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

Han Zou, Hao Jiang, Xiaoxuan Lu, Lihua Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Developing Indoor Positioning System (IPS) has become an attractive research topic due to the increasing demands on Location Based Service (LBS) in indoor environment recently. WiFi technology has been studied and explored to provide indoor positioning service for years since existing WiFi infrastructures in indoor environment can be used to greatly reduce the deployment costs. A large body of WiFi based IPSs adopt the fingerprinting approach as the localization algorithm. However, these WiFi based IPSs suffer from two major problems: the intensive costs on manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on online sequential extreme learning machine (OS-ELM) to address these problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey, and more importantly, its online sequential learning ability enables the proposed localization algorithm to automatically and timely adapt to the environmental dynamics. The experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches due to its fast adaptation to various environmental changes.
Original languageEnglish
Title of host publication2014 IEEE World Forum on Internet of Things (WF-IoT)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-1-4799-3459-1
Publication statusPublished - 24 Apr 2014
Event2014 IEEE World Forum on Internet of Things - Seoul, Korea, Republic of
Duration: 6 Mar 20148 Mar 2014


Conference2014 IEEE World Forum on Internet of Things
Abbreviated titleWF-IoT 2014
Country/TerritoryKorea, Republic of
Internet address


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