The optimum frequency range in 10-100 MHz was identified in [25] for energy harvesting in UGWSNs. To reduce the battery consumption and to improve the signal to noise ratio (SNR) at the receiver, a code division multiplexing scheme was proposed in [36]. Consequently, the impact of soil type on multi-carrier modulation was examined in [29] which showed that the data rate of 124 Mbps is achievable for the transmission distance of 12 m for IoUT. In [37], the authors used pulse amplitude modulation, quadrature phase shift keying, m-ary quadrature amplitude modulation, and Gaussian minimum shift keying for IoUT. Moreover, it was shown in [37] that adaptive equalization improve the performance of the underground channel. Furthermore, the authors in [30] tested 97-130 MHz EM frequencies for underground radio propagation; however, the error was almost 50 % at such high frequencies. In [38]

, a real-time soil moisture sensing and permittivity estimation system called Di-Sense was proposed to implement IoUT for agricultural applications. Software defines radio-based experiments was conducted where the permittivity and soil moisture were calculated at a depth of 4 cm and horizontal distance of 1 to 15 m, for the frequency range of 100-500 MHz.

Recently, the authors in [39] investigated the soil effects, the orientation of the buried antenna, and depth on the underground to the above ground wireless communication link. Consequently, empirical studies were conducted in [40] to show the propagation characteristics of the underground to above ground communication link at 2.4 GHz and 433 MHz respectively. Table II summarizes the literature on channel modeling for EM-based IoUT.


Fig. 3: Network model for EM-based IoUT for oil and gas reservoirs.

Fig. 4: Network model for EM-based IoUT for agricultural applications.

Ii-B Networking

The literature on channel model for EM-based IoUT is rich. However, few works exist on the routing protocols. In this section, we cover the existing research on networking layer protocols for EM-based IoUT. Fig. 3 and 4 shows examples of a multi-hop network for an EM-based oil and gas IoUT and agricultural IoUT respectively. Due to the limited transmission range of EM waves in the underground environment multi-hop communication is investigated. For example, the idea of dense sensor networks with multi-hop communication for oil and gas exploration was presented in [8]. Moreover, the authors in [41] developed a TCP/IP based simulator for the IoUT. Furthermore, they evaluated the performance of various multiple access schemes in [42]. The path connectivity problem for EM-based IoUT was investigated in [43]

which showed that low volumetric water content and low operating frequency lead to a higher probability of connectivity. In

[44], the throughput of EM-based IoUT was optimized to achieve the QoS requirement. Recently, the influence of soil texture, particle density, and bulk density on the hop count was examined for IoUT where the number of hops between a source and a sink increases with an increase in the water content and clay in the soil. A relay based approach with physical constraints on the relay location, propagation environment, and load balancing was examined in [45] to improve the lifetime of IoUT.

Iii Acoustic Waves for IoUT

Most of the communication and detection techniques for underground measurements are based on acoustic waves. Geologists use acoustic waves to look for underground resources such as oil and gas. Acoustic waves are transmitted into the ground, and the reflection is measured from the propagation of the acoustic waves. Moreover, acoustic waves are used in drilling to communicate with underground equipment. Fig. 5

shows the major applications of acoustic-based IoUT. The research work on acoustic-based underground communications systems is rich and can support various applications. Based on the signal generation, the acoustic-based methods can be broadly classified into passive and active type methods.


Fig. 5: Applications of acoustic-based IoUT.

In passive acoustic-based methods, the subsurface environment generates an acoustic signal such as infrasonic sound caused by natural events such as earthquakes, nuclear explosion, and volcanic explosions. In such circumstances, the sensors are placed in the vicinity of the event area. These sensors detect the infrasonic signals which help in the prediction of a natural disaster. Moreover, sudden changes underground such as rock crack formation, structural transformation, and pipeline leakage can also be detected by using passive acoustic methods. In active acoustic-based methods, the signal is generated by an artificial explosion or vibration which is sent underground to estimate the properties of the earth’s subsurface (see Fig. 6). The popular application of such method is reflection-based seismology.

Due to the low propagation speed of acoustic waves, they are mostly used for detection purposes in soil rather than for communication. In [46], the authors investigated the speed of sound in the soil. Acoustic signals were transmitted through different samples of soil and received by the hydrophones. The attenuation coefficients were calculated for the frequencies range of 2 to 6 KHz. The proposed empirical solution was able to detect an object buried at 40 cm. Similarly, in [47, 48], the soil moisture was measured by using the speed-moisture curves for underground acoustic signals transmission. Moreover, acoustic waves with a frequency of 900 HZ was used in [49] to estimate the moisture content of the soil. A universal soil loss equation was derived in [50] for acoustic waves propagation in soil at 16 KHz frequency. Recently, the authors in [51, 52] proposed an acoustic based wireless data transmission system (SoilComm) for IoUTs. SoilComm system was able to transmit the sensing data over 30 m distance through soil. Table III summarizes the literature on acoustic-based IoUT for agricultural applications.

Ref. Frequency range Issue addressed Applications Year
[46] 2-6 KHz Soil sampling Agriculture 2002
[47, 48] - Soil moisture detection Agriculture 2003-2004
[49] 900 Hz Soil moisture detection Agriculture 2010
[50] 16 KHz Universal soil loss equation Agriculture 2015
[51, 52] - Wireless data transmission in soil Agriculture 2018
TABLE III: Summary of acoustic-based IoUT for agricultural applications.

In addition to the investigation of soil properties, acoustic waves are widely used for down-hole telemetry purposes. In acoustic telemetry, the steel walls of the drill-string are used as a source of a communication channel. Acoustic-based telemetry system consists of a piezoelectric-electric transmitter underground, repeater at 500-2000 m apart, and a transceiver at the ground surface. Acoustic waves passing through the drill string are highly attenuated and therefore require a sufficient number of repeaters. Additionally, the drilling noise also affects the propagation of acoustic waves along the string. The authors in [53] were able to achieve the data rate of 20 bps at a depth of 3695 feet. In [54], field tests were performed by using acoustic telemetry where data rates of 20, 40, and 60 bps were achieved at a depth of 1000 m. In [55, 56], a testbed was developed to study the channel behavior for acoustic waves over the string pipes. The results in [55, 56] have shown that acoustic waves suffer from noticeable dispersion and pipe string acts as a frequency selective channel.

Authors in [57] also performed experiments by using acoustic waves for downhole communications where the data rate of 20 and 6 kbps were achieved for 4.5 and 55 m depth respectively. Acoustic waves were generated by using a magnetostrictive actuator which converts electrical signals into acoustic vibrations. The acoustic signals were then transmitted over the drill string to the bottom and received back at the surface by the geophones. For a frequency selective channel of the drill string, orthogonal frequency division multiplexing (OFDM) was used. The arrangements of pipes also play an important role in acoustic communication in drilling. Hence, the authors in [58] argued that the ascend-to-descend arrangement of pipes provide the best telemetry performance for downhole acoustic communication. The problems of acoustic noise and attenuation of the acoustic signal due to the pipes joints was studied in [59]. The authors proposed a single carrier with frequency domain equalization (SC-FDE) to improve the reliability of the acoustic transmission along the pipe strings. The impact of multiphase flow was examined in [60] for downhole acoustic communication with amplitude shift keying and frequency shift keying modulation schemes. Moreover, the authors in [61] introduced the use of trellis coded modulation for downhole acoustic communication where a more realistic model of 1000 m depth was considered with an achievable data rate of up to 400 bps. Recently, non-contiguous OFDM with adaptive pilot design was used in [62] to provide data rate of up to 500 bps at the depth of 53.76 m. Table IV summarizes the literature on acoustic-based IoUT for down-hole communications.

Ref. Data rate Depth Issue addressed Applications Year
[53] 20 bps 1120 m Down-hole communication Underground drilling 2006
[54] 20-60 bps 1000 m Field tests for down-hole communication Underground drilling 2007
[57] 6 and 20 kbps 55 and 4.5 m OFDM for down-hole communication Underground drilling 2013
[59] - - Impact of pipe joints on signal transmission Underground drilling 2013
[60] - - Impact of multi-phase flow with ASK and FSK Underground drilling 2014
[61] 400 bps 1000 m Trellis coded modulation for down-hole communication Underground drilling 2014
[62] 500 bps 53.76 m NC-OFDM for down-hole communication Underground drilling 2018
TABLE IV: Summary of acoustic-based IoUT for down-hole communication.

Fig. 6: Architecture of an active acoustic-based IoUT for seismic monitoring.

Moreover, in addition to the channel model for acoustic waves based underground communications, work on the transceivers design for such applications has also been an active area of research. For example, in [63], a novel receiving unit for acoustic communication along the drill string was proposed. Similarly, a tri-axial accelerometer was used in [64] to compare the single channel and multi-channel uphole acoustic communication in oil wells. Nevertheless, Gao et. al studied the transmission of acoustic waves along the drill strings for various applications [65]. Besides, the literature on transmission characteristics and transceiver design, studies on the characteristics of acoustic signals also exists for rock failure [66], cracks in pipelines [67, 68, 69], and landmines detection [70].

