The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

Recently, there is a high interest for appropriate systems and methods to localize the position of people and their devices in indoor environments (e.g. home, office, hospital). From technical and economical points of view, Bluetooth Low Energy (BLE) is one of the promising technologies in this field. This paper deals with the study of performances of this technology for these purposes. For the indoor localization, a received signal strength indication (RSSI) method is used. It was tested in ideal (anechoic chamber) and real (in the office) environment conditions. For the measurement campaign and evaluation of the obtained results a generic framework is proposed. Experimental results (simulation vs. measurement) show similar behaviour of the BLE technology in ideal (no signal reflection) and real (multipath propagation) transmission environments.

Nowadays, Bluetooth Low Energy (BLE) technology has a great attention in the field of wireless localization techniques, especially in the case of indoor scenarios. Study presented in this paper explores BLE localization performance in indoor scenarios based on a received signal strength (RSS). Firstly, we present a Ray-Launching based application to emulate BLE radio frequency (RF) signal propagation in the office environment. Secondly, an appropriate measurement workplace and its setup is proposed to measure RSS values. Obtained results are used to create an RSS-fingerprinting map. Furthermore, accuracy of position determination is calculated and evaluated. Results show advantage of BLE technology for indoor localization purposes (without calibration measurement). However, its performance highly depends on the number of considered BLE nodes and on applied evaluation method (e.g. number of considered sectors).

With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations—the machine-learning-based, localization-based, and behavior-based methods.

This paper presents a new concept of a capacitance multiplier using the topology of differential voltage buffer and current conveyor, where the capacitor is connected to the current input terminal. The presented topology overcomes the typical issue known from similar solutions, i.e. creation of an undesired lossy character of the impedance plot. The added feedback path in the structure serves for minimization of the serial parasitic resistance of the current input terminal as well as the output resistance of differential voltage buffer. The electronic driving of the current and voltage internal gains of the active elements allows the adjustment of the capacitance multiplication factor as well as readjustment of the overall capacitance structure between the lossy and lossless modes of operation. The adjustment of the multiplication factor intentionally targets low ranges of gains. Despite that the multiplication factor equals or is less than 1, the range of adjustability is very wide. Simple modifications of the proposed concept leading to the differential-mode operation and enhancement of the multiplication factor are shown and explored. They were experimentally tested in more than 2 decades, from 0.03 to 5.8 nF, and controlled by single DC voltage from 0.1 to 1.0 V. The outputs of experimental measurements meet with the PSpice simulations and confirm the design validity.