IoT technologies are becoming more pervasive in many critical applications which leads to an increase in the deployment of the popular LoRaWAN (Long Range Wide Area Network) networks. Therefore, it is essential to characterize the network performance and monitor targeted end nodes. In this paper, a passive packet sniffer is proposed that can monitor LoRaWAN at a given geographical location. Using a commercial gateway, a measurement is conducted to passively collect LoRa (Long Range) packets within a duration of one month. From the acquired data, the transmission parameters of the received packets are analyzed. Furthermore, information is extracted from various fields of the frame structure. Then, a Sequence of Contiguous Packets (SCP) is introduced for providing additional packet labeling beyond the Device Address (DevAddr). This original SCP algorithm classifies a stream of packets transmitted by a single end node, based on the similarities between the packets’ parameters. The feasibility of the proposed SCP algorithm is confirmed after presenting statistical analysis and the characteristics of estimated SCPs from the real acquired data.

The growing prevalence of Internet of Things (IoT) technologies in many outdoor applications has led to an increase in the deployment of popular LoRaWAN (Long Range Wide Area Network) networks. As a result, there is increasing interest in tracking end nodes and detecting device transmission behavior. In this paper, a passive packet sniffer is demonstrated to receive LoRa (Long Range) packets in the Campus Beaulieu area of Rennes, France, over a period of two months. After processing the acquired packets, the inter-arrival times between packets of each identified device are estimated properly using a proposed technique, i.e., based on Kernel Density Estimation (KDE). Another algorithm is also employed to detect any pattern in the inter-arrival times. The study is then extended to reveal the spectrum allocation technique of a device and detect any periodic structure in the sequence of the used frequencies. Over and above, the spectrum allocation scheme is modelled as a Markov chain, allowing for the prediction of the next selected frequency using the estimated trajectory. The proposed algorithms are validated through statistical analysis of the inter-arrival times, temporal patterns, and spectrum allocation schemes’ characteristics from the actual acquired data.

The rising concept of reconfigurable intelligent surface (RIS) has promising potential for Beyond 5G localization applications. We herein investigate different phase profile designs at a reflective RIS, which enable non-line-of-sight positioning in nearfield from downlink single antenna transmissions. We first derive the closed-form expressions of the corresponding Fisher information matrix (FIM) and position error bound (PEB). Accordingly, we then propose a new localization-optimal phase profile design, assuming prior knowledge of the user equipment location. Numerical simulations in a canonical scenario show that our proposal outperforms conventional RIS random and directional beam codebook designs in terms of PEB. We also illustrate the four beams allocated at the RIS (i.e., one directional beam, along with its derivatives with respect to space dimensions) and show how their relative weights according to the optimal solution can be practically implemented through time sharing (i.e., considering feasible beams sequentially).

In the Internet of Things (IoT) context such as low power wide area network (LPWAN), it is essential to reduce the packet losses, e.g., to save energy. Decentralized artificial intelligence (AI) techniques have been proposed to combat radio collisions, but the approach here is extended to deal additionally with the channel propagation effects. In this article, a Quality of Channel Allocation (QoC-A) learning technique based on bandit algorithms is proposed in order to choose the transmission channel. This aims to reduce the effect of the propagation impairments between the radio channels while using the effective signal power (ESP) as a quality metric. In addition, a discounted QoC-A (DQoC-A) algorithm is proposed to adapt rapidly to any abrupt change in the channels’ conditions. An experimental campaign on a real IoT device is carried out to demonstrate the low complexity and efficiency of these proposed decentralized algorithms. In the given results, QoC-A outperforms the classical upper confidence bound (UCB) policy with a more accelerated learning process. On the other hand, the feasibility of using the DQoC-A in nonstationary scenarios is illustrated by its rapid convergence when abrupt changes in the channels’ conditions occur. At the end of the process, these proposed learning techniques give 4.1 and 2.4 times fewer packet losses than the traditional ones with a random channel assignment scheme, in the stationary and nonstationary scenarios, respectively.

In this paper, we present a low-latency direction assisted channel estimation algorithm suitable for millimeter wave (mm-Wave) systems. First, during the beam training procedure, we perform angle of departure (AoD) and angle of arrival (AoA) measurements with their corresponding variances and based on them, we design both downlink and uplink beams. We then perform signal measurements with these beams and accordingly design the sensing matrix, while also iteratively refining the angle estimation and the beams for the next measurements accordingly. Finally, exploiting both the sparseness and the intrinsic geometric nature of the mm-Wave channel, we apply compressive sensing tools so as to complete the estimation procedure. Simulations show that the corresponding estimation error decreases rapidly in comparison with other conventional approaches.