Iv Mud Pulse Telemetry for IoUT

The most common and mature method for downhole communication is mud pulse telemetry (MPT) which dates back to the mid of the century. The early MPT systems were able to only communicate the azimuth and inclination information for the wells navigation. The main concept of MPT lies in the circulation of the mud for the transmission of the data [71]. During the drilling process, the pumps at the surface circulate the mud down to the drill string through the pressure pulses [72, 73]. The mud is used to cool the downhole drill string components, carry information from the bottom to the surface, and balance the pressure. The mud passes through a valve which restricts and generates the pressure waves. The pressure pulses are controlled and are used to modulate, frequency, amplitude, and phase of the mud pulse signals [74]. Three different types of mud pulse signals are transmitted; i.e., positive, negative, and continuous wave pulses as shown in Fig. 7 [75]. The signal processing modules at the surface recognize these pressure pulses. The pressure pulse signals in MPT systems are encoded by various techniques to carry the critical information such as temperature, pressure, and conductivity etc. of the well. Although the MPT systems are mature, the mud pulse signals suffers from several impairments which are discussed in the following subsections.


Fig. 7: Three different types of MPT systems. (a) Positive pulses from blocking/unblocking of the fluid; (b) Negative pulses by pressure in the drill string; (c) Continuous pulses by using a rotor.

Iv-a Mud Pump Noise

During the down-link transmission of the mud pulse signal, the piston in the valve moves back and fro to generate the signal. At the same time the up-link signal is generated in the similar fashion in opposite direction resulting in the interference between the down-link and up-link signals [76]. Moreover, the pressure of the pump creates noticeable amount of frequency and amplitude varaitions in the frequency range of 1-20 Hz. To diminish these effects, MPT systems uses two different transducers at the surface receivers which are well spaced [77, 78]. In [79] and [80], the authors have used least mean square filtering algorithm to reduce the noise generated by the mud pumps.

Iv-B Attenuation and Dispersion

As the mud pulse signals propagate along the borehole, the signals are attenuated and dispersed due to under-balanced drilling mud [81]. The major sources of attenuation are the mud type, joints in the drill string, signal frequency, diameter of the string, and borehole depth [82]. Low frequency signals can be used to avoid excessive attenuation of the mud pulse signals [83].

Iv-C Rock Fragments and Gas Leakage

During the drilling process, rock particles and gas may enter the mud used for the pressure pulses which changes the density and compressibility of the mud [84]. These rock particles and gas reduce the transmission speed of the pressure pulse waves. Hence, it is essential to study the velocity continuity of the drilling mud because the gas leakage into the mud can lead to unstable drilling which can cause environmental pollution and potential loss of human lives [85].

V Magnetic Induction for IoUT

One of the major factors which limit the evolution of IoUT is the challenging underground environment. We have previously discussed that the heterogamous soil medium and water content of the soil limit the transmission range of EM-based IoUT [18]. Hence, magnetic induction (MI) has been introduced to overcome the limitations of EM waves for IoUT [86, 87]. In this section, we will cover multiple aspects of MI-based IoUT which include channel modeling, networking, and localization.

Fig. 8: MI communication link.

V-a Chanel Modeling

A time-varying magnetic field is used in MI-based IoUT for communication between a transmitting and a receiving node. The coil antenna of the transmitting node produces a time-varying magnetic field which induces the current at the coil antenna of the receiver. The design of a conventional MI-based transceiver is shown in Fig. 8 where is the distance between the coil and , and , are their radiuses respectively. The transmitting current with direct current and angular frequency induces current in the nearby coil. However, if the transmitting and receiving coils are not well coupled, a single coil may not guarantee communication, and therefore a tri-directional coil structure was proposed in [88] for efficient MI communication (see Fig 9). Based on the MI phenomena, the link budget for MI-communication at high frequency and with a large number of turns in the transmitter coil is given in [89, 86, 90, 88, 91] as

(10)

where represents the permeability of the soil, is the transmit power, is the number of turns in the receiver coil, is the angle between the axes of the two coils, and is the resistance of a unit length loop. The authors in [88] experimentally validated the link budget expression in (10).

Fig. 9: Tri-directional MI coils.

Based on the above channel model, different modulation schemes such as BPSK, QPSK, and QAM were proposed in [92] for MI-based underground communications. Consequently, a square wave with pulse code modulation was used in [93] to provide inductive power transfer and average data rate communication for MI-based IoUT. Authors in [94] were able to determine the size of the antenna and the number of turns in the coil for MI-based low power and low-frequency underground communications. Furthermore, they studied the impact of soil conductivity on the MI-based underground communication links [95]. In [96], the soil path attenuation model with the best operating frequency range identification was presented for MI-based IoUT. A pulse power method (use of relay coils) was used in [97] to improve the transmission range of underground MI-based communications. Recently, the authors in [98] suggested meta-material based MI coils for long-range subsurface communications. Authors in [99, 100] evaluated the performance of IoUT for sandy and stone type of media where it was shown that the receiver sensitivity should be -70 dBm. Table V summarizes the literature on the various physical layer issues of MI-based IoUT.

Ref. Issue addressed
[89, 86, 90, 88, 91] Path loss modeling
[92] BPSK, QPSK, and QAM for the underground MI links
[93] PCM for the underground MI link
[94] Study of the coil design parameters
[95] Impact of soil conductivity on the underground MI links
[96] Soil path attenuation model and best frequency selection
[97] Improving transmission range by using relays
[98] Meta-material for coil design to improve transmission range
[99, 100] To study the impact of different medium on the MI link
TABLE V: Various physical layer issues addressed in the literature for MI-based IoUT.

V-B Networking

Although the MI-based techniques address the issue of the dynamic underground channel model, the transmission distance is lower for practical use. In practical applications, the transmission distance of MI-based IoUT is improved by using the relay coils [101, 102, 103, 104]. The typical MI-based IoUT network consists of buried sensors (underground things) and aboveground equipment (surface things) as shown in Fig. 10. Hydraulic fracturing is used to inject the underground things into the well bottom or the reservoir [105, 106]. The surface things can provide extended MI communication link by using large dipole antennas and large transmission power [105]. Therefore, the downlink communication channel is assumed to be single hop while the up-link communication channel is multi-hop due to the limited transmission range of underground things [107]. The surface things can also work as anchors for the localization purpose. In [108, 109, 110], the network capacity of a multi-hop MI-based IoUT was evaluated. The results in [108, 109] suggested that the use of relays and optimizing the orientation of the coils improve the throughput of a multi-hop MI-based IoUT network. A distributed environment aware cross-layer protocol (DEAP) was proposed in [111] to satisfy the quality of service (QoS) requirement, achieve higher throughput, and reduce energy consumption. A two-stage cross-layer protocol called Xlayer wasn proposed in [112] for multi-hop MI-based IoUT to guarantee the QoS requirement. Xlayer protocol was able to achieve high throughput, low delay, and low energy consumption. A full-duplex meta-material enabled MI-based communication was proposed in [113] to reduce the transmission delay for multi-hop underground communication.

Ref. Issue addressed
[101, 102, 103, 104] Range enhancement by using relays
[107] multi-hop MI-based underground communication
[108, 109, 110] Throughput analysis of Multi-hop MI-based underground communication
[111] Throughput and energy consumption analysis
[112] Throughput, delay, and energy consumption analysis
[113] Range enhancement by using meta-material based relay coils
[114] Connectivity analysis of multi-hop MI-based IoUT
TABLE VI: Various network layer issues addressed in the literature for MI-based IoUT.

Fig. 10: Network model for MI-based IoUT for oil and gas reservoirs.

Recently, connectivity analysis for IoUT was provided in [114] where the probability of a connected network increases with an increase in the number of underground things and low volumetric water content. Additionally, the connectivity performance of the EM and MI-based IoUT was compared where the results have shown that for a given environmental and network settings MI perform better than EM waves. Table VI presents the literature on various network layer issues for MI-based IoUT.

V-C Localization

Localization is an essential task in wireless networks which enable various location-based services. Hence, localization techniques for the terrestrial and underwater wireless communication networks are well investigated in the past. For example in [115] the authors reviewed various localization techniques for terrestrial wireless networks. Similarly, in [116] localization techniques for marine networks are studied. The localization techniques can be classified based on the ranging technique (range-based/range-free), type of computation (centralized/distributed), and space (2D/3D). However, the literature on localization techniques for the underground wireless networks is limited due to various challenges such as harsh and light-less underground environment, non-availability of global positioning system (GPS) signals, high attenuation, and narrow operational area. Although efforts have been made in the past to develop localization techniques for harsh environments such as indoor and underwater, however, the underground environment does not support the use of these communication technologies, and therefore the localization techniques for the indoor and marine environment cannot be directly applied to the underground case [2].

