ZU-Mean: Fingerprinting based Device Localization Methods for IoT in the Presence of Additive and Multiplicative Noise
This paper proposes Zero-Mean and Unity-Mean (ZU-Mean) features based device localization methods for internet of things (IoT). These features do not depend on the hardwares and/or specifications of the devices being used. Moreover, the zero-mean and unity-mean features mitigate the additive and multiplicative noise, respectively. Extensive real experiments are conducted in two different sites (residential and mall areas) using WiFi received signal strength (RSS) for five weeks. The performance of the proposed methods is better than the absolute RSS based method. We also highlight that the absolute RSS feature cannot be used in calibration-free method and hence, it is not suitable for diverse devices in IoT networks. Additionally, the proposed low-cost method is computationally efficient as compared to the existing methods in the literature.
S. Kumar and S. K. Das, "ZU-Mean: Fingerprinting based Device Localization Methods for IoT in the Presence of Additive and Multiplicative Noise," Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking (2018, Varanasi, India), Association for Computing Machinery (ACM), Jan 2018.
The definitive version is available at https://doi.org/10.1145/3170521.3170536
Workshop Program of the 19th International Conference on Distributed Computing and Networking, Workshops ICDCN '18 (2018: Jan. 4-7, Varanasi, India)
Intelligent Systems Center
Keywords and Phrases
Distributed computer systems; Mobile computing; Phase noise; Additive and multiplicative noise; Calibration-free methods; Computationally efficient; Fingerprinting; Internet of Things (IOT); Localization; Localization method; Wi-fi received signal strengths
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Jan 2018