A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification.

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.

Full monitoring for long-reach TWDM passive optical networks.

This paper presents a novel and simple fiber monitoring system based on multi-wavelength transmission-reflection analysis for long-reach time and wavelength division multiplexing passive optical networks. For the first time, the full localization functionality of long-reach passive optical networks is possible with the proposed monitoring scheme, including supporting fault detection, identification, and localization in both feeder and distribution fiber segments. By measuring the transmitted and reflected/backscattered optical powers launched by an unmodulated continuous-wave optical source, the proposed solution is able to supervise the network with good spatial accuracy, a high detection speed and a low impact on data traffic. Both the theoretical analysis and experimental validation show that the proposed scheme is capable of providing an accurate fault monitoring functionality for long-reach time and wavelength division multiplexing passive optical networks.

Review of Trackside Monitoring Solutions: From Strain Gages to Optical Fibre Sensors.

A review of recent research on structural monitoring in railway industry is proposed in this paper, with a special focus on stress-based solutions. After a brief analysis of the mechanical behaviour of ballasted railway tracks, an overview of the most common monitoring techniques is presented. A special attention is paid on strain gages and accelerometers for which the accurate mounting position on the track is requisite. These types of solution are then compared to another modern approach based on the use of optical fibres. Besides, an in-depth discussion is made on the evolution of numerical models that investigate the interaction between railway vehicles and tracks. These models are used to validate experimental devices and to predict the best location(s) of the sensors. It is hoped that this review article will stimulate further research activities in this continuously expanding field.

Localization and quantification of reflective events along an optical fiber using a bi-directional TRA technique.

We report on the theory and the implementation of a novel approach for the detection and localization of a reflective event along a fiber link. By launching a continuous-wave signal into both fiber ends and by analyzing the transmitted and reflected/backscattered optical powers, it is possible to localize an optical event and to quantify the induced insertion and return losses simultaneously. The novel idea of utilizing bi-directional measurement allows the localization of both reflective and non-reflective events. Theoretical and experimental studies show that for a 10 km-long single mode fiber, the localization accuracy can be in the range of 5.0 m.

Multi-wavelength Transmission-Reflection Analysis for fiber monitoring.

We propose and implement a novel approach based on multi-wavelength Transmission-Reflection Analysis (MW-TRA) technique for monitoring lossy events (e.g. disconnected connectors, fiber breaks and fiber bendings) along an optical fiber link. By launching un-modulated continuous-wave lights carried by different wavelengths into the fiber and measuring their transmitted and reflected/backscattered optical powers, our proposed MW-TRA scheme is able to localize any lossy event (including both reflective and non-reflective) and to quantify the corresponding insertion and return losses with high accuracy.