Improving the Localization Accuracy for Sigfox Low-Power Wide Area Networks

Abstract

Sigfox is a new LPWAN (Low-Power Wide Area Networks) wireless technology proposed for IoT applications. Its transmit power is only 22.5 dBm, but its wireless transmission range can be up to 30 km in open space. In this paper, we propose a new machine learning-based localization method to predict the location of a Sigfox module. Our method divides the space around each base station into a few sectors and trains a more accurate path-loss-to-distance model for each sector to reflect its site specific multipath propagation environment. Our experimental results measured in a big city show that the maximum distance errors of our method are much smaller than those of the official Sigfox localization service in many cases.