AlQahtani, Ali Abdullah S.
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A weighting system to build physical layer measurements maps by crowdsourcing data from smartphones Alamleh, Hosam; AlQahtani, Ali Abdullah S.
IAES International Journal of Robotics and Automation (IJRA) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.262 KB) | DOI: 10.11591/ijra.v9i3.pp211-219

Abstract

Mobile devices can sense different types of radio signals. For example, broadcast signals. These broadcasted signals allow the device to establish a connection to the access point broadcasting it. Moreover, mobile devices can record different physical layer measurements. These measurements are an indication of the service quality at the point they were collected. These measurements data can be aggregated to form physical layer measurement maps. These maps are useful for several applications such as location fixing, navigation, access control, and evaluating network coverage and performance. Crowdsourcing can be an efficient way to create such maps. However, users in a crowdsourcing application tend to have different devices with different capabilities, which might impact the overall accuracy of the generated maps. In this paper, we propose a method to build physical layer measurements maps by crowdsourcing physical layer measurements, GPS locations, from participating mobile devices. The proposed system gives different weights to each data point provided by the participating devices based on the data source’s trustworthiness. Our tests showed that the different models of mobile devices return GPS location with different location accuracies. Consequently, when building the physical layer measurements maps our algorithm assigns a higher weight to data points coming from devices with higher GPS location accuracy. This allows accommodating a wide range of mobile devices with different capabilities in crowdsourcing applications. An experiment and a simulation were performed to test the proposed method. The results showed improvement in crowdsourced map accuracy when the proposed method is implemented.