Where are You? Localising Stationary Nodes with Limited Information LowPower wireless technology has allowed the free and flexible deployment of embedded systems in a variety of scenarios. As more and more systems get deployed and an increasing number of devices are introduced in scenarios with already existing infrastructures, it becomes increasingly important to understand system properties with limited information and intervention from the user. In this paper, we focus on understanding the positioning of wireless devices in an indoor environment with use of connectivity information and a physical map of the scenario as well as optional anchor devices to further increase accuracy. With limited information, we explore how the location of devices can be estimated through the use of forcedirected graphs augmented with specific domain knowledge. By looking at different indoor testbeds and challenging simulated scenarios, we investigate to what extent we can infer the correct positioning. We discuss the assumptions that needed to be made, thus exploring to what extent our approach can be employed in practice. In conclusion, this paper shows that it is possible to position devices correctly with only limited user intervention, having the network identify the displacement of its own components and supporting further reasoning about system properties.

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.