This work is on 3-D localization of sensor motes in massive swarms based solely on 1-D relative distance-measurements between neighbouring motes. We target applications in remote and difficult-to-access environments such as the exploration and mapping of the interior of oil reservoirs where hundreds or thousands of motes are used. These applications bring forward the need to use highly miniaturized sensor motes of less than 1 centimeter, thereby significantly limiting measurement and processing capabilities. These constraints, in combination with additional limitations posed by the environments, impede the communication of unique hardware identifiers, as well as communication with external, fixed beacons.
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In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).
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This paper extends the 'go-with-the-flow' method to explore enclosed environments, like oil reservoirs, pipe lines that transport liquids, and industrial tanks for processing chemicals, where sensing nodes cannot establish communication with the external world. Nonetheless, large quantities of highly miniaturized, thus power-constrained sensor nodes are injected into these environment and flow through them along with the medium, monitoring their environment but also reconstructing their time-varying position from mutual communication, but without any communication to external base stations or beacons. The relative trajectories of nodes yield essential insights of the fluid flow in the otherwise inaccessible environment. We present a functional implementation of a ranging protocol accommodating size and energy constraints. Our simulation chain models node movement from different types of flow dynamics. It comprehensively assesses not only the performance of the communication and ranging protocols, but also of the reconstruction algorithm. Our assessments cover a wide range of different environments and flow profiles, including highly dynamic ones.
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