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Researchers have used several technologies and methods for indoor location determination. Recently research on Indoor Real Time Location Systems (RTLS) identifies Bluetooth Low Energy as one of the technologies promising a suitable response to the needs of the Indoor Location. An indoor environment always has varying numbers and types of obstructions in the transmission path. These negatively affect the reception of Bluetooth transmission. Determining the location of obstacles and their impact will be valuable in improving accurate location determination. This research uses machine learning to predict the location of obstacles in an indoor environment. Obstacles used in this research were at fixed positions. Some obstacles will be in motion in real life, whilst others will change locations. The results indicate that machine learning can provide a valuable contribution to this research area. It must be noted that due to the evolving nature of IoT, improvement resulting from this can further improve indoor location.

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Using Machine Learning for Prediction of Obstructions for Indoor Location Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Jay Pancham,
  • Richard Millham,
  • Simon James Fong

摘要

Researchers have used several technologies and methods for indoor location determination. Recently research on Indoor Real Time Location Systems (RTLS) identifies Bluetooth Low Energy as one of the technologies promising a suitable response to the needs of the Indoor Location. An indoor environment always has varying numbers and types of obstructions in the transmission path. These negatively affect the reception of Bluetooth transmission. Determining the location of obstacles and their impact will be valuable in improving accurate location determination. This research uses machine learning to predict the location of obstacles in an indoor environment. Obstacles used in this research were at fixed positions. Some obstacles will be in motion in real life, whilst others will change locations. The results indicate that machine learning can provide a valuable contribution to this research area. It must be noted that due to the evolving nature of IoT, improvement resulting from this can further improve indoor location.