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International audience

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The rise of IoT in the past few years has led to the massive deployment of connected devices making IoT networks denser. To optimize transmission arising from congestion in dense networks, adaptive data-rate algorithms have been implemented such as the one used in the LoRaWAN protocol. Utilization of algorithms based on reinforcement learning, especially multi-armed bandit, have been investigated, but the duty-cycle limitation decreases the performance of these algorithms in dense networks up to 15 %. This paper aims at giving a solution to resolve the issue caused by duty-cycle limitation, using the LoRa technology as a study case. An effort was done on energy consumption and the reward is modified in order to save energy according to the quality of service. Performances are evaluated thanks to our new simulator J-LoRaNeS based on the Julia language.

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.

Accurate indoor localization remains challenging due to frequent Non-Line-of-Sight (NLOS) conditions, which degrade the performance of Ultra-Wideband (UWB)-based positioning systems. Traditional filters like the Extended Kalman Filter (EKF) and its variants assume ideal Line-of-Sight (LOS) scenarios and struggle in complex indoor environments. To address this, we propose the NLOS-Aware Adaptive Extended Kalman Filter (NA-AEKF), designed to improve robustness and accuracy under dynamic conditions. NA-AEKF uses machine learning to classify real-time measurements as LOS or NLOS, adjusting filter parameters accordingly. This integration improves adaptability to changing conditions and mitigates NLOS-related errors. Simulations show that NA-AEKF outperforms EKF, the Unscented Kalman Filter (UKF), the Adaptive EKF (AEKF) and the Improved adaptive EKF (I-AEKF) [1], achieving higher precision in complex indoor settings.

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.

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 decade Internet of Things (IoT) grew up in an exponential behavior with applications requiring long range and low power wireless transmissions. Factory of the Future (FoF), also called Industry 4.0, aims to use IoT technologies to enhance productivity, therefore adding high reliability constraints. Several IoT standards were proposed and LoRa has emerged as a high potential candidate for a variety of applications. LoRa modulation is based on a chirp spread-spectrum technique and offers efficient transmission up to 50 kbps over several kilometers. The performance of LoRa in terms of symbol or bit error probability has been theoretically analyzed in few recent papers for a Gaussian channel. However, the industrial environment is often corrupted with impulsive non Gaussian noise generated by high-power equipment. In this paper, the impact of impulsive noise, modeled by the Middleton Class-A noise, is introduced and the robustness of a LoRa communication is studied. Compared to the Gaussian case, simulations show that impulsive noise may severely degrade system performance. This Signal-to-Noise Ratio loss can reach up to 10 dB, but increasing the spreading factor can reduce the noise impact.

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.

Many emerging applications based on robotics or augmented reality (e.g. for museums, BIM...) require accurate indoor positioning, that obviously can not be achieved by global positioning systems or traditional radio systems using received signal strength indicators. Ultra-wideband radio is a promising technology to enhance this accuracy, but its robustness still suffers in noisy environment and most of localization algorithms keep a prohibitive complexity to be embedded on target nodes. The Newton-Gauss algorithm represents a good trade-off between positioning performance and processing needs at the node level. As some recent wireless body area networks embed inertial measurement unit, the target direction can be used to further enhance the accuracy while keeping an acceptable complexity. In fact, the direction allows to narrow the research area of the mobile position. Thanks to this additional information, our direction-aided Newton-Gauss algorithm allows a gain of more than 14% in terms of accuracy over classical Newton-Gauss algorithm.

