International audience

International audience

Recent advances in radio frequency fingerprint identification (RFFI) show promise in enhancing the physical-layer security that is crucial for Internet of Things (IoT) devices. However, current methods rely on deep learning, which is resource-intensive for embedded devices. Convolution layers are widely used for RFFI and are the reason these methods are computationally complex. This paper calls into question the utility of these layers for RFFI and demonstrates that networks without convolutional layers, such as fully connected neural networks (FCNNs), are highly effective. When evaluated on 4 state-of-the-art RFFI datasets, it is found that the selected FCNNs require at least five times fewer floating-point operations (FLOPs) and four times less inference time than classical deep learning architectures. Depending on the dataset, the loss in F1-score ranges from 0.03% to 0.89% compared to the best-performing networks.

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by hardware flaws. These flaws may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce a virtual database and suggest utilizing it for the examination of three crucial aspects when creating a RFF database: the number of signals required to perform DL classification, the impact of RFF similarities between emitters, and the propagation channel impact in static and dynamic contexts. For instance, such analysis shows that data augmentation with 10 channels improves accuracy classification up to 70% in a scenario where RFFs are close from a transmitter to another.

Radio-Frequency Fingerprint Identification (RFFI) shows promise for enhancing wireless identification security through unique emitter imperfections. However, this method, which primarily relies on deep learning, faces challenges for a real-time deployment on edge devices. Both memory and computational resources are indeed presented as the major obstacle of such deployment. To address these issues, this paper proposes to investigate the behavior of compressed convolutional neural networks when using unstructured pruning in a data-free scenario, on several public datasets. Indeed, unstructured pruning exhibits significant compression capabilities while preserving performance with minimal computation. We show that state-of-the-art RFFI neural networks can be pruned by up to 70% of the weights, while maintaining F1-Scores above 99%, without retraining. Additionally, we advocate the use of data from a later day, referred to as resilience, as an additional indicator of network performance.

This paper proposes to use Tangled Program Graph (TPG) for Radio Frequency Fingerprint (RFF) identification. RFF is a unique signature created by electromagnetic distortions of the different radio frequency hardware components in the device. This signature is contained in the signal and may be used as a secure identifier because it can not be easily spoofed. In recent years, RFF identification is mainly based on Deep Learning (DL). TPG is a new machine learning technique based on genetic evolution, which are less complex than DL. In this paper, we propose to use TPG-based classification to achieve a lightweight and accurate RFF identification scheme. Results show that TPGs achieve the same accuracy as a state-of-the-art convolutional neural network with a learning phase duration clearly reduced on the CPU. TPGs are also used to analyse both the impact of the channel and the receiver radio on the accuracy.

Synchronized MAC protocols are now considered as the ultimate solution to access the medium in wireless sensor networks. They guarantee both high throughout and constant latency and achieve reasonable energy consumption performance. However, synchronization is achieved at the cost of a complex framework with low flexibility on its parameters that is not suitable for some network topologies or application requirements. By contrast, asynchronous MAC protocols are versatile by nature but suffer from the tradeoff between energy consumption and latency. However, the addition of Wake-up Radio (WuR) can reduce the energy consumption of such protocols while maintaining very low latency thanks to its always-on feature and ultra-low power consumption. In this article, we present WuR-based Multi-hop Multi-channel (W2M), an asynchronous MAC protocol for wireless sensor networks. We also provide a fair comparison with Time Synchronized Channel Hopping (TSCH) through an extensive simulation campaign based on Contiki-NG and Cooja. Our results show that in low traffic scenarios, W2M outperforms TSCH in reducing both the energy consumption and the latency (at least 68% of energy is saved), but at the cost of slightly lower reliability.

