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RcdMathLib: An Open Source Software Library for Computing on Resource-Limited Devices.

Zakaria Kasmi1, Abdelmoumen Norrdine2, Jochen Schiller1

  • 1Freie Universität Berlin, Department of Mathematics and Computer Science, Takustraße 9, 14195 Berlin, Germany.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
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This summary is machine-generated.

RcdMathLib is an open-source library for solving complex equations on resource-constrained devices, enabling Internet of Things (IoT) applications through decentralized computing and efficient algorithms.

Area of Science:

  • Computational Mathematics
  • Embedded Systems Engineering
  • Internet of Things (IoT)

Background:

  • Multivariate systems require significant computational resources, limiting their application on low-cost, resource-constrained devices.
  • Decentralized processing and efficient algorithms are crucial for enabling ubiquitous computing and advanced Internet of Things (IoT) applications.
  • Existing libraries may not be optimized for on-the-fly computation on embedded systems.

Purpose of the Study:

  • To develop an open-source library, RcdMathLib, for solving multivariate linear and nonlinear systems.
  • To enable on-the-fly computation on low-cost and resource-constrained devices, facilitating IoT implementations.
  • To provide a modular and layer-based library with diverse functionalities for algebraic operations, data processing, and system optimization.
Keywords:
Gauss–NewtonLevenberg–MarquardtRIOT-OSembedded systemsinternet of thingsmicrocontrollersmultipath recognition and mitigationpositioningsingular value decompositiontrilateration

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Main Methods:

  • Development of a modular, layer-based open-source library (RcdMathLib).
  • Implementation of efficient decomposition approaches, including Singular Value Decomposition (SVD), for solving linear and nonlinear equations.
  • Integration of optimization methods like Gauss-Newton and Levenberg-Marquardt for regression, smoothing, and curve fitting.
  • Inclusion of a positioning module with trilateration algorithms and multipath error reduction for IoT devices.

Main Results:

  • RcdMathLib successfully supports on-the-fly computing on microcontrollers and other resource-constrained devices.
  • The library enables the solution of multivariate linear and nonlinear systems using efficient decomposition and optimization techniques.
  • A dedicated positioning module allows for accurate device location computation and error optimization in IoT networks.
  • The library has been implemented and tested on both resource-limited IoT devices and full-fledged operating systems.

Conclusions:

  • RcdMathLib provides a versatile and efficient solution for complex mathematical computations in resource-constrained environments.
  • The library is a significant step towards ubiquitous computing, enabling the development of sophisticated IoT applications.
  • Its open-source nature and modular design promote accessibility and further development within the scientific and engineering communities.