In this paper, imperfect feedback-based linear precoding schemes are proposed for multiple-input-multipleoutput (MIMO) wireless communication systems, where input symbol streams are encoded by using generalized-orthogonal space-time block codes. The imperfect feedback information drastically degrades the system performance. Therefore, to overcome the effect of erroneous feedback information, error-tolerant weighting (ETW) schemes are implemented by employing diagonal precoder. The communication links are assumed to be Nakagami-m distributed for accurately characterizing practical wireless channels under different fading scenarios. Exact and asymptotic symbol-error-rate (SER) expressions of the considered MIMO system are derived under imperfect feedback with the help of order statistics. The most appropriate values of the transmit weights of the precoder matrix are obtained by minimizing the derived average SER over fixed and adaptive signal-to-noise ratio. Further, we study the trade-off between the proposed optimal and suboptimal feedback-based precoding schemes. Moreover, the ergodic-rate of the ETW schemes is also derived with imperfect feedback for arbitrary MIMO system by using the moment generating function approach. The simulated and analytical results show that a significant performance gain is achieved by the ETW schemes as compared to the uniform power allocation and existing precoding techniques under erroneous feedback information.

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.

Nowadays, there is a high demand for human and/or objects monitoring/localizing in the context of applications like Building Information Modeling (BIM), automated drone missions, contextual visits of museum or sports monitoring for instance. While for outdoor positioning accurate and robust solutions (i.e. GPS) exist for many years, indoor positioning is still very challenging. There is also a need of gesture/motion tracking systems that could replace video solutions. We propose in this paper a hardware/software platform named Zyggie that combines both Ultra Wide Band (UWB) technology and Received Signal Strength Indicator (RSSI) for low power accurate indoor positioning and Inertial Measurement Unit (IMU) utilization for motion tracking. Very few industrial/academic existing solutions can simultaneously perform indoor positioning and motion tracking and none of them can do both under low power, low cost and compacity constraints addressed by our platform. As Zyggie has the capability to estimate distances w.r.t other platforms in the environment and quaternions (which represents the attitude/orientation) users can test/enhance state of the art algorithms for positioning and motion tracking applications.

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.

In this paper, we propose an imperfect quantized feedback-based diagonal precoding scheme for generalized orthogonal space-time block codes in an N × 1 multiple-input single-output (MISO) wireless communication system employing M-QAM constellation. It is well known that a feedback-based precoding scheme aids channel adaptive signaling in wireless communication system and capitulates large improvements in almost all performance metrics. However, a practical feedback link is susceptible to error due to wireless channel fading and leads to wrong precoder selection. Therefore, a signal-to-noise ratio (SNR) adaptive error-tolerant weighting scheme is introduced which provides significant gain in comparison with another proposed arbitrarily fixed SNR error-tolerant weighting scheme. At first, a tight approximate closed-form expression of average symbol-error-rate (SER) and an exact analytical expression of outage probability are derived under imperfect feedback information with the help of order statistics. By minimizing the derived average SER, optimized transmit weights for the diagonal precoding matrix (based on feedback bits) are obtained. Later, a closed-form expression of the ergodic capacity with erroneous feedback bits for arbitrary MISO system is also provided by using the moment generating function approach. Further, numerical results show that the considered error-tolerant schemes outperform the uniform power allocation scheme in term of all the investigated performance metrics.

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.

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.

The linear closed-loop MIMO precoding technique employs the channel state information (CSI) at both sides of the link to optimize various criteria such as the capacity, the mean square error, the signal to noise ratio (SNR), etc. Besides classical criteria such as capacity or bit error rate, the diversity-multiplexing trade-off (DMT) is now widely used to evaluate the performance of designed precoders. Indeed, it is known that a fundamental tradeoff between the spatial multiplexing and the diversity order exists. The first definition was given for asymptotic SNR, then was extended to finite values. The DMT was studied for open-loop scheme (Alamouti or V-BLAST) and we propose in this paper a method to obtain DMT of multi-form MIMO precoders. Although several multi-form solutions were found, to obtain their theoretical performance is still difficult. In order to tackle this challenge, we propose to investigate the minimal distance approach: starting from the probability density function of a square minimum distance, we obtain the outage probability and diversity-multiplexing trade-off (DMT) at operational SNR. We arbitrarily choose the max-dminprecoder based on the maximization of a minimal distance using the CSI at the transmitter (closed-loop). This expression is validated by simulations and comparisons between different MIMO precoding schemes are performed. The method can be applied to others precoders and fading channels.

