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A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

Sadik Kamel Gharghan1,2, Rosdiadee Nordin3, Mahamod Ismail4

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor 43600, Malaysia. sadiq@siswa.ukm.edu.my.

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Summary

This study introduces soft computing methods for wireless sensor network localization, enhancing bicycle tracking accuracy in velodromes. The hybrid Gravitational Search Algorithm-Artificial Neural Network (GSA-ANN) achieved superior distance estimation for both outdoor and indoor cycling tracks.

Keywords:
WSNcyclingdistance estimationoptimization techniquesoft computing

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Area of Science:

  • Wireless Sensor Networks
  • Soft Computing
  • Localization Techniques

Background:

  • Accurate localization of moving objects is crucial in various applications, including sports analytics.
  • Existing range-based localization methods using Received Signal Strength Indicator (RSSI) in Wireless Sensor Networks (WSNs) face challenges in dynamic environments like cycling tracks.
  • Soft computing approaches offer potential for improving localization accuracy in WSNs.

Purpose of the Study:

  • To propose and evaluate two soft computing techniques for range-based localization in WSNs.
  • To estimate the distance of bicycles on outdoor and indoor velodromes using RSSI measurements.
  • To compare the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and hybrid Artificial Neural Network (ANN) models with optimization algorithms.

Main Methods:

  • Implemented a range-based localization method using RSSI from ZigBee anchor nodes.
  • Developed an ANFIS model for distance estimation.
  • Hybridized ANN with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA).

Main Results:

  • The hybrid GSA-ANN model demonstrated superior performance in localization and distance estimation accuracy.
  • The GSA-ANN achieved a mean absolute distance estimation error of 0.02 m for outdoor velodromes.
  • The GSA-ANN achieved a mean absolute distance estimation error of 0.2 m for indoor velodromes.

Conclusions:

  • Hybrid soft computing techniques, particularly GSA-ANN, significantly enhance localization accuracy in WSNs for dynamic environments.
  • The proposed GSA-ANN method provides a robust solution for accurate bicycle tracking in both outdoor and indoor velodromes.
  • Soft computing offers a promising avenue for improving range-based localization in WSNs, with practical implications for sports analytics.