A substantial part of the research on wireless sensor networks is focused on the optimization of the energy consumption through either hardware or protocol communication stacks. Wake-up Receivers (WuRs) represent a new paradigm that offers both ultra low power consumption and low latency through asynchronous communications. However, WuRs have a low sensitivity and thus can misinterpret the received signal inducing a performance degradation of the whole communicating system. To tackle this issue, low power channel coding techniques can be used and we propose in this work to apply Hamming coding and Minimum Energy Coding (ME) to enhance WuR range. A performance study of these two types of coding shows that ME coding outperforms Hamming code in reducing both bit error rate and energy consumption. At a range of 28 m, ME coding saves about 3 times the energy at a bit error rate of 10−3compared to uncoded scheme. Furthermore, experimentation on the missed wake-ups when applying ME coding was done, showing a gain of 22% in reliability compared to uncoded scheme.

Wake-up Receivers (WuRs) represent one of the most promising solutions for allowing an ultra low power consumption in wireless sensor networks. However, WuRs have several limitations such as low sensitivity, inducing a miss-interpret of the wake-up signal, and thus a performance degradation of the whole system. This work introduces two complementary schemes, namely minimum energy coding and preamble filtering, in order to enhance the WuR reliability while being energy efficient. It is shown through experimental measurements an enhancement on the reliability up to 22% and a total energy saving of 42% while applying minimum energy coding. Moreover, a significant reduction of the false wake-up is realized through preamble filtering.

In the last years, Internet of Things (IoT) grew up in an exponential behavior and required long range and low power wireless transmissions. Several standards were proposed and LoRa has emerged as a high potential candidate for many IoT solutions. LoRa modulation is based on a chirp spread-spectrum technique and offers efficient transmission up to 50 kbps over several kilometers. Although the principle is known and studied for decades now, the performance in terms of symbol or bit error probability has been theoretically analyzed in few papers only. Closed-form approximations for additive white Gaussian noise and Rayleigh fading were recently proposed. In this paper, we propose a new approach based on Marcum function. Simulations and comparisons with the state of the art show that the proposed approximation of the Binary Error Probability is up to ten times more accurate for a full SNR range.

Wireless sensor systems represent reliable platforms for monitoring and managing of a variety of applications for health care, civil, and military environments. As most of these systems are energy constrained, an accurate control of the energy consumption is required to well design the system. The main focus of this paper is the accurate energy modeling of Wireless Body Area Networks. To this end, we discuss the propagation channel model at 2.4 GHz between two body sensors placed on the human body. This model combines path losses in free space and on human body. Then, an analytical energy consumption model is derived by including channel characteristics such as the proposed path loss model, small scale fading and signal to noise ratio. The model is validated by measurements performed with patch antennas on human subjects. Our results provide interesting insights about the effect of on-body propagation on the global path losses. Moreover, with the proposed approach, the correlation between propagation channel features and energy efficiency is highlighted.

Emerging Low Power Wide Area Networks (LPWAN) represent a real breakthrough for monitoring applications, since they give the possibility to generate and transmit data over dozens of kilometers while consuming few energy. To further increase the autonomy of such wireless systems, the present paper proposes an original methodology to correctly dimension the key elements of an energy autonomous node, namely, the supercapacitor and the battery that mainly give the form factor of the node. Among the LPWAN candidates, LoRa is chosen for real field experiments with a custom wireless platform that proves its energy neutrality over a finite horizon. Different LoRa configurations are explored, leading to adequate dimensioning. As an example, it is shown that, for the same quality of service, the size of the solar panel needed to keep a LoRa node autonomous in the South of France is less than half of the size required in North of France.

Energy harvesting technologies are constantly evolving to help power sensor network nodes. Ranging from miniature power solar panels to micro wind turbines, nodes still express a deep need to harvest energies in order to keep both good performance level and energy autonomy. Recently, the simultaneous use of multiple sources has been proposed to tackle the time-varying characteristics of certain sources that can induce energy scarcity period and thus alter the node performance. In this context, this paper presents a methodology aimed at classifying the energy sources to choose the most efficient energy manager. As sensor nodes are embedded devices, it is necessary to ensure a balance between computational effort and classification accuracy. Feature extraction and selection phases can be processed and analyzed offline before deployment, and only a subset of features will be needed by the nodes to achieve efficient energy management. Simulations on real energy traces show that the proposed approach achieves classification accuracy higher than 95% through the computation of 4 features only.