Accordingly, a two-dimensional (2D) localization technique was developed by Andrew et. al in [117] by using magnetic-induction to track animals underground. Furthermore, they extended their 2D tracking system to a three-dimensional (3D) one in [118]. Moreover, they used the 3D MI-based tracking model for underground rescue operations [119]. As the propagation of MI signals is highly affected by the soil medium, therefore, the impact of minerals and rocks on the localization accuracy was studied in [120]. It was observed in [120] that the skin effect of the underground medium is almost negligible at very low frequencies whereas the localization accuracy depends on the attenuation properties of various underground materials. Recently, simulated annealing wasn used for MI-based terrestrial and underground wireless networks in [121, 122] which can achieve sub-meter level accuracy.

Fig. 11: CRLB vs. Number of turns in the receiver.
Fig. 12: CRLB vs. Coil size.

Furthermore, a modified semidefinite programming-based relaxation technique was used in [123] to determine the position of the underground sensors. A single anchor was used in [124]

to find the location of all other underground sensors in 3D for MI-based IoUT. Trilateration, machine learning, and hybrid passive localization techniques were used in

[125] to estimate the position of a target node in 2D MI-based IoUT. Recently, an analytical model has been presented in [126] for distance estimation in MI-based IoUT. However, none of the above works analyze the achievable accuracy of localization for MI-based 3D IoUT which is an important and challenging task. The achievable accuracy of localization techniques is characterized by estimation bounds such as the Cramer Rao lower bound (CRLB). In the past, CRLB have been derived for various wireless networks such as internet of things (IoT) [127], vehicular ad-hoc networks [128], source localization [129], radar tracking [130], cognitive radio networks [131, 132, 133], and underwater wireless networks [134, 135, 136]. The CRLB analysis mainly depends on the ranging technique and network parameters such as the number of anchors, network type (multi-hop or single hop), the density of the network, and transmission range [137, 138]. These findings have led to the development of robust and accurate localization algorithms. Hence CRLB analysis is an attractive tool for localization systems due to its simplicity and generic expressions. Therefore, in [139], we derived the expression of the CRLB for the MI-based IoUT localization. The derived bound in [139] takes into account the channel and network parameters of MI-based IoUT.

Fig. 13: CRLB vs. Transmit power.

Consequently, we used the simulation parameters from [123] to investigate the impact of various channel and network parameters on the CRLB. Fig. 11

shows the impact of the number of turns in the MI coil and the noise variance on the achievable accuracy. The values of the noise variance are kept as 0.05, 0.3, and 0.7 m respectively whereas the frequency is 13 MHz. Fig.

11 suggests that to get better localization accuracy more number of turns in the coil are required however increasing the number of turns may increase the size of the coil. However, the harsh underground environment requires a small size of the MI coil. Hence, the impact of coil size is examined in Fig. 12 which suggests that increasing the size of the coil improve the accuracy. Thus, there is a trade-off between the size of the coil and the localization accuracy which should be taken into account before the deployment.

Moreover, we have also tested the impact of transmission power on the achievable localization accuracy in Fig. 13. Commercially available MI coil have transmission power in the range of 100 – 200 mW (20-23 dBm). Thus we kept the transmit power in the range of 20-23 dBm with the variable size of the coils. Fig. 13 shows that with the increase in transmit power the achievable accuracy improves. Accordingly, the above results suggest that the achievable accuracy of any localization algorithm for MI-based IoUT is the function of the number of turns in the MI coil, noise variance, size of the coil, transmit power, frequency, and the number of anchors. Therefore, all these parameters are important to design a robust and accurate localization technique for IoUT.

V-D Charging of the MI Coils

Lifetime is an important parameter for IoUT due to the harsh underground environment. Therefore, research efforts have been made to improve the lifetime of the IoUT [140]. A charging method for IoUT has been proposed in [140] where a virtual magnetic relay network and optimized routing protocol was used to reduce the energy consumption. However, the charging efficiency of the proposed system in [140] remains very low for even moderate size of coils. Consequently, in [141] an optimized energy model framework was proposed for linear topology. The problem of charging underground coils is still open research problem and other options such as energy harvesting can be investigated.


Fig. 14: Fiber optic monitoring system for IoUT.

Fig. 15: VLC based IoUT for gas pipelines.

Vi Wired Communications for IoUT

Wireless communication channels reduce the complexity and cost of underground monitoring. However, existing wireless technologies fail to provide timely, reliable and accurate solutions, especially for the deep underground monitoring. Hence, wired technologies such as coaxial cable and optical fiber are used for down-hole monitoring [142, 143, 144, 145, 146]. Since for higher data rates, optical fiber has replaced co-axial based wired communication. In this section, we cover the literature on optical fiber-based communication systems for underground applications.

Fiber optic sensing technologies have been applied to several commercial and industrial application in the past two decades. These sensing technologies can provide both sensing and information transfer in the harsh environment. Due to these attributes, they are well suited for the harsh environment in oil and gas reservoirs [147]. The fiber optic based underground sensing system consists of fiber Bragg grating (FBG) sensors which are connected to the optical fiber cable with the help of ultraviolet photo-inscription method [148] (see Fig. 14). The authors in [149] proposed an FBG based real-time temperature and fluid monitoring system of oil bore-holes. In [150], Yan et. al designed FBG based seismic geophone for oilfield exploration which has shown better sensitivity than the conventional geophones for 10-70 Hz range of frequencies. Wavelength division multiplexing was used to combine the information from two fiber onto a single fiber in [151] for the temperature and pressure sensing in a well-bore. Recently, the authors in [152] installed an optical fiber-based down-hole monitoring system at the shoreline of Marmara sea in Turkey to provide geophysical observations. In short, optical fiber based underground monitoring systems provide high-speed communication and are immune to electromagnetic interference which mainly depends on the development of fiber grating sensors [153].

Vii Visible light communications for IoUT

Light cannot pass through soil therefore visible light communication (VLC) can only be used for down-hole monitoring in gas fields. To the best of the author’s knowledge, the only VLC-based down-hole monitoring systems were proposed in [154, 155]. In [154], light emitting diodes (LEDs) were used at the bottom of the pipeline, and a single photon avalanche diode (SPAD) was used as a receiver at the surface (see Fig. 15). The proposed system in [154] was able to achieve the data rate of 1-5 Kbps for the depth of 4000 m. However, the authors in [154] assume empty pipeline which is not realistic. Hence, in [155] the authors provided a channel model for VLC-based underground gas pipelines in the presence of methane gas. Different pulse amplitude modulation (PAM) schemes were tested, and the results have shown that for a target BER of , 8-PAM can reach the target distance of 22 m. Similarly, higher order PAM provides a better data rate but reduced transmission range with a minimum of 3.82 m for 512-PAM. Nevertheless, the research on VLC-based IoUT is in its infancy and need to be examined in the future.

Viii Future Research Challenges

The recent advances in IoUT have broadened the scope of this research area. Hence, in this section, we provide various new challenges for the IoUT. Table VII presents the significance of each research challenge for a specific underground application.

Research Challenge Agriculture Seismic exploration Oil & Gas
Deployment Medium High High
Channel modeling Medium Medium High
Transmission range Low High Medium
Latency Low Low Medium
Reliability Low Medium High
Security Medium High High
Scalability Low Medium Medium
Robustness Low Medium High
Networking High Medium Medium
Cloud computing High Medium Low
Fog computing Low Medium High
Localization Medium High Medium
TABLE VII: Significance of each research challenge for various applications.

Viii-a Deployment

The deployment of smart objects for IoUT in the harsh underground environment is a challenging issue [2]. Installation and management of smart objects underground are much more difficult compared to the terrestrial networks. Moreover, the underground objects can be easily damaged during the digging process. Hence, efficient deployment of the smart objects is required to minimize the installation cost of the IoUT. For example, a smart object with high energy requirement should be deployed near the surface for ease of management as the replacement of batteries in the underground environment is challenging. Moreover, to avoid the replacement of batteries, a battery with high capacity should be used, and power saving protocols should be applied. Unfortunately, the research work on the efficient deployment of IoUT is limited, which takes into account the various system and network parameters such as deployment depth, number of smart objects, lifetime, and routing.

Viii-B Channel Modeling

In terrestrial communications, the strength of the EM signal decays with the square of the distance while in the soil, the decay is much faster due to attenuation from the soil medium [18]. The major loss factors for a given frequency in the soil are the permittivity and conductivity of the type of soil. Hence, MI-based communication has been introduced for the IoUT which is based on magnetic field propagation [88]. Although the path loss for each type of communication channel has been extensively analyzed in the past, unfortunately, few efforts have been made to provide a fully functional MI-based IoUT with practical signal transmission schemes to verify the channel models. Nevertheless, the demanding applications of the underground stimulate the research in this direction.