We study a millimeter wave (mm-wave) wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave small cell base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.

Time Difference of Arrival (TDoA) is currently viewed as an important technique for the positioning capabilities in the Internet of Things (IoT). However, in the case of practical measurement, not all the TDoA values between the gateways have the same impact on the localization accuracy. In this paper, a novel TDoA pre-processing methodology for dropping out the outlier TDoA values is presented, after instrumentalizing a paired Cramér-Rao lower bound (CRLB). Thus, the proposed approach is detecting the best TDoA values, which have the lowest paired CRLB values specifically, in the vicinity of the guessed node location, based on a robust thresholding method. A comparison is performed investigating the attainable accuracies for localizing based on this pre-processing algorithm, on a well-defined simulation environment. This simulator is based on a Poisson distribution approach for defining the gateways and node positions, as well as a noise model for emulating the timestamp imperfections. In the given results, the feasibility of the proposed technique is asserted by a drastic improvement over a wide range of the number of gateways as well as measurement noise variances. This manifests the robustness of the contributed method to the outlier TDoA values and its valuable rendering.

Received Signal Strength Indicator (RSSI)-based fingerprinting is currently viewed as an important technique for the positioning capabilities in the Internet of Things (IoT). However, in the case of practical measurement, the localization methods based on RSSI are easily affected by the temporal and spatial variation, which contributes to most of the estimation errors in current systems. In this paper, the feasibility of utilizing the Channel State Information (CSI) for localization is studied, after knowing that the CSI contains information about the channel between the sender and receiver at the level of individual data subcarriers. Unlike most of the previous work, the intended approach is to use the entire subcarrier magnitudes without averaging or any reduction of the obtained narrowband CSI. Moreover, the frequency hopping in the LoRa systems should be a profit for localization by getting access to a wider band. In order to obtain a reliable basis for this approach, an outdoor measurement campaign is performed in the area of the Campus Beaulieu in Rennes to estimate the CSI of transmitted LoRa signals from different locations. For this, it is necessary for the individual channels from each different position to be appropriately different from one another to achieve significant localization gain. Hence, a comparison is done investigating the attainable evolution in the CSI at each location based on the CSI slope versus its average amplitude. In the given results, the feasibility of using the proposed technique is asserted by the drastic stability of the CSI slope over time and space, in contrary to the CSI average amplitude. This manifests the robustness of the CSI to the signal fluctuations and its more valuable rendering than the RSSI.

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location and rotation angle of the user terminals are not well investigated. A joint heuristic beam selection and user position and orientation tracking approach is proposed. First, the user location is tracked in the uplink by joint beam selection together with time-of-arrival (TOA) and angle-of-arrival (AOA) tracking at the base station (BS). Then, the user rotation angle is obtained using the location information by joint beam selection and tracking at the mobile station (MS). The beam selection, TOA and AOA tracking, at the BS and MS are performed during the data transmission phase. Numerical results demonstrate that the proposed method performs close to the estimated position and rotation angle in the training phase with reduced complexity and reduced number of required pilots for the estimation.

In this paper, we present a method to analyse the millimeter-wave (mm-wave) radio channel scatterers applied to channel sounding at 32 GHz in an urban microcell (UMi) environment in the city of Espoo (Finland). The proposed method is based on a geometric single bounce assumption for recovering the scatterer positions. A simple formulation exploiting the discovered scatterer positions allows quantifying the excess-loss to free space for each scatterer. We found that large building walls, as well as small urban furniture such as metallic sign-board reflectors, are objects producing reflections and strong scattering. Finally, their radar cross-section and equivalent scattering area are estimated.

In this paper, we propose optimal beamforming strategies for a millimeter wave (mmWave) system consisting of multiple users based on the localization performance bounds. We consider a single base station (BS) with prior coarse knowledge of the users' positions and formulate the optimal beamforming problem in order to minimize the localization error consisting of Cramer Rao Lower Bounds (CRLBs) of delay, angle of departure (AoD) and angle of arrival (AoA) estimation at the mobile users. We first formulate the simplified CRLB of estimation parameters, taking advantage of multiple sub-carriers, and then formulate the localization error for optimization of the beamformer. Finally, we evaluate the resulting position and orientation error bounds after optimization for several fairness strategies through Monte Carlo simulations.

