Signal Propagation and Analysis in Wireless Underground Sensor Networks

Abstract

The most challenging issue in the design of wireless sensor networks for the application of localization in the underground environment, mostly for miner’s location, is the sensor nodes’ energy consumption, efficiency and communication. Underground Wireless Sensor Networks are active and promising area of application of Wireless Sensor Networks (WSNs), whereby sensor nodes perform sensing duties in the underground environment. Most of the communication techniques used in the underground environment experience a high path loss and hence, hinders the range needed for transmission. However, the available option to increase information transmission is to increase the transmission power which needs large size of apparatus which is also limited in the underground. To solve the mentioned problems, this paper proposed a Magnetic Induction based Pulse Power. Analytical results of the Magnetic Induction based Pulse Power with an ordinary magnetic induction communication technique show an improvement in Signal-to-Noise Ratio (SNR) and path loss with variation in distance between nodes and frequency of operation. This paper further formulates a nonlinear program to determine the optimal data (events) extraction in a grid based WUSNs.

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

“…For the conceptual scheme in the radio frequency area, the coefficients are based on soil types. Zungeru et al [12] suggested a pulse power dependent on magnetic induction. Their analytical findings of the Magnetic Modulation derived the pulse strength with a usual electromagnetic field communication channel, an increase in signal-to-noise ratio, and fading channels of variance in node.…”

“…Eng way = {Eng way (wusn sn , wusn) Eng way (wusn sn , wusn x+1 ) Eng way (wusn way , SN)} (12) Ebl way is a set of variables for balancing load of Eng way .…”

Wireless Network Indoor Positioning Method Using Nonmetric Multidimensional Scaling and RSSI in the Internet of Things Environment

“…RSSI Ranging. In indoor environments, the strength of wireless signals is unpredictable due to multipath propagation and shadowing effects, and log-distance models are often used to represent signal propagation losses [18]. Let the distance between the receiving node and the transmitting node be d, then the loss caused by the signal during propagation can be expressed as…”

Performance Analysis of Multihop Underground Magnetic Induction Communication

“…Underground communication has recently gained considerable attention owing to its wide range of applications including seismic and climate change monitoring, smart agriculture, mining, infrastructure monitoring, industrial Internet of Underground Things (IoUT) and border patrol [1][2][3][4][5]. Existing well-established communication protocols for abovethe-ground communication cannot be directly modeled for underground communication because of the dynamic nature of the channel and the unpredictability of its characteristics such as temperature, humidity and composition, which directly influence signal propagation [2,3,6]. Consequently, many different communication techniques have been investigated, such as those involving electromagnetic (EM) waves [7], acoustic waves [8] and magnetic induction (MI) [9].…”