Intelligent connected objects, which build the IoT, are electronic devices usually supplied by batteries that significantly limit their life-time. These devices are expected to be deployed in very large numbers, and manual replacement of their batteries will severely restrict their large-scale or wide-area deployments. Therefore energy efficiency is of the utmost importance in the design of these devices. The wireless communication between the distributed sensor devices and the host stations can consume significant energy, even more when data needs to reach several kilometers of distance. In this paper, we present an energy-efficient multi-sensing platform that exploits energy harvesting, long-range communication and ultra-low-power short-range wake-up radio to achieve self sustainability in a kilometer range network. The proposed platform is designed with power efficiency in mind and exploits the always-on wake-up radio as both receiver and a power management unit to significantly reduce the quiescent current even continuously listening the wireless channel. Moreover the platform allows the building of an heterogeneous long-short range network architecture to reduce the latency and reduce the power consumption in listening phase at only 4.6 μW. Experimental results and simulations demonstrate the benefits of the proposed platform and heterogeneous network.

In the last years, the integration of specialized hardware accelerators in Multiprocessor System-on-Chip (MpSoC) led to a new kind of architectures combining both software (SW) and hardware (HW) computational resources. For these new Heterogeneous MpSoC (HMpSoC) architectures, performance and energy consumption depend on a large set of parameters such as the HW/SW partitioning, the type of HW implementation or the communication cost. Design Space Exploration (DSE) consists in adjusting these parameters while monitoring a set of metrics (execution time, power, energy efficiency) to find the best mapping of the application on the targeted architecture. With the shift from performance-aware to energy-aware designs, computer-aided design and development tools try to reduce the large design space by simplifying HW/SW mapping mechanisms. However, energy consumption is not well supported in most of DSE tools due to the difficulty to fast and accurately estimate the energy consumption. To this aim, this work introduces a DSE method based on an analytical power model to circumvent the computation time bottleneck of state-of-the-art DSE methods. This exploration method proposes to optimize the HW/SW partitioning and mapping under user-defined objectives, especially an energy constraint. It targets tiling-based parallel applications and relies on an analytical power model that provides the DSE framework with the execution time and energy of a HW/SW configuration. The power model parameters are obtained with the measurements of a tiny subset of the design space, which are then injected into two extraction functions to obtain analytical formulations of the execution time and the energy consumption of the computation kernel. The partitioning problem constraints are defined as a set of inequalities with Boolean, integer (discrete) and non-integer (continuous) variables within a Mixed Integer Linear Programming (MILP) framework. Then, the best configuration that minimizes the user objective (e.g. execution time or total energy consumption) can be efficiently determined using commercial or open source solvers within a second. This methodology was tested on a Zynq-based heterogeneous architecture with two application kernels: a matrix multiplication and a Stencil computation. The results show a minimum of 12% acceleration speed-up and energy saving compared to standard approaches. They also show that the most energy-efficient solution is application-and platform-dependent and moreover hardly predictable. Such method could be included in a complete framework with a multi-step exploration to obtain an energy-efficient mapping of a full application on HMpSoC and to open new opportunity for future computer-aided design tools.

Energy harvesting is a promising approach to enable autonomous long-life wireless sensor networks. As typical energy sources present time-varying behavior, each node embeds an energy manager, which dynamically adapts the power consumption of the node to maximize the quality of service, while preventing power failure. In this work, RLMan, a novel energy management scheme based on reinforcement learning theory, is proposed. RLMan dynamically adapts its policy to time-varying environment by continuously exploring, while exploiting the current knowledge to improve the quality of service. The proposed energy management scheme has a very low memory footprint, and requires very few computational power, which makes it suitable for online execution on sensor nodes. Moreover, it only necessitates the state of charge of the energy storage device as an input, and therefore is practical to implement. RLMan was compared to three state-of-the-art energy management schemes, using simulations and energy traces from real measurements. Results show that using RLMan can enable almost 70 % gains regarding the average throughput.

