An online sequential extreme learning machine approach to WiFi based indoor positioning

By Han Zou, Hao Jiang, Xiaoxuan Lu, Lihua Xie. 2014

66 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ala Al-Fuqaha
2. Lihua Xie
3. Han Zou
4. Mohsen Guizani
5. Y. C. Soh
6. Sameh Sorour
7. M. Mohammadi
8. Hao Jiang

In Computer Networks and Communications

1. Mohsen Guizani
2. Ala Al-Fuqaha
3. M. Mohammadi
4. Sameh Sorour
5. Lihua Xie
6. Y. C. Soh
7. Umair Mohammad
8. Ekram Hossain

In Electrical and Electronic Engineering

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Lihua Xie
4. Mohsen Guizani
5. Y. C. Soh
6. Sameh Sorour
7. Hao Jiang

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Zou et al. [34] have also reported using this architecture for an indoor localization algorithm based on WiFi fingerprinting, in which the OS-ELM model can bear well the dynamic environmental changes while still showing good accuracy.”
• Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization (2015, methodology): “The most commonly-applied technique in RSS-based localization is the fingerprinting approach, which requires manual collection of a huge dataset for training [1,2].”