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

Railway monitoring system using optical fiber grating accelerometers

Optimal operation, reduced energy consumption, longer service availability, and high safety level are the major concerns in today's railway transport systems. Smart monitoring systems should address these issues without interrupting railway operability. Many successful works have been carried out to provide railway monitoring functions using fiber Bragg grating (FBG) sensors on rail. Most of them are based on strain measurement due to the train passage. This paper presents a highly sensitive means for railway monitoring based on vibration measurement. FBG accelerometers placed on sleeper have been employed as sensor heads, which significantly facilitated the field sensor installation work compared to the positioning on the foot of the rail. An optimized signal demodulation algorithm has been effectively used to extract from the accelerometer traces both the axle number and the average speed information. Excellent capability of the developed system to obtain both parameters has been demonstrated by the way of field trials carried out on a Belgian railway line, during its normal operation. Easy installation, multi-function diagnosis, good data integrity, and compatibility with fiber optic sensors make the proposed sensor a good candidate for railway monitoring applications.INOGRAMS (Innovations for a Global Rail Management System) project of Wallonia (Belgium) 7171 and TUBITAK (BIDEB-2219-1059B191600612

Europe and the future for WPT COST action IC1301 team

This article presents European-based contributions for wireless power transmission (WPT), related to applications ranging from future Internet of Things (IoT) and fifth-generation (5G) systems to high-power electric vehicle charging. The contributors are all members of a European consortium on WPT, COST Action IC1301. WPT is the driving technology that will enable the next stage in the current consumer electronics revolution, including batteryless sensors, passive RF identification (RFID), passive wireless sensors, the IoT, and machine-to-machine solutions. The article discusses the latest developments in research by some of the members of this group