We analyze a millimeter wave network, deployed along the streets of a city, in terms of positioning and downlink data-rate performance, respectively. First, we present a transmission scheme where the base stations provide jointly positioning and data-communication functionalities. Accordingly, we study the trade- off between the localization and the data rate performance based on theoretical bounds. Then, we obtain an upper bound on the probability of beam misalignment based on the derived localization error bound. Finally, we prescribe the network operator a scheme to select the beamwidth and the power splitting factor between the localization and communication functions to address different quality of service requirements, while limiting cellular outage.

In this paper, we propose a geometric positioning technique for cooperative localization in Wireless Networks. The proposed technique is based on building geometric constraints from radio observables. These constraints are then merged to estimate the true position. In this paper we address situations where blind nodes, due to the lack of radio observables, cooperate to estimate their positions. Compared to other techniques, Monte Carlo simulations illustrate interesting performances in terms of positioning accuracy and computation complexity and runtime.

Impulse Radio - Ultra Wideband (IR-UWB) ranging based on first detected path's Time-of-Arrival (ToA) theoretically enables high-accuracy localization in Line of Sight (LoS) contexts. However, performance can be severely degraded in practical indoor environments due to both dense multipath propagation and signal obstructions. To mitigate these effects, the space-time correlation of tracked multipath components (MPCs) under mobility can be beneficial to collectively reconstruct the missing direct path's ToA in case of detected NLoS situation. This paper aims at improving further the robustness of such Multipath-aided Direct Path Reconstruction (MDPR) in very dense multipath and generalized NLoS with respect to anchors, while considering constraints of real integrated IR-UWB receivers. More specifically, new strategies are introduced to better approximate and dynamically adjust the link between direct and secondary paths for longer-term correction validity, as well as to handle MPCs appearance/disappearance over NLoS portions of trajectory. The proposed solution is validated by means of semi-deterministic dynamic simulations based on IEEE 802.15.4a channel models.

In wireless decentralized networks, security and privacy become more challenging due to stringent architecture and sometimes low-complexity constraints. Confidentiality of the transmissions is usually achieved by symmetric cryptography, which implies the distribution of identical secret keys to the legitimate nodes. Classical solutions for symmetric key distribution can be too complex from a computational or infrastructural point of view. An alternative would be physical layer key generation, which exploits the radio channel as a source of common information. This paper looks into the generation of secret keys from Impulse Radio - Ultra Wideband (IR-UWB) channels using low-power integrated devices. We investigate the possibility to generate identical secret bit sequences in practical environments (e.g., possibly occupied indoor rooms) and operating conditions (e.g., mobility) under specific hardware limitations inherent to device integration (e.g., time resolution and signal dynamics). The main challenge is to obtain reciprocal and random bits at an acceptable rate. Taking into account these three criteria (i.e., reciprocity, randomness, and key length), we describe and evaluate a new quantization scheme adapted to the channel impulse responses acquired by real devices.

In the specific context of wearable wireless networks, this work aims at improving the accuracy and the robustness of radiolocation solutions based on standard narrow-band radio technologies for user-centric mobile applications. The proposed solution takes benefits from off-body or body-to-body radio links, with respect to fixed elements of infrastructure or to other mobile equipped subjects, respectively. The main idea is to infer relative angular information between the carrying body's heading and the median direction of arrival of received signals. For this sake, we rely on differential received power measurements issued at judiciously placed on-body nodes. Light calibration procedures (e.g., based on the full-scale dynamics of observed measurements) and joint absolute position/orientation estimation algorithms then enable to cover both individual/non-cooperative and collective/cooperative navigation needs. The performance is assessed by means of field experiments with IEEE 802.15.4-compliant integrated devices operating at 2.4 GHz and an optical motion capture system delivering the ground truth reference. On this occasion, our proposal is shown to be resilient against mobile Non-line of Sight (NLoS) situations (e.g., in crowded environments).

In this paper we introduce methods that strengthen the identity of end-devices in order to prevent impersonation attacks in wireless ad hoc networks. Pseudonyms are locally generated from Received Signal Strength Indicators (RSSI) for narrow band IEEE 802.15.4 standard or Round Trip - Time of Flight (RT-ToF) measurements (optionally, along with relative clock drift estimates) for Impulse Radio - Ultra Wideband (IR-UWB) technology. These radiolocation metrics are converted into range measurements, quantized and then fed into hash functions to produce pseudonyms. For the two benchmarked radio technologies, practical trade- offs are illustrated depending on the input measurement accuracy under different channel assumptions. The evaluated solution enables to securely guess the pseudonyms of trusted neighbors with no information leakage. It also achieves advantageous low probability of successful attacks based on brute-force or statistics-aided strategies or compared to other impersonation detection strategies (e.g. RSSI history monitoring).

