Localization based on Wi-Fi fingerprinting (WF) necessitates training the radio signals of target areas. Manual training enables good accuracy but requires service providers to conduct thorough site surveys to collect the radio signals of target areas periodically. Several systems are capable of eliminating the training phase by collecting radio signals from users, but these schemes are unable to provide location-based services until enough data are collected from the participatory users. Moreover, the accuracy of such systems is generally worse than that of systems that conduct manual training. In this paper, we propose a radio map management scheme in which the two methods are combined to achieve high accuracy with reduced management costs. The proposed scheme entails only a lightweight site survey for the construction of the initial radio map and does not necessarily require coverage of the entire area of interest. The quality of the radio map is enhanced in terms of both coverage and accuracy through user collaboration. In our system, mobile users conduct automatic war-walking with smartphone-based pedestrian dead reckoning (PDR), and to match the war-walking path to the radio map accurately, we employ a particle filter using both WF and PDR. We also consider the received signal strength variance problem caused by the device type and environmental changes. The proposed scheme is elastic since the service provider can adjust the costs required for the initial site survey depending on the quality of the crowdsensing-based radio map, which would compensate for the lack of coverage and accuracy of the initial radio map. The experiment's result validates that our scheme achieves competitive accuracy and coverage in comparison with systems that conduct full site surveys.
Bibliographical noteFunding Information:
This work was supported by a grant from the National Research Foundation of Korea (NRF), funded by the Korean government , Ministry of Education, Science and Technology under Grant (No. 2013-027363 ).
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications