A deep learning‐based indoor‐positioning approach using received strength signal indication and carrying mode information. (28th December 2020)

• Record Type: Journal Article
• Authors: Lin, Szu‐Yin; Leu, Fang‐Yie; Ko, Chia‐Yin; Shih, Ming‐Chien
• Other Names: Awan Irfan guestEditor.; Younas Muhammad guestEditor.; Benbernou Salima guestEditor.; Ogiela Lidia guestEditor.; Leu Fang‐Yie guestEditor.; Fiore Ugo guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 23(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-28
• Subjects: carrying‐mode information -- deep learning -- indoor positioning -- pattern recognition; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6135
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Indoor smartphone positioning is one of the key information and cummunication technology techniques enabling new opportunities for indoor navigation and mobile location‐based services to enrich our everyday lives. Generally, the development of an indoor positioning system heavily relies on wireless sensor network. Since wireless sensors can estimate the probable distance between radio source and the sensors themselves by evaluating the strengths of wireless signals received from radio sources, such as received strength signal indications of Wi‐Fi and Bluetooth. However, the radio signals could be influenced by indoor and outdoor objects, such as walls and furniture, and carrying mode of a user's smartphone, like in‐pocket or in‐backpack. But, according to the best of our knowledge, up to present, people do not know how carrying mode information (CMI) influences the positioning accuracy of a positioning system. Therefore, in this study, we propose an indoor positioning scheme, named LE arning‐based I ndoor P ositioning S ystem (LEIPS), which identifies the carrying mode of a user's smartphone by using this smartphone's inertial sensors and deep learning algorithms, aiming to increase indoor positioning accuracy. Our experimental results demonstrate that this system reaches 96% of positioning accuracy. CMI is also validated, showing that it is able to improve indoor prediction accuracy.