Magnetic Induction-Based Localization in Randomly Deployed Wireless Underground Sensor Networks

Levenberg-marquardt algorithm to identify the fault analysis for industrial applications

“…Next, the paper aims a two-stage positioning mechanism for acquiring quick and accurate area outputs by: first, building the fastinitial positioning through a flipping direction increased Lagrangian technique for rough sensor areas inside a short duration time, and after that proposing fine-grained positioning for executing powerful scan for ideal area estimations by means of the conjugate gradient algorithm. Simulation affirms that our results yield exact sensor areas with both low and high noise and uncovers the major effect of underground environments on the limitation execution [2]. Model-driven data acquisition is one of the methodologies used to save sensor node energy in wireless sensor networks (WSNs), which inhibits information transmission by running one synchronized prediction technique at the sink node and sensor, and just when the predicted value varies a long way from the genuine value should the sensor node transmit the detected information to the sink node.…”

3D Localization for Internet of Underground Things in Oil and Gas Reservoirs

“…The performance of the CRLB for MI-based IOUT is tested under various network settings. Table I presents the simulation parameters which are taken mainly from [13]. In the following, first, we examine the effect of operating frequency, noise variance, and the number of anchors on the performance of the CRLB.…”

“…However, the subsurface environment does not support the use of these signals, and therefore the localization techniques developed for the underwater or indoor environment cannot be directly applied. Thus, a two-dimensional (2D) localization technique has been proposed in [13] for MI-based sensing networks. The authors in [13] have introduced the use of MI induction for channel based distance estimation where However, in sparse underground sensing networks the connectivity of the network is limited due to the short transmission distance of MI communication.…”

“…Thus, a two-dimensional (2D) localization technique has been proposed in [13] for MI-based sensing networks. The authors in [13] have introduced the use of MI induction for channel based distance estimation where However, in sparse underground sensing networks the connectivity of the network is limited due to the short transmission distance of MI communication. Hence, localization of the underground things is challenging due to the limited connectivity, directionality of MI coils, interference from the Earths magnetic field and underground metals [16]- [18].…”

A Study on Communication Channel Estimation for Emergency Networks in Underground Mining Environment

“…Emergency networks in underground mining environment should provide many of mobile devices with the same time multi-access communications, high speed data transmission, and low transmit power capability. However, Bandwidth limitations, propagation loss, noise, interference, time variance, and frequency selective fading caused by multipath propagation can bring high symbol error ratio in high rate transmission [2]. Communication systems can enhance their performance by using anti multipath fading techniques only when they could make precise and real time estimation of fading coefficients of multipath channel.…”