On Achievable Accuracy of Localization in Magnetic Induction-Based Internet of Underground Things for Oil and Gas Reservoirs

TL;DR

This paper derives the theoretical lower bound on localization accuracy for 3D magnetic induction-based underground sensor networks in oil and gas reservoirs, highlighting how system parameters influence performance.

Contribution

It introduces the CRLB expression for 3D MI-based underground localization, considering channel parameters and providing insights for system design.

Findings

Localization accuracy depends on the number of anchors and noise variance.

System parameters like frequency and number of underground things affect error bounds.

The derived CRLB guides the design of more accurate underground localization systems.

Abstract

Magnetic Induction (MI) is an efficient wireless communication method to deploy operational internet of underground things (IOUT) for oil and gas reservoirs. The IOUT consists of underground things which are capable of sensing the underground environment and communicating with the surface. The MI-based IOUT enable many applications, such as monitoring of the oil rigs, optimized fracturing, and optimized extraction. Most of these applications are dependent on the location of the underground things and therefore require accurate localization techniques. The existing localization techniques for MI-based underground sensing networks are two-dimensional and do not characterize the achievable accuracy of the developed methods which are both crucial and challenging tasks. Therefore, this paper presents the expression of the Cramer Rao lower bound (CRLB) for three-dimensional MI-based IOUT localization which takes into account the channel parameters of the underground magnetic-induction. The derived CRLB provide the suggestions for an MI-based underground localization system by associating the system parameters with the error trend. Numerical results demonstrate that localization accuracy is affected by different channel and networks parameters such as the number of anchors, noise variance, frequency, and the number of underground things.