Viii-C Limited Transmission Range

The MI technology has certain advantages such as prone to multi-path fading and boundary effects which makes it ideal for the communication and localization in the underground environment [88]. However, MI technology also has some disadvantages; the most significant one is the limited transmission range due to high path loss in the soil. Although in few works large coils with high transmission power was used for the long-range downlink communication and localization [123], however, it may not be a practical solution. Hence, the limited transmission range of buried smart objects in IoUT is a significant issue which is still an open research problem.

Viii-D Low Latency and Reliable Communications

Most of the applications of IoUT require low latency and reliable communication. For example, the underground things are deployed in the oil and gas wells to perform critical sensing tasks such as temperature/pressure, pipe leaks and gas leaks, and therefore the data should be reliable and receive at the surface with low latency. If the sensed data from even a single sensor is not received on time, it can lead to a disaster. As the performance of the IoUT is highly susceptible to the harsh underground environment, it can lead to unreliable and high latency communication. Hence, a reliable and low latency architecture is required for IoUT which can count for sensor failures and minimize the transmission delay.

Viii-E Security

Although efforts have been made in the past to model the channel for IoUT [18, 88], unfortunately, security for IoUT did not get much attention from the research community. Security of IoUT includes the security of equipment and the communication protocols. Various attacks such as node replication, signal jamming, and wormhole can be launched to destabilize the operation of the IoUT. The security breach can also exhaust the network resources by triggering false alarms and responding to the false alarms. Therefore, security is of utmost importance for the stable IoUT and needs the attention of the researchers.

Viii-F Scalability

Routing overhead, higher network density, and node failures can cause scalability issue for IoUT. Moreover, the high energy consumption and limited memory of underground sensor nodes limit the scalability of the network. Additionally, the IoUT system may consist of sensor nodes developed by different vendors which can lead to interoperability issues. Hence, IoUT requires the development of self-healing and self-organizing techniques to overcome the scalability issue.

Viii-G Robustness

Robustness is another crucial issue for IoUT which has not been addressed in the past. In an underground resource-constrained environment where there are various challenges such as dynamic topology, an energy constraint, and sparse nodes, robustness is critical. Additionally, the localization techniques developed for IoUT should be robust to the noise since it can lead to large localization error.

Viii-H Hybrid Sensing

A hybrid sensing system for IoUT integrates signals from multiple types of sensor systems for the detection and localization of an event. For example, a network of long-term underground fiber sensors can be fused with short-term ground penetrating radars for the detection and localization of an underground event. Hence, the hybrid sensing technologies and new concepts, such as crowd-sensing can proactively detect and localize underground events.

Viii-I Software Defined Networking

Although the research on IoUT is still in its infancy, researchers are already looking to develop networking solutions for the smart objects connected underground. Software-defined networking (SDN) seems to be rather appropriate networking solution for the IoUT [156]. By employing SDN for the IoUT reduces the network complexity, improve load balancing, provide congestion control, efficiently utilize the network resources, improve the network lifetime, and reduce the latency. Hence with all these advantages, the SDN paradigm needs to be examined for the IoUT.

Viii-J Big Data

IoUT is going to generate a large amount of exploration data which include data coming from various applications such as agriculture, seismic surveying, and oil/gas fields. Hence this large of data need to be organized for a proper analysis, a metric calculation, or an event correlation to make accurate decisions [157]. Therefore, proper data analytics tools need to be developed to support the large amount of data produced in IoUT.

Viii-K Cloud and Fog Computing

The real-time and localized operations can be enabled for IoUT by integrating it with cloud and fog computing. Moreover, cloud/fog computing can provide various services for IoUT such as scalability, location awareness, low latency, and mobility. In the recent past cloud computing has been used to provide maintenance for the oil and gas industries while fog computing has been used to reduce the data traffic and provide the analysis of the data at the network edge [158]. For example, in time-critical applications by the time the data reach to the cloud for analysis the opportunity to make a decision might be gone. Hence, fog computing techniques should be integrated with the IoUT to support the in-time decision.

Viii-L Robust and Accurate Localization Methods

Localization for IoUT enables numerous applications such as geo-tagged sensing data, monitoring of the underground environment, and optimized fracturing. Few efforts have been made in the past to find the location of buried underground objects for MI-based IoUT [118, 120, 123, 121, 139]. However, localization techniques for IoUT systems based on the EM and acoustic waves do not exist. Therefore, robust and accurate three-dimensional methods need to be developed to enable the applications as mentioned earlier.

Ix Summary and Conclusions

In this article, we have surveyed various communication, networking, and localization techniques for the internet of underground things (IoUT). In section II, we have presented the literature on EM waves-based IoUT where we have briefly discussed the channel model and networking solutions. The primary applications of EM waves based IoUT are in agriculture due to their low penetration depth in the soil. In section III, acoustic waves-based solutions are presented for underground communications. Due to their low frequency, acoustic waves are mostly used for seismic exploration and down-hole communication during drilling. We have gathered the acoustic-based underground communication techniques in Table III and IV, where we have briefly stated their applications. Nevertheless, in section IV we have presented a well-known mud pulse telemetry system which is commercially used for down-hole communication but suffers from various impediments such as attenuation, dispersion, mud pump noise, and gas leakage. Furthermore, the most recent technology for IoUT, i.e., magnetic induction is presented in section V and various issues addressed in literature are stated in Table V and VI. Moreover, high speed wired solutions such as coaxial cable and optical fiber for the IoUT are presented in section VI. In section VIII, visible light communication-based IoUT are briefly discussed which did not get much attention of the researchers yet. Finally, we have presented various future research challenges which can be investigated by the researchers. In short, the research on communication, networking, and localization for IoUT still have a long way to go and require the attention of both academia and industry.

