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Learning Wireless Sensor Networks for Source Localization.

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This study introduces efficient Support Vector Machine (SVM) and Twin SVM (TWSVM) algorithms for source localization and target tracking in wireless sensor networks (WSN). These methods offer computationally-cheap solutions for resource-constrained sensor nodes.

Keywords:
Internet of Thingsquadratic programmingregion of event detectionsource localizationsupport vector machine (SVM)target trackingtwin support vector machine (TWSVM)wireless sensor network (WSN)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Source localization and target tracking are critical yet challenging tasks in wireless sensor networks (WSN).
  • Existing advanced solutions often exceed the processing and memory capabilities of low-cost sensor nodes.
  • There is a need for efficient algorithms suitable for resource-limited WSN environments.

Purpose of the Study:

  • To develop computationally inexpensive algorithms for source localization and target tracking in WSN.
  • To leverage machine learning, specifically Support Vector Machine (SVM) and Twin SVM (TWSVM), for WSN applications.
  • To address the limitations of current methods in terms of processing and memory requirements.

Main Methods:

  • Proposed methods utilize Support Vector Machine (SVM) and Twin SVM (TWSVM) learning algorithms.
  • Network nodes first detect the signal of interest.
  • The network is then trained to identify nodes near the source/target, defining the event region, with the centroid estimating the source location.

Main Results:

  • Simulations demonstrate the effectiveness of the proposed SVM and TWSVM based methods.
  • The computationally-cheap solutions are shown to be efficient for source localization and target tracking.
  • The algorithms successfully identify the event region and estimate the source location.

Conclusions:

  • The proposed SVM and TWSVM algorithms provide efficient and computationally inexpensive solutions for WSN source localization and target tracking.
  • These methods are well-suited for low-cost sensor nodes with limited processing and memory.
  • The study validates the practical applicability of these machine learning approaches in WSN.