Residual Neural Networks for Heterogeneous Smart Device Localization in IoT Networks

Abstract

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.

Meeting Name

International Conference on Computer Communications and Networks, ICCCN

Department(s)

Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Comments

National Science Foundation, Grant CCF-1725755

Keywords and Phrases

Deep learning; Device heterogeneity; IoT; Smart device localization

International Standard Book Number (ISBN)

978-172816607-0

International Standard Serial Number (ISSN)

1095-2055

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020, All rights reserved.

Publication Date

01 Aug 2020

Link to Full Text

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