References

  • [1] M. C. Vuran, A. Salam, R. Wong, and S. Irmak, “Internet of underground things in precision agriculture: Architecture and technology aspects,” Ad Hoc Networks, vol. 81, pp. 160 – 173, 2018.
  • [2] S. Kisseleff, I. F. Akyildiz, and W. H. Gerstacker, “Survey on advances in magnetic induction based wireless underground sensor networks,” IEEE Internet of Things J., pp. 1–18, Sep. 2018.
  • [3] M. Y. Aalsalem, W. Z. Khan, W. Gharibi, M. K. Khan, and Q. Arshad, “Wireless sensor networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges,” J. of Network and Computer Applications, vol. 113, pp. 87 – 97, 2018.
  • [4] X. Dong, M. C. Vuran, and S. Irmak, “Autonomous precision agriculture through integration of wireless underground sensor networks with center pivot irrigation systems,” Ad Hoc Networks, vol. 11, no. 7, pp. 1975 – 1987, 2013.
  • [5] X. Yu, W. Han, and Z. Zhang, “Path loss estimation for wireless underground sensor network in agricultural application,” Agricultural Research, vol. 6, no. 1, pp. 97–102, Mar 2017.
  • [6] F. Liedmann and C. Wietfeld, “SoMoS-A multidimensional radio field based soil moisture sensing system,” in IEEE Sensors, Oct. 2017, pp. 1–3.
  • [7] F. Liedmann, C. Holewa, and C. Wietfeld, “The radio field as a sensor- A segmentation based soil moisture sensing approach,” in IEEE Sensors Applications Symposium (SAS), Mar. 2018, pp. 1–6.
  • [8] S. Savazzi, U. Spagnolini, L. Goratti, D. Molteni, M. Latva-Aho, and M. Nicoli, “Ultra-wide band sensor networks in oil and gas explorations,” IEEE Commun. Mag., vol. 51, no. 4, pp. 150–160, 2013.
  • [9] N. G. Franconi, A. P. Bunger, E. Sejdić, and M. H. Mickle, “Wireless communication in oil and gas wells,” Energy Techno., vol. 2, no. 12, pp. 996–1005, 2014.
  • [10] M. Akkas, R. Sokullu, and A. Balci, “Wireless sensor networks in oil pipeline systems using electromagnetic waves,” in 9th Int. Conf. on Electrical and Electronics Engineering, 2016, pp. 143–147.
  • [11] R. Karli, A. Bouchalkha, and K. Alhammadi, “Investigation of electromagnetic (EM) wave attenuation in oil pipeline,” in Int. Conf. on Electrical and Comput. Techno. and Apps., (ICECTA), Nov. 2017, pp. 1–4.
  • [12] J. Wait and J. Fuller, “On radio propagation through earth,” IEEE Trans. on Antennas and Propag., vol. 19, no. 6, pp. 796–798, Nov. 1971.
  • [13] K. Sivaprasad and K. C. Stotz, “Reflection of electromagnetic pulses from a multilayered medium,” IEEE Trans. on Geoscience Electronics, vol. 11, no. 3, pp. 161–164, Jul. 1973.
  • [14] R. J. Lytle, “Measurement of earth medium electrical characteristics: Techniques, Results, and Applications,” IEEE Trans. on Geoscience Electronics, vol. 12, no. 3, pp. 81–101, Jul. 1974.
  • [15] R. J. Lytle and D. L. Lager, “The Yosemite experiments: HF propagation through rock,” Radio Science, vol. 11, no. 4, pp. 245–252, Apr. 1976.
  • [16] W. Harrison, R. Mazza, L. Rubin, A. Yost et al., “Air-drilling, electromagnetic, MWD system development,” in SPE/IADC Drilling Conference, 1990.
  • [17] Z. Zheng and S. Hu, “Research challenges involving cross-layered communication protocol design for underground WSNS,” in 2008 2nd International Conference on Anti-counterfeiting, Security and Identification, Aug. 2008, pp. 120–123.
  • [18] A. R. Silva and M. C. Vuran, “Empirical evaluation of wireless underground-to-underground communication in wireless underground sensor networks,” in Distributed Computing in Sensor Systems, B. Krishnamachari, S. Suri, W. Heinzelman, and U. Mitra, Eds., 2009, pp. 231–244.
  • [19] M. C. Vuran and I. F. Akyildiz, “Channel model and analysis for wireless underground sensor networks in soil medium,” Physical Commun., vol. 3, no. 4, pp. 245 – 254, 2010.
  • [20] A. R. Silva and M. C. Vuran, “Development of a testbed for wireless underground sensor networks,” EURASIP J. on Wireless Commun. and Networking, vol. 2010, no. 1, pp. 62–74, Jan 2010.
  • [21] M. Akkas, I. Akyildiz, and R. Sokullu, “Terahertz channel modeling of underground sensor networks in oil reservoirs,” in IEEE Global Telecommun. Conf., 2012, pp. 543–548.
  • [22] S.-U. Yoon, E. Ghazanfari, L. Cheng, S. Pamukcu, and M. T. Suleiman, “Subsurface event detection and classification using wireless signal networks,” Sensors, vol. 12, no. 11, pp. 14 862–14 886, 2012.
  • [23] S. Yoon, L. Cheng, E. Ghazanfari, Z. Wang, X. Zhang, S. Pamukcu, and M. T. Suleiman, “Subsurface monitoring using low frequency wireless signal networks,” in IEEE Int. Conf. on Pervasive Comput. and Commun. Works., Mar. 2012, pp. 443–446.
  • [24] E. Ghazanfari, S. Pamukcu, S.-U. Yoon, M. T. Suleiman, and L. Cheng, “Geotechnical sensing using electromagnetic attenuation between radio transceivers,” Smart Materials and Structures, vol. 21, no. 12, pp. 1–17, 2012.
  • [25] S. Jiang, S. V. Georgakopoulos, and O. Jonah, “RF power harvesting for underground sensors,” in Proc. of the IEEE Int. Symp. on Antennas and Propagation, Jul. 2012, pp. 1–2.
  • [26] R. Goyal, R. Kennedy, B. Kelsey, M. Whelan, and K. Janoyan, “Underground wireless sensor networks using 2nd generation RF transceivers,” in Geotechnical Special Publication, no. 234, 2014, pp. 2619–2629.
  • [27] X. Yu, W. Han, P. Wu, and Z. Zhang, “Experiment of propagation characteristics based on different frequency channels of wireless underground sensor network in soil,” Trans. of the Chinese Soc. for Agricultural Machinery, vol. 46, no. 4, pp. 252–260 and 218, 2015.
  • [28] G. Horvat, D. Vinko, and J. Vlaovic, “Impact of propagation medium on link quality for underwater and underground sensors,” in Int. Convention on Information and Commun. Techno., Electronics and Microelectronics (MIPRO), May 2016, pp. 129–134.
  • [29] A. Salam and M. C. Vuran, “Impacts of soil type and moisture on the capacity of multi-carrier modulation in internet of underground things,” in Int. Conf. on Computer Commun. and Networks, (ICCCN), Aug. 2016, pp. 1–9.
  • [30] S. Suherman, A. Rambe, and A. Tanjung, “Underground radio propagation on frequency band 97 MHz - 130 MHz,” Int. J. of Engineering and Techno., vol. 7, no. 3, pp. 722–726, 2018.
  • [31] W. Daily, “A new method for characterization of downhole antennas used in geophysical probing,” Geophysical Research Letters, vol. 9, no. 5, pp. 507–509, 1982.
  • [32] J. Schnitger and J. D. Macpherson, “Signal attenuation for electromagnetic telemetry systems,” in SPE/IADC Drilling Conference and Exhibition, 2009, pp. 1–5.
  • [33] N. R. Peplinski, F. T. Ulaby, and M. C. Dobson, “Dielectric properties of soils in the 0.3-1.3-GHz range,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 3, pp. 803–807, May 1995.
  • [34] M. T. Hallikainen, F. T. Ulaby, M. C. Dobson, M. A. El-rayes, and L. Wu, “Microwave dielectric behavior of wet soil-part 1: Empirical models and experimental observations,” IEEE Trans. Geosci. Remote Sens., vol. 23, no. 1, pp. 25–34, Jan. 1985.
  • [35] S. Yoon, L. Cheng, E. Ghazanfari, S. Pamukcu, and M. T. Suleiman, “A radio propagation model for wireless underground sensor networks,” in IEEE Global Telecommun. Conf., Dec. 2011, pp. 1–5.
  • [36] H. Koike and Y. Kamiya, “A new approach for subsurface wireless sensor networks,” in Intelligent Interactive Multimedia Systems and Services 2016, G. D. Pietro, L. Gallo, R. J. Howlett, and L. C. Jain, Eds.   Cham: Springer International Publishing, 2016, pp. 201–211.
  • [37] A. Salam and M. C. Vuran, “Wireless underground channel diversity reception with multiple antennas for internet of underground things,” in IEEE Int. Conf. on Commun., (ICC), May 2017, pp. 1–7.
  • [38] A. Salam, M. C. Vuran, and S. Irmak, “Di-Sense: In situ real-time permittivity estimation and soil moisture sensing using wireless underground communications,” Computer Networks, 2019.
  • [39] H. Zemmour, G. Baudoin, and A. Diet, “Soil effects on the underground-to-aboveground communication link in ultra wideband wireless underground sensor networks,” IEEE Antennas and Wireless Propag. Lett., vol. 16, pp. 218–221, May 2017.
  • [40] D. Du, H. Zhang, J. Yang, and P. Yang, “Propagation characteristics of the underground-to-aboveground communication link about 2.4GHz and 433MHz radio wave: An empirical study in the pine forest of Guizhou province,” in IEEE Int. Conf. on Computer and Commun., (ICCC), Dec. 2017, pp. 1041–1045.