In wireless networks, the cooperative diversity is an implicit form of space diversity commonly used when other conventional transmit diversity methods might not be practical. It was largely proved that cooperative transmission, where a source and a relay cooperate to communicate with a unique destination, is power‐efficient compared to the point‐to‐point transmission. However, the model considered when stating this conclusion is counting only the transmission power consumption. In this study, the authors study the effect of taking into account not only the transmission power at each transmission node but also the processing power consumed in each reception node on the overall end‐to‐end performance. They formulate the optimisation problem aiming to minimise the total power consumption in order to achieve a target performance constraint, where the total power consumption stands for the sum of the transmission power and the processing power consumed in the decoding (neglecting other forms of power consumption). The authors' analysis relies on the characterisation of an information‐theoretic bound on the decoding power of any modern code to achieve a specified bit error probability while operating at a certain gap from the capacity. As this bound is built on the sphere‐packing analysis, this study focuses on message‐passing decoders. Using this theoretical framework, the improvement of well‐known cooperative protocols over the original non‐cooperative point‐to‐point system system is reinvestigated in terms of total power consumption. Thanks to this theoretical framework, a new classification of the studied cooperative protocols is given revealing some surprising conclusions. In particular, the selective decode‐and‐forward protocol is no more constantly preferred to its simpler alternative, i.e. the decode‐and‐forward protocol.

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.

The linear closed-loop MIMO precoding technique employs the channel state information at both sides of the link to optimize various criteria such as the capacity, the mean square error, the mutual information. However, the cost is generally an increased complexity and the theoretical study of such a system can be difficult, i.e. obtain criteria such as the Bit Error Rate or Logarithm of Likelihood Ratio (LLR). Some studies are available for diagonal form precoders, but it is often difficult to deal with a precoder that chooses between different forms to adapt to the channel state, as in the case for the max-dmin precoder. In this paper, we propose a new approximation of the mutual information (MI) which is expressed as a function of the minimal distance. Indeed, our recent works permit us to obtain the probability density function of the minimal distance, and, this is the main point, to deal with the multi-forms. The MI can then be averaged over the statistics of the channel and the result is available for any MIMO system and any modulation size for the example of max-dmin.

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.

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.

In this paper, we study the effect of optimal power allocation on the performance of communication systems using hybrid-automatic repeat request (HARQ) protocols with a limited maximum number of transmission rounds. We formulate the optimization problem aiming to minimize the total average power consumption in order to achieve a target performance constraint, where the total power consumption stands for the sum of the transmission power and the processing power consumed in the decoding. Our analysis relies on the characterization of an information-theoretic bound on the decoding power of any modern code to achieve a specified bit error probability while operating at a certain gap from the capacity. As this bound is built on the sphere-packing analysis, the present study focuses on message-passing decoders. We find that the implementation of power-adaptive HARQ reduces the total average power consumption even when taking decoding power into consideration, compared with reference systems.

To design autonomous wireless sensor networks (WSNs) with a theoretical infinite lifetime, energy harvesting (EH) techniques have been recently considered as promising approaches. Ambient sources can provide everlasting additional energy for WSN nodes and exclude their dependence on battery. In this article, an efficient energy harvesting system which is compatible with various environmental sources, such as light, heat, or wind energy, is proposed. Our platform takes advantage of double-level capacitors not only to prolong system lifetime but also to enable robust booting from the exhausting energy of the system. Simulations and experiments show that our multiple-energy-sources converter (MESC) can achive booting time in order of seconds. Although capacitors have virtual recharge cycles, they suffer higher leakage compared to rechargeable batteries. Increasing their size can decrease the system performance due to leakage energy. Therefore, an energy-neutral design framework providing a methodology to determine the minimum size of those storage devices satisfying energy-neutral operation (ENO) and maximizing system quality-of-service (QoS) in EH nodes, when using a given energy source, is proposed. Experiments validating this framework are performed on a real WSN platform with both photovoltaic cells and thermal generators in an indoor environment. Moreover, simulations on OMNET++ show that the energy storage optimized from our design framework is utilized up to 93.86%.