Wireless sensor networks are employed in many applications, such as health care, environmental sensing, and industrial monitoring. An important research issue is the design of efficient medium access control (MAC) protocols, which have an essential role for the reliability, latency, throughput, and energy efficiency of communication, especially as communication is typically one of the most energy consuming tasks. Therefore, analytical models providing a clear understanding of the fundamental limitations of the different MAC schemes, as well as convenient way to investigate their performance and optimize their parameters, are required. In this paper, we propose a generic framework for modeling MAC protocols, which focuses on energy consumption, latency, and reliability. The framework is based on absorbing Markov chains, and can be used to compare different schemes and evaluate new approaches. The different steps required to model a specific MAC using the proposed framework are illustrated through a study case. Moreover, to exemplify how the proposed framework can be used to evaluate new MAC paradigms, evaluation of the novel pure-asynchronous approach, enabled by emerging ultra-low-power wake-up receivers, is done using the proposed framework. Experimental measurements on real hardware were performed to set framework parameters with accurate energy consumption and latency values, to validate the framework, and to support our results.

Low power communication has evolved towards multi-kilometer ranges and low bit-rate schemes in recent years. LoRa is an example of such a long-range technology that is triggering increasing interest. Using these technologies, a trade-off must be made between power consumption and latency for message transfer from the gateway to the nodes. However, domains such as industrial applications in which sensors and actuators are part of the control loop require predictable latency, as well as low power consumption. These requirements can be fulfilled using pure-asynchronous communication and idle listening elimination, allowed by emerging ultra-low-power wake-up receivers. On the other hand, state-of-the-art wake-up receivers present low sensitivity compared to traditional wireless node receivers and LoRa, which results in the fact that they can operate in short-range in the order of a few tens of meters. In this work, we propose an energy efficient architecture that combines long-range communication with ultra low-power short-range wake-up receivers to achieve both energy efficient and low latency communication in heterogeneous long-short range networks. The proposed hardware architecture uses a single radio transceiver that can communicate using both LoRa and state-of-the-art wake-up receivers while the proposed MAC protocol exploits the benefits of these two communication schemes. Experimental measurements and analytical comparisons show the benefits regarding both energy efficiency and latency enabled by the proposed approach. Analytical comparisons show that the proposed scheme allows up to 3000 times reduction of the power consumption compared to the standard LoRa approach.

Achieving energy efficient wireless communication is the most pursued goal in Wireless Sensor Networks (WSNs), as energy consumption is typically a major barrier to long term applications. In recent years, ultra-low power Wake-up Receivers (WuRx) have emerged, enabling pure asynchronous wireless communication that eliminates energy waste due to idle listening. However, to achieve a significant increase of energy efficiency compared to traditional duty-cycling approaches, Medium Access Control (MAC) protocols exploiting WuRx must be carefully designed. Therefore, we propose an analytical framework to model MAC protocols, leveraging WuRx or not, which gives an important evaluation of power consumption, latency and reliability. This framework was used to both model a WuRx-based MAC protocol, and to model two other state-of-the art MAC protocols for WSNs not using WuRx. Experimental power consumption and latency measurements were conducted to validate the proposed framework and the MAC protocol leveraging WuRx. Analytical results show the convenience of using WuRx and quantify the benefits of this emerging technology. These results demonstrate that using WuRx achieves up to 135 times lower power consumption and up to 23 times lower latency compared to traditional approaches in typical low throughput WSNs applications.

This paper addresses the problem of In-phase/Quadrature (I/Q) imbalance sensitivity of communication systems when it occurs at both Transmitter (TX) and Receiver (RX) sides of an optical coherent system. A novel blind technique is proposed based on the pseudo-rotation of the M-QAM constellation. The pseudo-rotation based compensator is a generic approach, because it does not depend on the modulation order and the level of imbalance. To implement the proposed compensation in practical systems, two algorithms are proposed: Recursive Pseudo-Rotation (RPR) that achieves the performance of the ideal compensator and LRPR, a Low complexity version of RPR. Monte Carlo simulation results show that the proposed compensator outperforms state-of-the-art algorithms for an Additive White Gaussian Noise (AWGN) and the complexity/performance trade-off is also discussed. The efficiency of the proposed method is experimentally validated with a 10 Gbaud QPSK optical system showing its operation in the presence of Inter-Symbol Interference (ISI).