  • [41] S. Conceicao, F. Ribeiro, R. Campos, and M. Ricardo, “A NS-3 based simulator of TCP/IP wireless underground networks,” in IFIP Wireless Days (WD), Nov. 2014, pp. 1–6.
  • [42] S. Conceicao, C. Pendão, A. Moreira, and M. Ricardo, “Evaluation of medium access and a positioning system in wireless underground sensor networks,” in Wireless Days (WD), Mar. 2016, pp. 1–6.
  • [43] L. T. Dung, H. T. H. Trang, S. Choi, and S. O. Hwang, “Impact of soil medium on the path connectivity of sensors in wireless underground sensor networks,” in Int. Conf. on Adv. Techno. for Commun., (ATC), Oct. 2016, pp. 60–64.
  • [44] G. Liu, Z. Wang, and T. Jiang, “QoS-aware throughput maximization in wireless powered underground sensor networks,” IEEE Trans. on Commun., vol. 64, no. 11, pp. 4776–4789, Nov. 2016.
  • [45] B. Yuan, H. Chen, and X. Yao, “Optimal relay placement for lifetime maximization in wireless underground sensor networks,” Information Sciences, vol. 418-419, pp. 463 – 479, 2017.
  • [46] M. L. Oelze, W. D. OBrien, and R. G. Darmody, “Measurement of attenuation and speed of sound in soils,” Soil Sci. Soc. Am. J., vol. 66, pp. 788–796, May 2002.
  • [47] F. Adamo, G. Andria, F. Attivissimo, N. Giaquinto, and M. Savino, “An acoustic method for soil moisture measurement,” in Proc. of the 20th IEEE Instrumentation Techno. Conf., vol. 2, May 2003, pp. 952–957.
  • [48] F. Adamo, G. Andria, F. Attivissimo, and N. Giaquinto, “An acoustic method for soil moisture measurement,” IEEE Trans. Instrum. Meas, vol. 53, no. 4, pp. 891–898, Aug. 2004.
  • [49] R. Sharma and A. Gupta, “Continuous wave acoustic method for determination of moisture content in agricultural soil,” Computers and electronics in agriculture, vol. 73, no. 2, pp. 105–111, 2010.
  • [50] R. Freire, M. H. M. de Abreu, R. Y. Okada, P. F. Soares, and C. R. GranhenTavares, “Sound absorption coefficient in situ: An alternative for estimating soil loss factors,” Ultrasonics sonochemistry, vol. 22, pp. 100–107, 2015.
  • [51] A. Singer, S. Yang, and M. Oelze, “Acoustic communications: Through soils, sands, water, and tissue,” The J. of the Acoustical Soc. of America, vol. 141, no. 5, pp. 3986–3987, 2017.
  • [52] S. Yang, O. Baltaji, Y. M. Hashash, and A. Singer, “Soilcomm: A miniaturized through-soil wireless data transmission system,” The J. of the Acoustical Soc. of America, vol. 144, no. 3, pp. 1872–1872, 2018.
  • [53] W. R. Gardner, R. E. Hyden, E. J. Linyaev, L. Gao, C. Robbins, and J. Moore, “Acoustic telemetry delivers more real-time downhole data in underbalanced drilling operations,” in IADC/SPE Drilling Conf., 2006.
  • [54] J. M. Neff, P. L. Camwell et al., “Field test results of an acoustic telemetry MWD system,” in SPE/IADC Drilling Conference, 2007.
  • [55] A. K. Farraj, S. Miller, and K. Qaraqe, “Channel characterization for acoustic downhole communication systems,” in SPE Annual Technical Conf. and Exhibition.   Society of Petroleum Engineers, 2012.
  • [56] A. Farraj, “Acoustical communications for wireless downhole telemetry systems,” Ph.D. dissertation, Texas A&M University, 2012.
  • [57] M. A. Gutierrez-Estevez, U. Krueger, K. A. Krueger, K. Manolakis, V. Jungnickel, K. Jaksch, K. Krueger, S. Mikulla, R. Giese, M. Sohmer, and M. Reich, “Acoustic broadband communications over deep drill strings using adaptive OFDM,” in IEEE Wireless Commun. and Networking Conf., (WCNC), Apr. 2013, pp. 4089–4094.
  • [58] L. S. Kumar, W. K. Han, Y. L. Guan, Y. H. Lee, and S. Sun, “Optimization of acoustic communication for industrial drilling,” in IEEE Conf. on Info. Commun. Techno., Apr. 2013, pp. 1060–1063.
  • [59] Z. Wei, S. Yibing, and L. Yanjun, “Design of acoustic wireless remote transmission system for logging-while-drilling data,” in IEEE Int. Conf. on Electronic Measurement Instruments, Aug. 2013, pp. 53–57.
  • [60] T. J. Ahmad, M. Noui-Mehidi, and M. Arsalan, “Performance analysis of downhole acoustic communication in multiphase flow,” in 40th Annual Conference of the IEEE Industrial Electronics Society, Oct. 2014, pp. 3909–3913.
  • [61] K. Pelekanakis, M. Chitre, L. S. Kumar, and Y. L. Guan, “Performance of channel coding and equalization for acoustic telemetry along drill strings,” in IEEE Int. Conf. on Commun. Systems, Nov. 2014, pp. 610–614.
  • [62] D. Ma, Y. Shi, W. Zhang, and G. Liu, “Design of acoustic transmission along drill strings for logging while drilling data based on adaptive nc-ofdm,” AEU - Int. J. of Electronics and Commun., vol. 83, pp. 329–338, 2018.
  • [63] Li, Zhigang, Ge, Shitong, and Fu, Zhongyao, “Design of the acoustic signal receiving unit of acoustic telemetry while drilling,” MATEC Web of Conf., vol. 61, pp. 7–12, 2016.
  • [64] A. Alenezi and A. Abdi, “A comparative study of multichannel and single channel accelerometer sensors for communication in oil wells,” in Int. Conf. on Commun. and Signal Processing (ICCSP), Apr. 2017, pp. 153–159.
  • [65] J. Gao, L. Chen, and Q. Li, “Study on acoustic wave transmission technology of measurement-while-drilling (MWD) data,” in Int. Conf. on Adv. in Materials, Mechatronics and Civil Engineering (ICAMMCE 2018), 2018, pp. 1–6.
  • [66] Z. Kang, Y. Yu, and C. Hou, “Study on stress and strain and characteristics of acoustic emission in the process of rock failure,” in Second Int. Conf. on Mechanic Automation and Control Engineering, Jul. 2011, pp. 7737–7740.
  • [67] L. Sun and Y. Li, “Acoustic emission sound source localization for crack in the pipeline,” in Chinese Control and Decision Conf., May 2010, pp. 4298–4301.
  • [68] B. Van Hieu, S. Choi, Y. U. Kim, Y. Park, and T. Jeong, “Wireless transmission of acoustic emission signals for real-time monitoring of leakage in underground pipes,” KSCE J. of Civil Engineering, vol. 15, no. 5, p. 805, May 2011.
  • [69] D. Su, J. V. Miro, and T. Vidal-Calleja, “Modelling in-pipe acoustic signal propagation for condition assessment of multi-layer water pipelines,” in IEEE 10th Conf. on Industrial Electronics and Applications (ICIEA), Jun. 2015, pp. 545–550.
  • [70] U. S. Khan, W. Al-Nuaimy, and F. E. A. El-Samie, “Detection of landmines and underground utilities from acoustic and gpr images with a cepstral approach,” J. of Visual Commun. and Image Representation, vol. 21, no. 7, pp. 731 – 740, 2010.
  • [71] P. D. Thakur, P. Agnihotri, L. Deng, A. M. Soliman, P. Kieduppatum, and W. Fernandes, “The most common impacts of drilling dynamics and environments on log-while-drilling data: A study from Abu dhabi,” in Abu Dhabi Int. Petroleum Exhibition & Conf., 2018.
  • [72] R. Hutin, R. Tennent, S. Kashikar et al., “New mud pulse telemetry techniques for deepwater applications and improved real-time data capabilities,” in SPE/IADC drilling conference, 2001.
  • [73] C. Klotz, P. R. Bond, I. Wassermann, and S. Priegnitz, “A new mud pulse telemetry system for enhanced MWD/LWD applications.” in IADC/SPE Drilling Conf., Mar. 2008, pp. 1–8.
  • [74] D. Hahn, V. Peters, C. Rouatbi, and E. Scholz, “Reciprocating pulser for mud pulse telemetry,” Aug. 2008, US Patent 7417920.
  • [75] F. Qu, Z. Zhang, J. Hu, J. Xu, S. Wang, and Y. Wu, “Adaptive dual-sensor noise cancellation method for continuous wave mud pulse telemetry,” J. of Petroleum Sci. and Engineering, vol. 162, pp. 386 – 393, 2018.
  • [76] Z. Jianhui, W. Liyan, L. Fan, and L. Yanlei, “An effective approach for the noise removal of mud pulse telemetry system,” in Int. Conf. on Electronic Measurement and Instruments, Aug. 2007, pp. 1–9.
  • [77] I. Wasserman, D. Hahn, D. H. Nguyen, H. Reckmann, and J. Macpherson, “Mud-pulse telemetry sees step-change improvement with oscillating shear valves,” Oil and Gas J., vol. 106, no. 24, pp. 39–39, 2008.
  • [78] I. Wassermann and A. Kaniappan., “How high-speed telemetry affects the drilling process,” J. of Petroleum Technology, Jun. 2009.
  • [79] H. Reckmann, “Downhole noise cancellation in mud-pulse telemetry,” Aug. 2014, US Patent 8811118.
  • [80] A. Jarrot, A. Gelman, and J. Kusuma, “Wireless digital communication technologies for drilling: Communication in the bits/s regime,” IEEE Signal Process. Mag., vol. 35, no. 2, pp. 112–120, 2018.
