A Pervasive Integration Platform of Low-Cost MEMS Sensors and Wireless Signals for Indoor Localization

Zhuang, Yuan, Yang, Jun, Qi, Longning, Li, You, Cao, Yue and El-Sheimy, Naser (2018) A Pervasive Integration Platform of Low-Cost MEMS Sensors and Wireless Signals for Indoor Localization. IEEE Internet of Things Journal, 5 (6). pp. 4616-4631. ISSN 2372-2541

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Official URL: http://dx.doi.org/10.1109/JIOT.2017.2785338


Location service is fundamental to many Internet of Things applications such as smart home, wearables, smart city, and connected health. With existing infrastructures, wireless positioning is widely used to provide the location service. However, wireless positioning has the limitations such as highly depending on the distribution of access points (APs); providing a low sample-rate and noisy solution; requiring extensive labor costs to build databases; and having unstable RSS values in indoor environments. To reduce these limitations, this paper proposes an innovative integrated platform for indoor localization by integrating low-cost microelectromechanical systems (MEMS) sensors and wireless signals. This proposed platform consists of wireless AP localization engine and sensor fusion engine, which is suitable for both dense and sparse deployments of wireless APs. The proposed platform can automatically generate wireless databases for positioning, and provide a positioning solution even in the area with only one observed wireless AP, where the traditional trilateration method cannot work. This integration platform can integrate different kinds of wireless APs together for indoor localization (e.g., WiFi, Bluetooth low energy, and radio frequency identification). The platform fuses all of these wireless distances with low-cost MEMS sensors to provide a robust localization solution. A multilevel quality control mechanism is utilized to remove noisy RSS measurements from wireless APs and to further improve the localization accuracy. Preliminary experiments show the proposed integration platform can achieve the average accuracy of 3.30 m with the sparse deployment of wireless APs (1 AP per 800 m 2 ).

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 08 Feb 2019 11:47
Last Modified: 11 Oct 2019 14:01
URI: http://nrl.northumbria.ac.uk/id/eprint/37901

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