Power management is an important issue in the design of Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this kind of networks, each Energy Harvesting Node (EH-node) must dynamically adapt its performance in order to avoid power failures while maintaining a good quality of service. The power management policy is implemented on each node by a Power Manager (PM). Designing a PM is challenging because the harvested energy is time varying, and the amount of energy that will be harvested in the future is hard to predict. In this work, we present Fuzzyman, a novel PM based on fuzzy control theory. Because of the unpredictability of the harvested energy, fuzzy control theory constitutes an appropriate framework to tackle the problem of designing PM for EH-nodes. We evaluate the performance of Fuzzyman by comparing it to a state of the art approach via extensive trace-driven network simulations. Results show that Fuzzyman achieves more efficient utilization of the harvested energy.

Blind adaptive source separation (BASS) based compensation for transmitter (Tx) IQ imbalance is presented for the first time in an M-QAM optical coherent system. The proposed method is numerically investigated with 4-QAM and 16-QAM signals in the presence of Tx IQ imbalance up to 30o. The robustness of the BASS method is studied after 200-km optical fiber transmission, in which the effects of chromatic dispersion (CD) and carrier frequency offset (CFO) are assumed to be dominant. It is also found that CFO, inherent to frequency difference between the transmitter and receiver lasers in optical coherent transmission, should be compensated before IQ imbalance compensation to achieve a better performance. The proposed method outperforms the Gram-Schmidt orthogonalization procedure (GSOP) in the presence of CD and CFO. We further validate experimentally the proposed method with 10-Gbaud optical 4-QAM and 16-QAM signals at 30o and 10o phase imbalance, respectively, with an emulated 200-km optical fiber transmission and 200-MHz CFO. More specifically, the optical signal-to-noise ratio (OSNR) penalty reduction of the BASS method compared to the GSOP method is 1 dB for 4-QAM at a bit-error-ratio (BER) of 2×10-3 and 2 dB for 16-QAM at a BER of 10-3. Moreover, instead of being a fully independent block and requiring statistical estimation as in GSOP, the BASS method can be integrated into an equalizer and operated at the sample rate, simplifying the operation and allowing parallel implementation.

Long lifetime is the most pursued goal in Wireless Sensor Networks (WSNs). As communication is typically the most energy consuming task, a lot of effort has been devoted to design energy efficient communication protocols using duty-cycling in the last decades. However, in the recent years, a new kind of Ultra Low Power (ULP) receivers, called Wake-up Receivers (WuRx), is emerging. These devices allow the continuous monitoring of the wireless channel while having a power consumption orders of magnitude less than typical WSNs transceivers. WuRx can wake-up the rest of the system (microcontroller (MCU) and main radio) using interrupts only when needed, minimizing the idle listening. In this work, we present an experimental and an analytical study which ultimately serve as guidelines for the design of communication protocols leveraging WuRx.

Wireless Body Area Network (BAN) is a mainstream technology for numerous application fields (medicine, security, sport science...) and precise determination of wireless sensors' positions responses to the great needs in many applications. In addition, Ultra Wide Band (UWB) radio is an attractive technology to achieve the centimeter-level distance measurements. However, the aggregation of the distance information remains a challenge and this paper presents a cutting-edge method for performing the accurate localization in wireless BAN. To this aim, by fully exploiting its unique features, a novel Cooperative-cum- Constrained Maximum Likelihood (CCML) localization algorithm is developed. Simulation results and UWB-based platform validation show absolute agreement with theoretical prediction and improvement over previous studies by Hamie et al and Mekonnen et al.