  • [81] J. Harrell, A. G. Brooks, and H. S. Morsy, “Method and apparatus for mud pulse telemetry in underbalanced drilling systems,” Aug. 2000, US Patent 6097310.
  • [82] Y. Lin, X. Kong, Y. Qiu, and Q. Yuan, “Calculation analysis of pressure wave velocity in gas and drilling mud two-phase fluid in annulus during drilling operations,” Mathematical Problems in Engineering, vol. 2013, 2013.
  • [83] R. Hutin, “Zero sum pressure drop mud telemetry modulator,” Jan. 2016, US Patent 9228432.
  • [84] Y. Lu, J. Tang, Z. Ge, B. Xia, and Y. Liu, “Hard rock drilling technique with abrasive water jet assistance,” Int. J. of Rock Mechanics and Mining Sciences, vol. 60, pp. 47–56, 2013.
  • [85] S. M. Mwachaka, A. Wu, and Q. Fu, “A review of mud pulse telemetry signal impairments modeling and suppression methods,” J. of Petroleum Exploration and Production Techno., Jun 2018.
  • [86] Z. Sun and I. F. Akyildiz, “Underground wireless communication using magnetic induction,” in IEEE Int. Conf. on Commun., Jun. 2009, pp. 1–5.
  • [87] C. M. Caffrey, J. Hakli, M. Hirvonen, I. Huhtinen, K. Nummila, and T. Lehikoinen, “Magnetically coupled wireless communication for buried environmental sensor,” in Int. Conf. on Environment and Electrical Engineering, May 2013, pp. 341–345.
  • [88] X. Tan, Z. Sun, and I. F. Akyildiz, “Wireless underground sensor networks: Mi-based communication systems for underground applications.” IEEE Antennas and Propag. Mag., vol. 57, no. 4, pp. 74–87, Aug. 2015.
  • [89] I. F. Akyildiz and E. P. Stuntebeck, “Wireless underground sensor networks: Research challenges,” Ad Hoc Networks, vol. 4, no. 6, pp. 669 – 686, Nov. 2006.
  • [90] Z. Sun and I. F. Akyildiz, “Magnetic induction communications for wireless underground sensor networks,” IEEE Trans. Antennas Propag, vol. 58, no. 7, pp. 2426–2435, Jul. 2010.
  • [91] L. Yan, D. Wei, M. Pan, and J. Chen, “Downhole wireless communication using magnetic induction technique,” in United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), Jan. 2018, pp. 1–5.
  • [92] S. Kisseleff, I. F. Akyildiz, and W. Gerstacker, “On modulation for magnetic induction based transmission in wireless underground sensor networks,” in IEEE Int. Conf. on Commun., (ICC), Jun. 2014, pp. 71–76.
  • [93] A. Gungi, V. Vippalapalli, K. A. Unnikrishna Menon, and B. Hariharan, “Inductively powered underground wireless communication system,” in Microelectronics, Electromagnetics and Telecommunications, S. C. Satapathy, N. B. Rao, S. S. Kumar, C. D. Raj, V. M. Rao, and G. V. K. Sarma, Eds., 2016, pp. 205–215.
  • [94] J. Ma, X. Zhang, and Q. Huang, “Near-field magnetic induction communication device for underground wireless communication networks,” Science China Information Sciences, vol. 57, no. 12, pp. 1–11, 2014.
  • [95] J. Ma, X. Zhang, Q. Huang, L. Cheng, and M. Lu, “Experimental study on the impact of soil conductivity on underground magneto-inductive channel,” IEEE Antennas Wireless Propag. Lett., vol. 14, pp. 1782–1785, Apr. 2015.
  • [96] A. R. Silva and M. Moghaddam, “Design and implementation of low-power and mid-range magnetic-induction-based wireless underground sensor networks,” IEEE Trans. Instrum. Meas., vol. 65, no. 4, pp. 821–835, Apr. 2016.
  • [97] A. M. Zungeru, H. Ezea, and J. Katende, “Pulsed power system for wireless underground sensor networks,” in Third Int. Conf. on Electrical, Electronics, Computer Engineering and their Applications (EECEA), Apr. 2016, pp. 126–132.
  • [98] H. Guo, Z. Sun, and C. Zhou, “Practical design and implementation of metamaterial-enhanced magnetic induction communication,” IEEE Access, vol. 5, pp. 17 213–17 229, 2017.
  • [99] C. H. Martins, A. A. Alshehri, and I. F. Akyildiz, “Novel MI-based (FracBot) sensor hardware design for monitoring hydraulic fractures and oil reservoirs,” in IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Commun. Conf., (UEMCON), Oct. 2017, pp. 434–441.
  • [100] A. A. Alshehri, C. H. Martins, and I. F. Akyildiz, “Wireless FracBot (sensor) nodes: Performance evaluation of inductively coupled near field communication (NFC),” in IEEE Sensors Applications Symposium (SAS), Mar. 2018, pp. 1–6.
  • [101] Z. Sun and I. F. Akyildiz, “Optimal deployment for magnetic induction-based wireless networks in challenged environments,” IEEE Trans. Wireless Commun., vol. 12, no. 3, pp. 996–1005, Mar. 2013.
  • [102] A. Kulkarni, V. Kumar, and S. B. Dhok, “Enabling technologies for range enhancement of MI based wireless non-conventional media communication,” in Int. Conf. on Computing, Commun. and Networking Techno., (ICCCNT), Jul. 2018, pp. 1–7.
  • [103] S. Swathi and S. Santhanam, “An efficient MI waveguide based underground wireless communication for smart irrigation,” in IEEE India Council Int. Conf., (INDICON), 2018.
  • [104] V. Pathak, V. Kumar, and R. K. Barik, “Magnetic induction communication based transceiver coil and waveguide structure modeling for non-conventional WSNs,” in Int. Conf. on Comput., Commun. and Networking Techno., (ICCCNT), Jul. 2018, pp. 1–7.
  • [105] H. Guo and Z. Sun, “Channel and energy modeling for self-contained wireless sensor networks in oil reservoirs,” IEEE Trans. Wireless Commun., vol. 13, no. 4, pp. 2258–2269, Apr. 2014.
  • [106] M. A. Akkas, “Channel modeling of wireless sensor networks in oil,” Wireless Personal Commun., vol. 95, no. 4, pp. 4337–4355, Aug. 2017.
  • [107] Z. Sun and B. Zhu, “Channel and energy analysis on magnetic induction-based wireless sensor networks in oil reservoirs,” in IEEE Int. Conf. on Commun., (ICC), Jun. 2013, pp. 1748–1752.
  • [108] S. Kisseleff, I. F. Akyildiz, and W. Gerstacker, “Interference polarization in magnetic induction based wireless underground sensor networks,” in IEEE Int. Symposium on Personal, Indoor and Mobile Radio Commun., (PIMRC Workshops), Sep. 2013, pp. 71–75.
  • [109] S. Kisseleff, W. Gerstacker, Z. Sun, and I. F. Akyildiz, “On the throughput of wireless underground sensor networks using magneto-inductive waveguides,” in IEEE Global Commun. Conf., (GLOBECOM), Dec 2013, pp. 322–328.
  • [110] S. Kisseleff, I. F. Akyildiz, and W. H. Gerstacker, “Digital signal transmission in magnetic induction based wireless underground sensor networks,” IEEE Trans. on Commun., vol. 63, no. 6, pp. 2300–2311, Jun. 2015.
  • [111] S. Lin, I. F. Akyildiz, P. Wang, and Z. Sun, “Distributed cross-layer protocol design for magnetic induction communication in wireless underground sensor networks,” IEEE Trans. on Wireless Commun., vol. 14, no. 7, pp. 4006–4019, Jul. 2015.
  • [112] S.-C. Lin, I. F. Akyildiz, P. Wang, and Z. Sun, “Optimal energy-throughput efficiency for magneto-inductive underground sensor networks,” in IEEE Int. Black Sea Conf. on Commun. and Networking (BlackSeaCom), May 2014, pp. 22–27.
  • [113] H. Guo and Z. Sun, “Full-duplex metamaterial-enabled magnetic induction networks in extreme environments,” in IEEE Conf. on Computer Commun., (INFOCOM), Apr. 2018, pp. 558–566.
  • [114] H. Trang, L. Dung, and S. Hwang, “Connectivity analysis of underground sensors in wireless underground sensor networks,” Ad Hoc Networks, vol. 71, pp. 104–116, 2018.
  • [115] A. K. Paul and T. Sato, “Localization in wireless sensor networks: A survey on algorithms, measurement techniques, applications and challenges,” J. of Sensor and Actuator Networks, vol. 6, no. 4, pp. 1–24, 2017.
  • [116] N. Saeed, A. Celik, T. Y. Al-Naffouri, and M.-S. Alouini, “Underwater optical wireless communications, networking, and localization: A survey,” CoRR, vol. abs/1803.02442, 2018. [Online]. Available: http://arxiv.org/abs/1803.02442
  • [117] A. Markham, N. Trigoni, S. A. Ellwood, and D. W. Macdonald, “Revealing the hidden lives of underground animals using magneto-inductive tracking,” in Proc. of the 8th ACM Conf. on Embedded Networked Sensor Systems, 2010, pp. 281–294.
  • [118] A. Markham, N. Trigoni, D. W. Macdonald, and S. A. Ellwood, “Underground localization in 3-D using magneto-inductive tracking,” IEEE Sensors J., vol. 12, no. 6, pp. 1809–1816, Jun. 2012.
  • [119] A. Markham and N. Trigoni, “Magneto-Inductive NEtworked Rescue System (MINERS): Taking sensor networks underground,” in ACM/IEEE 11th Int. Conf. on Information Processing in Sensor Networks (IPSN), April 2012, pp. 1–11.
