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Multisensor Data Fusion in IoT Environments in Dempster-Shafer Theory Setting: An Improved Evidence Distance-Based

Nour El Imane Hamda1,2, Allel Hadjali2, Mohand Lagha1

  • 1ASL, Aeronautics and Spatial Studies Institute, Blida 1 University, Blida 09000, Algeria.

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This study introduces an improved Dempster-Shafer (D-S) theory approach for multisensor data fusion in IoT environments. The method effectively manages conflicting data, enhancing decision-making accuracy and reliability.

Keywords:
Dempster–Shafer theoryIoTbelief entropyevidence distancemultisensor data fusionuncertainty

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) environments generate vast amounts of imperfect data, including uncertain, conflicting, or incorrect information.
  • Multisensor data fusion is crucial for integrating heterogeneous data sources and improving decision-making.
  • Dempster-Shafer (D-S) theory is a powerful tool for handling uncertainty but struggles with highly conflicting data.

Purpose of the Study:

  • To propose an improved evidence combination approach for Dempster-Shafer (D-S) theory.
  • To effectively manage both conflict and uncertainty in multisensor data fusion within IoT environments.
  • To enhance the accuracy and reliability of decision-making in IoT applications.

Main Methods:

  • Developed an improved evidence combination approach based on Hellinger distance and Deng entropy.
  • Applied the method to a benchmark target recognition example.
  • Validated the approach using two real-world IoT application cases: fault diagnosis and decision-making.

Main Results:

  • The proposed method demonstrated superior performance in conflict management and convergence speed compared to existing methods.
  • Achieved high accuracy rates: 99.32% in target recognition, 96.14% in fault diagnosis, and 99.54% in IoT decision-making.
  • Fusion results showed increased reliability and improved decision accuracy.

Conclusions:

  • The improved evidence combination approach effectively addresses the challenge of combining contradictory data in D-S theory.
  • The method significantly enhances multisensor data fusion for IoT applications, leading to more accurate decisions.
  • The approach offers a robust solution for managing uncertainty and conflict in complex data environments.