MultiRadio Perspectives for Massive MTC Localization: Energy Consumption and Utility The value of the location data for the Internet of Things (IoT) devices is undisputed, and todayxe2x80x99s radio technologies provide various means to obtain these data. In this paper, we shed light on the utility of the four illustrative radiobased localization techniques, including satellite navigation and the use of BLE, LoRaWAN, and CatNB1 communication technologies. First, we instrument a prototype device comprising all these solutions, thus confirming the feasibility of multiradioxe2x80x93 enabled IoT devices supporting localization. Then, we employ this prototype to empirically assess the energy utility of different localization approaches. Our results demonstrate that in terms of energy consumption, the difference between these techniques approaches five orders of magnitude. By conducting a followup survey of the relevant research papers, we assess other important performance metrics, such as the operating environment and the required infrastructure, as well as their effect on the localization accuracy. By combining these results, we show that multiradioxe2x80x93 enabled localization solutions are more flexible than those based on a single localization solution and provide space for further optimizations, especially with regards to energy consumption. Development of such devices is an crucial step toward enabling xe2x80x9canytime, anywherexe2x80x9d localization for IoT devices.

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.