In this study, a receiver initiated cooperative medium access control (RIC-MAC) protocol is proposed for cooperative communications to reduce the energy consumption of wireless sensor networks (WSNs). Considering a real WSN platform, the simulation results show that using the proposed RIC-MAC protocol in cooperative communications provides latency and energy gains as compared to multi-hop communications. However, the energy gain is shown to be reduced when the network traffic load increases. Finally, considering the impact of traffic load on energy consumption and latency, RIC-MAC is illustrated to be robust to traffic load variations in terms of latency.

In this paper, we investigate a novel low-latency MAC protocol (ARQ-CRI) for low-power cooperative wireless sensor networks WSNs, while preserving (i.e. in high traffic mode) or even increasing (i.e. in low traffic mode) energy-efficiency. Based on the wakeup period of relay nodes, a new relay selection technique is proposed to decrease latency. Simulations show that ARQ-CRI can get latency gain and help to increase energy-efficiency in low traffic mode as compared to CL-MAC, a recently proposed cooperative MAC protocol for WSNs. Besides, regarding traffic variation, ARQ-CRI is claimed to be more stable than CL-MAC. Finally, the effect of the number of potential relays on the performance of ARQ-CRI is also considered.

Exploiting energy from the environment to extend the system lifetime of Wireless Sensor Network (WSN), especially thermal energy, is considered as a promising approach. When considering self-powered systems, the Power Manager (PM) plays an important role in energy harvesting WSNs. Instead of minimizing the consumed energy as in the case of battery-powered systems, it causes the harvesting node to converge to Energy Neutral Operation (ENO) in order to achieve a theoretically infinite lifetime. In this paper, a low complexity PM for a thermal-powered WSN is presented. Our PM adapts the duty cycle of the node according to the estimation of harvested energy and the consumed energy provided by a simple energy monitor for a super capacitor based WSN to achieve the ENO. Experiments are performed on a real WSN platform where harvested energy is extracted from the wasted heat of a PC adapter by two thermoelectric generators.

Opportunistic forward techniques can improve the energy efficiency of wireless multi-hop networks by exploiting multiuser diversity. Opportunistic forward schemes generally integrate the functions of routing and MAC layers; nevertheless, most of the works focus on the design of routing protocol while assuming an energy efficient MAC layer. However, due to the unreliable links among the source node and its relay candidates, a great amount of energy expenditure results from the synchronization and multi-relay transmission, which degrades the energy performance of opportunistic forward techniques. In this paper, an energy efficient opportunistic MAC protocol with the mechanisms of reservation and a relay candidate coordination is proposed. Moreover, the multi-relay transmission probability is analyzed. Simulation and experiment results on a real wireless sensor network platform in different channels demonstrate the the proposed scheme greatly reduces the multi-relay transmission probability and achieves about 84% improvement of energy efficiency compared with the traditional opportunistic MAC schemes.

To reduce the decoding complexity of linearly precoded MIMO systems, the sphere decoder is applied instead of maximum likelihood and the performance complexity trade-off is investigated. The max-dminprecoding criterion allows to enhance dramatically the Bit-Error-Rate (BER) of MIMO systems by maximizing the minimum Euclidean distance between the lattice points of received constellation. The maximum likelihood (ML) decoder, therefore, is the optimal solution for the receiver, but its computational complexity is exponential in function of the number of antennas and constellation size. The sphere decoding (SD) algorithm, proposed as a sub-optimal ML-decoding, just considers a subset of lattice points that drop into the sphere centered by the received point to obtain the decoded solution, thus reducing significantly the complexity. In this paper, two structures of SD will be investigated: general SD with its adaptive covering radius and real-valued fixed complexity sphere decoder (RFSD). Because the structure of max-dminis complicated and strongly depends on the channel, it exists the case when all power is poured only on the best sub-channel. Some adjustments, therefore, of traditional sphere decoding algorithm will be made to adapt to the precoded MIMO systems.

