Novel Differential r-Vectors for Localization in IoT Networks
Department of Science and Technology, Ministry of Science and Technology, India
Fourth Author's Department
Mechanical Engineering Department
Find in your Library
IEEE Sensors Letters
Wireless fidelity received signal strength (RSS) fingerprints are widely used for localization. However, device heterogeneity and temporal variation in the RSS values are the bottlenecks for accurate localization in the Internet of Things (IoT) networks. This letter addresses these challenges by proposing a novel differential $r$-vectors which are the device-invariant signature of a particular location and are invariant, even with temporal RSS variations. The proposed network addresses the vanishing gradient problem which occurs in existing deep learning based localization methods, and hence, the localization accuracy improves. We evaluate the proposed method on two real-world datasets comprising device heterogeneity and temporal RSS variations. Additionally, the proposed method outperforms state-of-the-art fingerprinting-based methods that address these issues.
(2021). Novel Differential r-Vectors for Localization in IoT Networks. IEEE Sensors Letters, 5(6),
Tiwary, Piyush, et al.
"Novel Differential r-Vectors for Localization in IoT Networks." IEEE Sensors Letters, vol. 5,no. 6, 2021,