Symmetric secret key generation from physical layer measurements has recently emerged as an alternative to computationally secure protocols or predistribution techniques for key sharing. This new approach takes advantage of the intrinsic properties of radio channels (e.g. reciprocity and spatial decorrelation) by quantizing measurable channel characteristics into a suite of codewords. However, noise or other transmission artifacts can degrade the link reciprocity and lead to bit errors between the users' codewords, which are strongly related to the quantization thresholds. In this paper, we present a framework for quantization in the context of key generation. In order to find a trade-off between reciprocity (an indicator of robustness) and codeword diversity (an indicator of randomness) under a fixed-length constraint, we investigate the non-uniform quantization and we show the interest of optimizing the quantization thresholds as a function of the signal-to-noise ratio.

This paper describes a work realized for On-Body antennas characterization: the 3D deterministic modeling of a measured antenna disturbed by a dielectric circular cylinder of finite length. This prediction model is based on the ray-tracing technique for the electromagnetic wave paths search and the Uniform Theory of Diffraction (UTD) for the modeling of the electromagnetic waves interactions with the cylinder. After a detailed description, the model is validated in 3D with measurements made for an antenna disturbed by a cylindrical phantom. Indeed, the presented model gives results very close to these measurements. These first validations allow the presented model to be implemented into more complex WBAN (Wireless Body Area Networks) propagation models.

Generating secret keys in mobile wireless networks is considered a challenging problem where a key management infrastructure is not always available. Recent security methods have shown that secret keys can be generated using Ultra Wide Band (UWB) channels. These solutions rely on relevant channel properties such as reciprocity and spatial decorrelation. Accordingly, the radio channel responses can be used as common information to derive secret keys shared by legitimate parties. However, novel studies in the field of UWB channel prediction have demonstrated that channel profiles could be reliably inferred using for instance Ray-Tracing tools. This paper explores this technique to perform attacks and to evaluate the security of UWB secret key generation methods. The main observation here is that it is difficult for a third party to obtain the exact channel responses; thus to retrieve the secret keys. The robustness of UWB key generation methods then depends on the complexity for attackers to describe precisely the physical environment and on the post processing methods to agree on the same key (i.e., quantization, erroneous bits detection, etc.).

The use of multiplicative beamforming is described to improve the azimuthal resolution of existing high-frequency surface-wave radar (HFSWR). An existing method by Davies and Ward is used to improve the resolution by a factor of more than two for HFSWR with 16 antennas. The long integration time of HFSWR suppresses the cross products which are the usual drawbacks of this process. The concept is then validated on experimental data with synthetic targets.

A novel photonic analog-to-digital converter (ADC) architecture tailored for the localization of transmitters employing ultra-wide band (UWB) radio technology is proposed and experimentally demonstrated in this paper. The proposed photonic-ADC architecture comprises several parallel branches each including an electro-optic sensor at different spatial locations capturing UWB signals simultaneously (sub-picosecond resolution). A single optical source is required in this architecture for all electro-optic sensors and also a single processing unit is needed to process the optically sampled data, thus simplifying the system implementation. A five-branch photonic-ADC has been developed for the experimental demonstration. The laboratory measurements demonstrate the successful detection and localization of fully-standard UWB transmitters as in WiMedia specification for very-low power radio signals (-65 dBm received power) captured. A mean three-dimensional positioning error of 48.5 cm with 15 cm standard deviation is achieved.

This paper investigates the impact of realistic antennas on joint anchor-less localization and indoor characterization based on Impulse Radio (IR) Ultra Wideband (UWB) communications. In this frame, the Maximum Averaged Likelihood (MAL) algorithm and its extended version are considered, both relying on a tree approach consisting in two stages. The first part of the process, which is common to both algorithms, exploits the cross-correlation between received and locally predicted paths. The second stage calculates the averaged likelihood of measured path parameters obtained in the previous step, but different measurements are used for MAL and extended MAL (eMAL). In the first algorithm, only the Angle of Incidence (AoI) and the Time of Arrival (ToA) are considered, whereas the eMAL tree algorithm also accounts for two couples of Angles of Departure (AoDs) and Angles of Arrival (AoAs). The estimation errors of both nodes coordinates and room dimension obtained with the two algorithms are compared for three realistic UWB antennas. Finally, the remaining algorithm-independent ambiguities (i.e. resulting from scenario and geometry) are discussed.