This paper presents a self organized asynchronous medium access control (MAC) protocol for wireless body area sensor (WBASN). The protocol is optimized in terms of latency and energy under variable traffic. A body sensor network (BSN) exhibits a wide range of traffic variations based on different physiological data emanating from the monitored patient. For example, electrocardiogram data rate is multiple times more in comparison with body temperature rate. In this context, we exploit the traffic characteristics being observed at each sensor node and propose a novel technique for latency-energy optimization at the MAC layer. The protocol relies on dynamic adaptation of wake-up interval based on a traffic status register bank. The proposed technique allows the wake-up interval to converge to a steady state for variable traffic rates, which results in optimized energy consumption and reduced delay during the communication. A comparison with other energy efficient protocols is presented. The results show that our protocol outperforms the other protocols in terms of energy as well as latency under the variable traffic of WBASN.

Through a feedback link, the channel state information acquired at the receiver is known at the transmitter and MIMO precoding techniques can be applied according to various criteria. In this paper, an efficient linear precoder based on the maximization of the minimum Euclidean distance between two received data vectors for three data-stream MIMO spatial multiplexing systems is proposed. By using a singular value decomposition, the precoding matrix can be parameterized as the product of a power allocation matrix and an input shaper matrix. According to this representation, the optimal precoders are proposed for the case of three independent data-streams. Simulation results over Rayleigh fading channels show considerable bit-error-rate improvement with the proposed solution in comparison with other precoding strategies.

Cooperative relay techniques are exploited to reduce the transmission energy consumption which is very important for average and long range transmission in wireless sensor networks (WSNs). In this paper, the system having a two-antenna source, two one-antenna relays and a one-antenna destination is considered. Using distributed space-time code at relays, MIMO simple cooperative relay model (MSCR) and MIMO full cooperative relay model (MFCR) are proposed in comparison with MIMO normal cooperative relay model (MNCR) where the relays forward signals consecutively to destination. The outage probability analysis derives that all the models have the diversity order of 4. However, the analytic and simulation results show that MFCR has better performance than MNCR, whereas MSCR provides better spectral efficiency than MNCR. Moreover, the energy efficiency of these models is also considered by using a realistic power consumption model where the parameters are extracted from the characteristics of CC2420, a wireless sensor transceiver widely used and commercially available. For each transmission ranges, the optimal cooperative scheme in terms of energy efficiency is provided by simulation results.

Not only the minimum Euclidean distance but also the number of neighbors providing the dminin the received constellation has an important role in reducing the error probability when Maximum Likelihood detection is considered. Assuming that the channel-state-information is available at the transmitter, a new precoder in which the rotation parameter has no influence is proposed for two independent data-streams. The expression of the new precoding strategy is less complex and the space of solution is, therefore, smaller. Simulation results over Rayleigh fading channel confirm a bit-error-rate improvement of the proposed solution in comparison with other precoders. The improvement depends on the channel characteristics and is more significant if the channel is dispersive.

Distributed space-time codes (DSTC) are often used in cooperative relaying networks whose relays can support a single antenna due to the limited physical size. In this paper, full DSTC protocol in which there is a data exchange between relays before forwarding signals to destination is proposed to improve the performance of a cooperative relaying system. A lower bound for the average symbol error probability (ASEP) of full DSTC cooperative relaying system in a Rayleigh fading environment is provided. In the case when the Signal to Noise Ratio (SNR) of the relay-relay link is much greater than that of the source-relay link, the upper bound on ASEP of this system is also derived. From the simulations, we show that the average SNR gain of full DSTC system over DSTC system is 3.8dB and the maximum SNR gain is 5dB when the relay-relay distance is small and the relays are in the middle of the source and the destination. The effect of the distance between the relays shows that the performance does not degrade so much as the distance between relays is lower than a half of the source-destination distance. Moreover, we also show that when the error synchronization range is lower than 0.5, the impact of the transmission synchronization error of the relay-destination link on the performance is not considerable.

