Residual Neural Networks for Heterogeneous Smart Device Localization in IoT Networks
Location-based services assume significant importance in the Internet of Things (IoT) based systems. In the scenarios where the satellite signals are not available or weak, the Global Positioning System (GPS) accuracy degrades sharply. Therefore, opportunistic signals can be utilized for smart device localization. In this paper, we propose a smart device localization method using residual neural networks. The proposed network is generic and performs smart device localization using opportunistic signals such as Wireless Fidelity (Wi-Fi), geomagnetic, temperature, pressure, humidity, and light signals in the IoT network. Additionally, the proposed method addresses the two significant challenges in IoT based smart device localization, which are noise and device heterogeneity. The experiments are performed on three real datasets of different opportunistic signals. Results show that the proposed method is robust to noise, and a significant improvement in the localization accuracy is obtained as compared to the state-of-the-art localization methods.
P. Pandey et al., "Residual Neural Networks for Heterogeneous Smart Device Localization in IoT Networks," Proceedings - International Conference on Computer Communications and Networks, ICCCN, Aug 2020.
The definitive version is available at https://doi.org/10.1109/ICCCN49398.2020.9209703
International Conference on Computer Communications and Networks, ICCCN
Center for High Performance Computing Research
Keywords and Phrases
Deep learning; Device heterogeneity; IoT; Smart device localization
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 Aug 2020