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.

Meeting Name

Workshop Program of the 19th International Conference on Distributed Computing and Networking, Workshops ICDCN '18 (2018: Jan. 4-7, Varanasi, India)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


The work was supported in part by Science and Engineering Research Board (SERB), Early Career Research project (ECR/2016/001532) titled "Cyber-Physical Systems for M-Health".

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)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2018 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jan 2018