In wireless distributed networks, cooperative relay and cooperative multiple-input-multiple-output (MIMO) techniques can be used to exploit the spatial and temporal diversity gains to increase the performance or reduce the transmission energy consumption. The energy efficiency of cooperative MIMO and relay techniques is then very useful for the infrastructure-to-vehicle (I2V) and infrastructure-to-infrastructure (I2I) communications in intelligent transport system (ITS) networks, where the energy consumption of wireless nodes embedded on road infrastructure is constrained. In this paper, applications of cooperation between nodes to ITS networks are proposed, and the performance and the energy consumption of cooperative relay and cooperative MIMO are investigated and compared with the traditional multihop technique. The comparison between these cooperative techniques helps us choose the optimal cooperative strategy in terms of energy consumption for energy-constrained road infrastructure networks in ITS applications.

This paper proposes an efficient sub-optimal MIMO linear precoder based on the maximization of minimum distance for three virtual suchannels. A new virtual MIMO channel representation with two channel angles allows the parameterization of the linear precoder and the optimization of the distance between signal points at the received constellation. To illustrate the optimization process, a precoder is derived for BPSK and QPSK modulation following the max-SNR approach, which consists in pouring power only on the most favored virtual sub-channel. Simulation results over a Rayleigh channel confirm the interest of this new precoder in terms of bit-error-rate.

In wireless distributed networks where multiple antennas can not be installed in one wireless node, cooperative relay and cooperative Multi-Input Multi-Output (MIMO) techniques can be used to exploit the spatial and temporal diversity gain in order to reduce the energy consumption. The simplicity and the energy efficiency of cooperative Multi-Input Single-Output (MISO) and relay techniques is very useful for the energy constrained Wireless Sensor Networks (WSN). In this paper, the performance and the energy consumption of the cooperative MISO and relay techniques are investigated over a Rayleigh fading channel. If under ideal conditions cooperative MISO has been proved to be better than relay, the latter is a better solution when transmission synchronization errors occur. The comparison between these two cooperative techniques helps us to choose the optimal cooperative strategy for energy constrained WSN applications.

A precoder based on the exact optimization of the minimum Euclidean distance dminbetween signal points at the receiver side is proposed for MIMO-OFDM systems using a 16-QAM modulation. Assuming that channel state information (CSI) can be made available at the transmitter, the channel is diagonalized and a precoder can be derived. A numerical approach shows that the precoder design depends on the channel characteristics, leading to 8 different precoder expressions. Comparisons with maximum signal-to-noise ratio (SNR) strategy and other precoders based on criteria, such as water-filling (WF), minimum mean square error (MMSE), and maximization of the minimum singular value of the global channel matrix, are performed to illustrate the significant bit-error-rate (BER) improvement of the proposed precoder. In order to make its implementation easier, it is shown that it can be expressed by only two ways without significant performance degradation.

Wireless sensor networks where neighbor nodes can cooperate both at transmission and reception by using the cooperative multi-input multi-output (MIMO) technique are considered. Space-time diversity gain can be exploited to reduce the transmission energy consumption which is very important for average and long range transmission in wireless sensor network (WSN). However, differing from classical MIMO systems, cooperative systems suffer from the lack of synchronization between distributed nodes and the additive noise in the cooperative reception. At the cooperative transmission side, we investigate the effect of transmission synchronization error which generates inter-symbol interference (ISI), decreases the desired signal amplitude and makes the channel state information (CSI) more difficult to be estimated by the receiver. At the cooperative reception side, two strategies of wireless transmission techniques which are energy efficient and have good performance are also presented. The simulation of a cooperative MIMO system using Alamouti and Tarokh space-time block codes (STBC) over Rayleigh fading channels presents a performance degradation in the presence of transmission synchronization error and additional noise of cooperative reception techniques. For a small synchronization error range at cooperative transmission and a reasonable amplification factor in reception, the degradation is negligible and the cooperative MIMO performance is rather good.