This paper presents an algorithm that enables anchor-less localization for pairs of static Impulse Radio - Ultra Wide Band (IR-UWB) devices in a room. The estimation progresses through a tree, as the most significant received paths are successively considered. Unlikely path parameters estimates are progressively rejected, relying on cross-correlation (i.e. with respect to predicted waveforms), as well as on a Maximum Averaged Likelihood (MAL) criterion. The latter is based on the measurements of Angle of Incidence (AoI) and Time of Arrival (ToA) for each detected multipath component, in addition to one range measurement. Once intrinsic mirror and transmitter/receiver ambiguities are solved, in the lack of antennas array and reference landmarks, a positioning error lower than 0:6 m and a room dimension error lower than 2% are obtained for 95% of estimates.

In this paper, localization based on Received Signal Strength (RSS) is investigated assuming a path loss log normal shadowing model. On the one hand, indirect RSS-based estimation schemes are investigated; these schemes are based on two steps of estimation: estimation of ranges from RSS and then estimation of position using weighted least square approximation. We show that the performances of this type of schemes depend on the used estimator in the first step. We suggest that typical median estimator must be replaced by maximum likelihood estimator (mode) to enhance the positioning accuracy. On the other hand, a new direct RSS-based estimation scheme of position is proposed; Monte Carlo simulations show that the new estimator performs better than indirect estimators and can be reliable in future hybrid localization systems.

This paper presents an algorithm that interprets the waveforms associated with received mutipath components in order to estimate their Direction of Arrival (DoA), Direction of Departure (DoD) and Incident Angle to the Indoor Surface (IAIS) in the context of Impulse Radio - Ultra Wide Band (IR-UWB) communications. The proposed solution combines a deterministic description of the filtering effects of antennas and materials with a simple statistical model for IAIS. Iteratively, a set of jointly estimated path parameters is chosen according to the cross- correlation values obtained with currently resolved paths and predicted waveforms. Following an estimation tree approach, impossible sets are progressively discarded. An Estimation Quality Indicator is defined, in order to handle aberrant configurations made up of five single-bounce estimated paths. We show here that the IR-UWB transmission technology, which benefits from fine resolution capabilities, looks particularly suitable to the discussed algorithm. Assuming known walls material, simulation results are provided to illustrate the performance of such a technique in a simplified peer-to-peer multipath scenario. The proposed solution discloses new perspectives for context-awareness through classical communication links.

In this paper, we investigate the use of multiple-input multiple-output (MIMO) techniques with linear precoded orthogonal frequency division multiplexing (LP-OFDM) waveform for high data rate ultra-wideband (UWB) systems. This scheme is an evolution of the multiband OFDM (MB-OFDM) solution supported by the WiMedia Alliance. The aim of this paper is to obtain a very high data rate of around one gigabit for home access networks (HAN) and to improve the system range for lower data rates, while not significantly increasing the system complexity compared to the WiMedia solution. Firstly, a single- input single-output (SISO) LP-OFDM system study is led to highlight the benefits of adding a preceding function to an OFDM signal in the UWB context. In an analytical study, different system choices and parameterization strategies are proposed in order to minimize the mean bit-error-rate (BER) and consequently improve the system range. Secondly, a MIMO scheme is added and global system simulations are performed on a proposed new geometric statistic MIMO channel model. We show that the proposed system can considerably improve the system range at low data rates, and can reach very high data rates up to 1 Gbit/s with comparable BER performances to WiMedia.

This paper presents an algorithm that interprets the specificities of the received waveforms associated with well-isolated multipath echoes, depending on the type of interaction and on the direction-dependent signal deformations due to antennas. The goal is to jointly estimate Direction of Arrival (DoA), Direction of Departure (DoD) and reflecting material for simple-bounce interaction in a peer-to-peer Impulse Radio - Ultra Wide Band (IR-UWB) communication context. The proposed technique is mainly based on prior antenna characterisation and on prediction of site-specific filtering effects due to simple electromagnetic interactions with environment (i.e. simple-bounce reflections). Simulation results are provided to illustrate the expected performance of such a system. The proposed technique discloses new perspective for context-aware services in wireless networks.