Improving the Localization Accuracy for Sigfox LowPower Wide Area Networks Sigfox is a new LPWAN (LowPower 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 learningbased 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 pathlosstodistance model for each sector to reflect its sitespecific 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.

DeepLocNet: Deep Observation Classification and Ranging Bias Regression for Radio Positioning Systems WiFi technology has been used pervasively in finegrained indoor localization, gesture recognition, and adaptive communication. Achieving better performance in these tasks generally boils down to differentiating LineOfSight (LOS) from NonLineOfSight (NLOS) signal propagation reliably which generally requires expensivespecialized hardware due to the complex nature of indoor environments. Hence, the development of lowcost accurate positioning systems that exploit available infrastructure is not entirely solved. In this paper, we develop a framework for indoor localization and tracking of ubiquitous mobile devices such as smartphones using onboard sensors. We present a novel deep LOSNLOS classifier which uses the Received Signal Strength Indicator (RSSI), and can classify the input signal with an accuracy of 85%. The proposed algorithm can globally localize and track a smartphone (or robot) with a priori unknown location, and with a semiaccurate prior map (error within 0.8m) of the WiFi Access Points (AP). Through simultaneously solving for the trajectory and the map of access points, we recover a trajectory of the device and corrected locations for the access points. Experimental evaluations of the framework show that localization accuracy is increased by using the trained deep network; furthermore, the system becomes robust to any error in the map of APs.