In the context of cooperative multi-input multi-output (MIMO) techniques for wireless sensor networks, the transmission synchronization error impact is investigated in this paper and a new space-time combination technique is proposed. Cooperative MIMO techniques have been recently studied in order to reduce the energy consumption in distributed wireless sensor networks. Differing from classical MIMO systems, the cooperative antennas are physically separated in a cooperative system which leads to the unsynchronized cooperative transmission. The transmission synchronization error generates inter-symbol interference (ISI) and decreases the desired signal amplitude at the receiver, leading to a performance degradation and an additional energy required for data transmission. For small range of synchronization error, the performance degradation is negligible and the cooperative system performance is rather tolerant. However, for large range of error, the degradation is significant. A new space time combination technique is proposed for a cooperative multi-input single-output (MISO) system using Alamouti codes. The proposed technique combines two sequences of the received signal sampled at different times in order to perform an orthogonal combination. This technique has a better tolerance to the transmission synchronization error and also a low complexity. The significant advantage of using this new combination technique over traditional Alamouti combination is illustrated by simulations over a Rayleigh fading channel.

A cooperative MIMO scheme selection is proposed for wireless sensor networks where energy consumption is the most important design criterion. Space-time block codes are designed to achieve maximum diversity for a given number of transmit and receive antennas with very simple decoding algorithm. In radio fading channel, STBC require less transmission energy than SISO technique for the same bit error rate and can be employed practically in wireless sensor networks by using the cooperative MIMO scheme. Considering Alamouti and Tarokh space-time block codes, the number of antennas at both the transmission and the reception sides are selected with respect to the transmission distance. By using cooperative MIMO transmission instead of SISO, it is shown that the distance between nodes can be increased and a large amount of the total energy can be saved for middle and long distance transmission. The energy efficiency of cooperative MIMO over SISO and multihop SISO is proved by simulations, and a multi-hop technique for cooperative MIMO is also proposed for good energy-efficiency and limited number of available cooperative nodes

Previous papers have shown that multipath wireless channels are capable of enormous capacities, provided that the multipath scattering is sufficiently rich and is properly exploited. A layered space-time architecture, known as BLAST, has been proposed. A basic hypothesis made by the BLAST algorithm is that the symbol period is large compared to the maximum echo delay (hence, the data rate cannot be too high). In this paper, we use an approach that combines BLAST and OFDM. Its interest is to suppress the data rate constraint. First, the symbols are packed into matrices; then matrix manipulations prior to BLAST transmission provide a transmission system which is theoretically equivalent to many independent BLAST channels. Results obtained with the UHF COST 207 channel corresponding to GSM transmission in an urban area are provided and discussed.

Direct sequence spread spectrum transmissions (DS-SS) are now widely used for secure communications, as well as for multiple access. Since the transmission uses a large bandwidth, the power spectral density of a DS-SS signal can be below the noise level. Hence, such a signal is difficult to detect. We propose a method which is able to detect a spread spectrum signal hidden in noise. The method does not require a priori knowledge about the spreading sequence used by the transmitter. It is based on two parallel computations: the "theoretical path", in which we compute the theoretical behavior of the fluctuations of second order moment estimators in the case that noise alone is present, and the "experimental path", in which we compute the actual fluctuations. When a DS-SS signal is hidden in noise, the results provided by both paths diverge, hence the presence of the signal is detected. Experimental results show that the method can detect a signal far below the noise level.