Wireless Underground Sensor Networks: Channel Modeling and Operation Analysis in the Terahertz Band

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.

Section: Related Workmentioning

confidence: 99%

A Machine Learning Centered Approach for Uncovering Excavators’ Last Known Location Using Bluetooth and Underground WSN

Kumari

Siwach

Singh

et al. 2022

“…WUSN was also used for increasing the efficiency of oil recovery processes. In this case, millimeter-scale sensors and antennas are deployed within confined oil reservoir fractures, which necessitates the sensor nodes operating in the terahertz range [19].…”

Section: Related Workmentioning

confidence: 99%

Signal Propagation Models in Soil Medium for the Study of Wireless Underground Sensor Networks: A Review of Current Trends

Banaseka

Katsriku

Abdulai

et al. 2021

Radio signal propagation modeling plays an important role in the design of wireless communication systems. Various models have been developed, over the past few decades, to predict signal propagation and behavior for wireless communication systems in different operating environments. Recently, there has been an interest in the deployment of wireless sensors in soil. To fully exploit the capabilities of sensor networks deployed in soil requires an understanding of the propagation characteristics within this environment. This paper reviews the cutting-edge developments of signal propagation in the subterranean environment. The most important modeling techniques for modeling include electromagnetic waves, propagation loss, magnetic induction, and acoustic wave. These are discussed vis-a-vis modeling complexity and key parameters of the environment including electric and magnetic properties of soil. An equation to model propagation in the soil is derived from the free space model. Results are presented to show propagation losses and at different frequencies and volumetric water content. The channel capacity and the operating frequency are also analyzed against soil moisture at different soil types and antenna sizes.

“…It is worth noting that in this architecture communication may take place through the soil medium between the underground devices or may take place through air and plant foliage devices attached to the plant body. IoUT applications have challenging requirements that include impairments attributable to wireless communication through soil [20][21][22][23] and machinery operating in remote crop fields and exposure to the elements. Progress in IoUT research will significantly impact and benefit many application areas such as plant monitoring and control, border patrol, underground mines and tunnels, and pipeline assessment [6,20,22].…”

Section: System Architecture and Iout Application In Agriculturementioning

confidence: 99%

Soil Medium Electromagnetic Scattering Model for the Study of Wireless Underground Sensor Networks

Banaseka

Franklin

Katsriku

et al. 2021

In recent years, there has been keen interest in the area of Internet of Things connected underground, and with this is the need to fully understand and characterize their operating environment. In this paper, a model, based on the Peplinski principle, for the propagation of waves in soils that takes into account losses attributable to the presence of local inhomogeneity is proposed. In the work, it is assumed that the inhomogeneities are obstacles such as stones or pebbles, of moderate size, all identical and randomly distributed in space. A new wave number is obtained through a combination of the multiple scattering theory and the Peplinski principle. Since the latter principle considers the propagation in a homogeneous medium (without obstacles), the wave number it provides is inserted into the one resulting from the former, the multiple scattering theory. The effective wave number thus obtained is compared numerically with that of Peplinski alone on the one hand and with that of multiple scattering alone on the other hand. The phase velocity and the loss tangent are analyzed against the particle concentration at the low-frequency Rayleigh limit condition ( k a ≲ 0.1 ) and against the frequency at two particle concentrations ( c = 0.2 and c = 0.4 ), two particle radii ( a = 0.55 cm and a = 1.10 cm), and 5% and 50% volumetric water content of the soil. Path losses are also compared to each other to examine the effects on transmission of soil containing obstacles. The results obtained suggest that the proposed model has better accuracy in estimating the wave number than previously used schemes.