Millions of RFID tags are pervasively used all around the globe to inexpensively identify a wide variety of everyday-use objects. One of the key issues of RFID is that tags cannot use energy-hungry cryptography, and thus can be easily cloned. For this reason, radio fingerprinting (RFP) is a compelling approach that leverages the unique imperfections in the tag's wireless circuitry to achieve large-scale RFID clone detection. Recent work, however, has unveiled that time-varying channel conditions can significantly decrease the accuracy of the RFP process. Prior art in RFID identification does not consider this critical aspect, and instead focuses on custom-tailored feature extraction techniques and data collection with static channel conditions. For this reason, we propose the first large-scale investigation into RFP of RFID tags with dynamic channel conditions. Specifically, we perform a massive data collection campaign on a testbed composed by 200 off-the-shelf identical RFID tags and a software-defined radio (SDR) tag reader. We collect data with different tag-reader distances in an over-the-air configuration. To emulate implanted RFID tags, we also collect data with two different kinds of porcine meat inserted between the tag and the reader. We use this rich dataset to train and test several convolutional neural network (CNN)-based classifiers in a variety of channel conditions. Our investigation reveals that training and testing on different channel conditions drastically degrades the classifier's accuracy. For this reason, we propose a novel training framework based on federated machine learning (FML) and data augmentation (DAG) to boost the accuracy. Extensive experimental results indicate that (i) our FML approach improves accuracy by up to 48%; (ii) our DAG approach improves the FML performance by up to 19% and the single-dataset performance by 31%. To the best of our knowledge, this is the first paper experimentally demonstrating the efficacy of FML and DAG on a large device population. To allow full replicability, we are sharing with the research community our fully-labeled 200-GB RFID waveform dataset, as well as the entirety of our code and trained models, concurrently with our submission.

Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.

Has now become more important than ever to guarantee an always present connectivity to users, especially in emergency scenarios. However, in case of a disaster, network infrastructures are often damaged, with consequent connectivity disruption, isolating users when are more in need for information and help. Drones may supply with a recovery network, thanks to their capabilities to provide network connectivity on the fly. However, users typically need special devices or applications to reach these networks, reducing their applicability and adoption.

With the ever-growing usage of batteries in the IoT era, the need for more eco-friendly technologies is clear. RF-powered computing enables the redesign of personal computing devices in a battery-less manner. While there has been substantial work on the underlying methods for RF-powered computing, practical applications of this technology has largely been limited to scenarios that involve simple tasks. This paper demonstrates how RFID technology, typically used to implement object identification and counting, can be exploited to realize a battery-free smart home. In particular, we consider the coexistence of several battery-free devices, with different transmission requirements-periodic, event-based, and real-time-and propose a new adaptive and quick-to-learn MAC protocol, called APT-MAC, which dynamically collects information from devices without requiring any a priori knowledge of the environment. Extensive simulations clearly show the benefits of using APT-MAC, which is able to successfully deliver 97.7% of new data samples in complex scenarios, including several high traffic demanding devices, such as joysticks and cameras.

In this paper we address configuration and management issues of smart homes. Current platforms requires the user to deal with several management inconvenience problems, such as increasing devices, operating between devices, and using new devices. From a user perspective, system configuration and management are major issues: ordinary consumers want to use systems performing minimal configuration. To address this issue, we propose a platform, composed of a web application and Software Defined Network (SDN). While the user interacts with an easy-to-use interface on a smart device, the app automatically generates and installs SDN rules. Our platform, besides facilitating configuration and management, results more efficient --- up to 4 times faster --- and reliable --- able to operate even in case of no connection with the cloud --- than current solutions.

This demo presents our experiences in developing a joystick for videogames that uses RFID backscattering for battery-free operation. Specifically, we develop a system to gather data from a wireless and battery-less joystick, named JoyTag, while it interacts with a videogame console. Our system enables consumers to use JoyTag at every moment without caring about charging.