In this work, Wireless Sensor Network (WSN) applications that require long-term sustainability are considered. Energy harvesting forms a promising technology to address this challenge, by allowing each node to be entirely powered by energy harvested from its environment. To be sustainable, each node must dynamically adapt its Quality of Service (QoS), regarding the harvested energy using a power management strategy. This strategy is implemented on each node by the Power Manager (PM). In this paper, GRAPMAN (GRAdual Power MANager) is proposed, a novel PM for Energy-Harvesting WSN (EH-WSN) powered by pseudo-periodic energy sources. Unlike most state of the art PMs, GRAPMAN aims to achieve high average throughput while maintaining consistent QoS, i.e. with low fluctuations with respect to time, by looking for the highest throughput that can be supplied by the node over a finite time horizon while remaining sustainable. We show through extensive trace-driven network simulations that GRAPMAN outperforms state of the art PMs in both average throughput and throughput consistency.

The FPGA (Field Programmable Gate Array) technology is expected to play a key role in the development of Software Defined Radio (SDR) platforms. To this aim, leveraging the nascent High-Level Synthesis (HLS) tools, a design flow from high-level specifications to Register-Transfer Level (RTL) description can be thought. Based on such a flow, this paper describes the Design Space Exploration (DSE) that can be achieved using loop optimizations. The mainstream objective is to demonstrate the compile-time flexibility of an architecture when associated with a reconfigurable platform. Throughout both IEEE 802.15.4 and IEEE 802.11g waveform examples, we show how the FPGA resources can be tuned according to a targeted throughput.

Software defined radio (SDR) opens a new door to future Internet of Things with higher degree of designing flexibility in context of wireless system development. Prototyping a remote implementation of wireless protocols on a hardware over the web requires a highly versatile software radio platform along with laid-back designing tools. To this aim, an FPGA-based SDR scheme has been proposed combining Virtex-6 Perseus 6010 platform capabilities and a design flow based on High-Level Synthesis (HLS) tools. A full IEEE 802.15.4 (ZigBee) physical layer has been implemented on the proposed platform from a C-language dataflow specification. All the results have been analyzed to lead to a fair comparison between different design flows. Although the proposed SDR has some designing issues, it shows a noticeable designing potentiality to flexible prototyping of future wireless systems.

This paper analyses signal detection using watermark insertion, which is artificially embedded into the digital modulated signal. When a signal does not contain intrinsic information, its detection is hard to achieve using blind detectors. For that kind of signals, we propose to insert a low-power watermark that will be detected by a matched filter based detector. The system design is a trade-off between the watermark insertion strength (i.e. reducing the transmission quality) and the detection sensitivity. This trade-off is discussed in this paper and simulations results show the advantage of the watermark insertion.

In this paper, we address the architecture of multistandard simultaneous reception receivers and we aim to reduce the complexity of the analog front-end. To this end, we propose an architecture using the double orthogonal translation technique in order to multiplex two signals received on different frequency bands. A study case concerning the simultaneous reception of 802.11g and UMTS signals is developed in this paper. Theoretical and simulation results show that this type of multiplexing does not significantly influence the evolution of the signal to noise ratio of the signals.

In this paper, we address the IQ imbalance sensibility of the code multiplexing front-end architecture. This innovative architecture has been recently proposed in order to reduce the analog complexity of an antenna diversity receiver front-end. An interesting characteristic of this structure is that the resulting IQ imbalance is equal for each received baseband signal. Associated with Single Matrix Inversion algorithm, this property ensures a high IQ imbalance robustness. A global antenna diversity system including analog front-end and digital processing has been implemented in order to perform simulation validation. Results show that the bit error rate does not increase significantly with the multiplexing and this increase is compensated for a high IQ imbalance.

In this paper, we address the architecture of an antenna diversity receiver and we aim to reduce the complexity of the analog front-end. To this end, an innovative architecture is introduced based on code multiplexing. This architecture uses the direct sequence spread spectrum technique in order to multiplex the different antennas contributions through a single demodulator. Simulation and measurement results show that, in a Gaussian case, the bit error rate does not increase so much with the multiplexing. The complexity evaluation shows that the proposed architecture significantly reduces the power consumption of the front-end.