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

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    Type
    Preprint
    Authors
    Saeed, Nasir
    Alouini, Mohamed-Slim cc
    Al-Naffouri, Tareq Y. cc
    KAUST Department
    Computer Electrical and Mathematical Sciences & Engineering (CEMSE) Division, KAUST, Thuwal, Makkah Province, Kingdom of Saudi Arabia, 23955-690
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-01-28
    Permanent link to this record
    http://hdl.handle.net/10754/660857
    
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    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.
    Publisher
    arXiv
    arXiv
    1901.09556
    Additional Links
    https://arxiv.org/pdf/1901.09556
    Collections
    Preprints; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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