  • [120] T. E. Abrudan, O. Kypris, N. Trigoni, and A. Markham, “Impact of rocks and minerals on underground magneto-inductive communication and localization,” IEEE Access, vol. 4, pp. 3999–4010, Aug. 2016.
  • [121] Q. Huang, X. Zhang, and J. Ma, “Underground magnetic localization method and optimization based on simulated annealing algorithm,” in IEEE Int. Conf. on Ubiquitous Intelligence and Computing, 2016, pp. 168–173.
  • [122] H. Huang and Y. R. Zheng, “3-D localization of wireless sensor nodes using near-field magnetic-induction communications,” Phy. Commun., vol. 30, pp. 97–106, 2018.
  • [123] S. Lin, A. A. Alshehri, P. Wang, and I. F. Akyildiz, “Magnetic induction-based localization in randomly deployed wireless underground sensor networks,” IEEE Internet of Things J., vol. 4, no. 5, pp. 1454–1465, Oct. 2017.
  • [124] T. Abrudan, Z. Xiao, A. Markham, and N. Trigoni, “Underground incrementally deployed magneto-inductive 3-d positioning network,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 8, pp. 4376–4391, 2016.
  • [125] S. Kisseleff, X. Chen, I. F. Akyildiz, and W. Gerstacker, “Localization of a silent target node in magnetic induction based wireless underground sensor networks,” in IEEE Int. Conf. on Commun., (ICC), May 2017, pp. 1–7.
  • [126] W. Tian and W. Yang, “Analytical model of transmission distance for magnetic induction through-the-earth communication under the beacon mode,” J. of China Univ. of Mining and Techno., vol. 47, no. 6, pp. 1368–1377, 2018.
  • [127] M. I. ul Haq and D. Kim, “Improved localization by time of arrival for internet of things in 3D,” in Int. Conf. on Applied Electromagnetics and Commun., (ICECOM), Sep. 2016, pp. 1–5.
  • [128] A. R. Ansari, N. Saeed, M. I. U. Haq, and S. Cho, “Accurate 3D localization method for public safety applications in vehicular ad-hoc networks,” IEEE Access, vol. 6, pp. 20 756–20 763, Apr. 2018.
  • [129] Z. Duan and Q. Zhou, “CRLB-weighted intersection method for target localization using AOA measurements,” in IEEE Int. Conf. on Computational Intelligence and Virtual Environments for Measurement Systems and App., (CIVEMSA), Jun. 2015, pp. 1–6.
  • [130] A. Dersan and Y. Tanik, “Passive radar localization by time difference of arrival,” in Proc. of MILCOM, vol. 2, Oct 2002, pp. 1251–1257 vol.2.
  • [131] N. Saeed and H. Nam, “Robust multidimensional scaling for cognitive radio network localization,” IEEE Trans. Veh. Technol., vol. 64, no. 9, pp. 4056–4062, 2015.
  • [132] ——, “Cluster based multidimensional scaling for irregular cognitive radio networks localization,” IEEE Trans. on Signal Process., vol. 64, no. 10, pp. 2649–2659, 2016.
  • [133] ——, “Energy efficient localization algorithm with improved accuracy in cognitive radio networks,” IEEE Commun. Lett., vol. 21, no. 9, 2017.
  • [134] N. Saeed, A. Celik, T. Y. Al-Naffouri, and M.-S. Alouini, “Underwater optical sensor networks localization with limited connectivity,” in IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2018, pp. 1–5.
  • [135] ——, “Robust 3d localization of underwater optical wireless sensor networks via low rank matrix completion,” in IEEE 19th Int. Works. on Signal Processing Adv. in Wireless Commun., (SPAWC), Jul. 2018, pp. 1–5.
  • [136] N. Saeed, A. Celik, S. Alouini, and T. Y. Al-Naffouri, “Performance analysis of connectivity and localization in multi-hop underwater optical wireless sensor networks,” IEEE Trans. on Mobile Comput., 2018.
  • [137] N. Patwari, A. O. Hero, M. Perkins, N. S. Correal, and R. J. O’dea, “Relative location estimation in wireless sensor networks,” IEEE Trans. on Signal Process., vol. 51, no. 8, pp. 2137–2148, 2003.
  • [138] T. Jia and R. M. Buehrer, “A new cramer-rao lower bound for TOA-based localization,” in IEEE Military Commun. Conf., Nov. 2008, pp. 1–5.
  • [139] N. Saeed, , M.-S. Alouini, and T. Y. Al-Naffouri, “On achievable accuracy of localization in magnetic induction-based internet of underground things for oil and gas reservoirs,” CoRR, vol. abs/1803.02442, 2019. [Online]. Available: http://arxiv.org/abs/1901.09556
  • [140] S. Kisseleff, X. Chen, I. F. Akyildiz, and W. H. Gerstacker, “Efficient charging of access limited wireless underground sensor networks,” IEEE Trans. on Commun., vol. 64, no. 5, pp. 2130–2142, May 2016.
  • [141] A. A. Alshehri, S. Lin, and I. F. Akyildiz, “Optimal energy planning for wireless self-contained sensor networks in oil reservoirs,” in IEEE Int. Conf. on Commun., (ICC), May 2017, pp. 1–7.
  • [142] R. J. Schroeder, “The present and future of fiber optic sensors for the oilfield service industry: where is there a role?” in 15th Optical Fiber Sensors Conf. Techn. Digest., May 2002, pp. 39–42 vol.1.
  • [143] M. Hernandez, D. W. MacNeill, M. Reeves, A. D. Kirkwood, J. P. Ruszka, R. Zaeper, S. R. Lemke et al., “High-speed wired drillstring telemetry network delivers increased safety, efficiency, reliability, and productivity to the drilling industry,” in SPE Indian Oil and Gas Technical Conf. and Exhibition, 2008.
  • [144] J. Algeroy, J. Lovell, G. Tirado, R. Meyyappan, G. Brown, R. Greenaway, M. Carney, J. H. Meyer, J. E. Davies, and I. D. Pinzon, “Permanent monitoring: taking it to the reservoir,” Oilfield Review, vol. 22, no. 1, pp. 34–41, 2010.
  • [145] R. Mijarez, D. Pascacio, R. Guevara, O. Pacheco, C. Tello, and J. Rodríguez, “Communication system for downhole measurement tools based on real-time SNR characterization in coaxial cable used as communication channel,” Microelectronics Research, vol. 2013, no. 2, pp. 174–183, 2013.
  • [146] C. Baldwin, “Fiber optic sensors in the oil and gas industry: Current and future applications,” in Opto-Mechanical Fiber Optic Sensors, H. Alemohammad, Ed.   Butterworth-Heinemann, 2018, pp. 211 – 236.
  • [147] T. K. Kragas, B. A. Williams, G. A. Myers et al., “The optic oil field: deployment and application of permanent in-well fiber optic sensing systems for production and reservoir monitoring,” in SPE Annual Tech. Conf. and Exhibition, 2001.
  • [148] A. D. Kersey, “Optical fiber sensors for permanent downwell monitoring applications in the oil and gas industry,” IEICE Trans. on electronics, vol. 83, no. 3, pp. 400–404, 2000.
  • [149] P. M. Nellen, P. Mauron, A. Frank, U. Sennhauser, and K. Bohnert, “Reliability of fiber bragg grating based sensors for downhole applications,” Sensors and Actuators A: Physical, vol. 103, no. 3, pp. 364 – 376, 2003.
  • [150] Y. Zhang, J. Ning, S. Yang, and H.-L. Cui, “Field test investigation of fiber optic seismic geophone in oilfield exploration,” Proc. SPIE, vol. 6770, no. 8, pp. 6770 – 6770, 2007.
  • [151] X. Zhou, Q. Yu, and W. Peng, “Simultaneous measurement of downhole pressure and distributed temperature with a single fiber,” Measurement Science and Techno., vol. 23, no. 8, pp. 8–16, 2012.
  • [152] B. Prevedel, F. Bulut, M. Bohnhoff, C. Raub, R. F. Kartal, F. Alver, and P. E. Malin, “Downhole geophysical observatories: best installation practices and a case history from Turkey,” Int. J. of Earth Sci., vol. 104, no. 6, pp. 1537–1547, Sep. 2015.
  • [153] H. Wu, Y. Guo, L. Xiong, W. Liu, G. Li, and X. Zhou, “Optical fiber based sensing, measuring, and implementation methods for slope deformation monitoring: A review,” IEEE Sensors J., pp. 1–20, Feb. 2019.
  • [154] Y. Li, S. Videv, M. Abdallah, K. Qaraqe, M. Uysal, and H. Haas, “Single photon avalanche diode (spad) VLC system and application to downhole monitoring,” in IEEE Global Commun. Conf., (GLOBECOM), Dec. 2014, pp. 2108–2113.
  • [155] F. Miramirkhani, M. Uysal, O. Narmanlioglu, M. Abdallah, and K. Qaraqe, “Visible light channel modeling for gas pipelines,” IEEE Photonics J., vol. 10, no. 2, pp. 1–10, Apr. 2018.
  • [156] J. Puente Fernández, L. García Villalba, and T.-H. Kim, “Software defined networks in wireless sensor architectures,” Entropy, vol. 20, no. 4, p. 225, 2018.
  • [157] M. S. Hajirahimova, “Opportunities and challenges big data in oil and gas industry,” Institute of Information Techno. of Azerbaijan National Academy of Sci., Tech. Rep., 2017.
  • [158] R. K. Perrons and A. Hems, “Cloud computing in the upstream oil & gas industry: A proposed way forward,” Energy Policy, vol. 56, pp. 732 – 737, 2013.