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The main motivation of paraglider pilots is to stay in the air for as long as possible. Therefore, paraglider pilots are always searching for thermal upwind that allow them to gain altitude. These thermal lifts are difficult to detect. Therefore, sensors and devices that indicate the vertical speed (so-called variometers) are widely used among paraglider pilots. This article presents the design and the implementation of an ultralow-power, self-sustaining, high-precision, wrist-worn variometer with a minimal form factor but infinite lifetime, which can visually and acoustically indicate the vertical velocity. This article demonstrates the benefits of combining multisource energy harvesting (EH) for wearable devices with low power design, exploiting a novel near-threshold ARM Cortex-M4F microcontroller, the Ambiq Apollo2, for the onboard processing. The experimental results show a power consumption of only $17.12~\mu \text{W}$ in sleep mode and $1937.21~\mu \text{W}$ in the worst case scenario when processing the data and outputting an audio feedback. Measurements confirmed that combining both thermal and solar EH makes the designed electronics self-sustaining. Without EH, the system will be operational for up to 372 h in always-on mode (worst case scenario) supplied by a 200-mAh lithium-ion battery.

A nano-power integrated circuit is designed to convert the ac output of an electromagnetic micro generator for wearable kinetic energy harvesting to dc usable power. The converter is designed to minimize the quiescent current and to maximize the energy conversion and maximum power point tracking efficiency from the kinetic energy harvester. The architecture implements an ac/dc chopper exploiting the intrinsic inductance of the transducer combined with a zero-drop rectifier. An optimal switching scheme is derived for the converter and a corresponding control circuit is developed and optimized for low power consumption. The converter is designed in an SMIC 130-nm process, and circuit simulations show an efficiency of 87%-88% with respect to the maximum power point of the transducer with a battery supply voltage ranging from 3.5 V to 4.2 V. The quiescent current of the converter is as low as 250 nA.

Minimizing the power consumption of always-on sensors is crucial for extending the lifetime of battery-operated devices that are required to monitor events continuously and for long periods. This paper proposes a novel programmable $\mu \text{W}$ event-driven acoustic detector featuring “always-on” audio pattern recognition. The event-driven detector detects up to eight programmable spectral–temporal features extracted with a low-power single-channel analog circuit and classifies the features by an onboard microcontroller. The event-driven detector is combined with novel microbial fuel cells (MFCs) to achieve self-sustainability in an underwater scenario. Experimental results demonstrate that the power consumption of the detector is only $26.89~\mu \text{W}$ during always-on mode, achieving up to 59-dB sound pressure level of sensitivity. High detection accuracy of up to 95.89% in recognizing acoustic patterns has been experimentally verified. Accurate measurements with commercial MFCs demonstrate the capability to achieve self-sustainability in always-on monitoring.

Self-sustainability and intelligence will be critical features of the next generation of Internet of Things devices. Power management circuits need to be energy-efficient in multiple power modes, from nW sleep to mW active, and to support energy harvesting (EH) from multiple environmental sources. This article presents an adaptive and firmware-configurable power supply unit that enables intelligent devices to operate in the sub μW range while achieving self-sustainability through EH. The microcontroller-based design allows efficient conversion from batteries, dc, ac environmental sources, and nA quiescent currents. Source and load power points are decoupled with multiple dc–dc converters aiming to supply loads with adaptive voltage scaling and achieving high reliability. Experimental results show the flexibility and efficiency of this approach: the proposed power supply unit achieves a quiescent current of 54 nA and a maximum peak load current of 300 mA, delivered with an end-to-end power efficiency above 85%. © 2020 IEEE., IEEE Transactions on Power Electronics, 36 (5), ISSN:0885-8993, ISSN:1941-0107

LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster. © 2021 by the authors. Licensee MDPI, Basel, Switzerland., Sensors, 21 (3), ISSN:1424-8220

Low power and long-range communications are essential features of the Internet of Things (IoT) paradigm that is becoming widespread across a spectrum of industrial applications. In this paper, we present performance evaluation of the most promising long-range communication technologies, namely LoRaWAN and NB-IoT. We present accurate in-field measurements using a monitoring application as a testbench for a fair comparison in terms of energy efficiency and lifetime. Experimental results highlight that NB-IoT payload length does not impact on transmission energy. Thus, applications that implement buffering and caching techniques are favored. On the other hand, LoRaWAN consumes 10x less energy to transmit a payload equivalent to that of NB-IoT, thereby allowing longer end-device lifetime.

Wireless wearable sensors have developed rapidly in recent years, primarily driven by e-health, fitness and wellness applications. The technological evolution of low power microprocessors is enabling to process data locally, saving energy and bandwidth and increasing the overall throughput of the wireless network. This paper presents a new general-purpose Inertial Measure Unit that exploits a dual-core architecture. A core offers processing capability, and the other one is a radio interface IEEE 802.15.4. We propose the whole system and a protocol to maximize the throughput, reduce the packet loss and improve the robustness of wireless sensor nodes communication. Experimental results show that our solution offers better data throughput for configurations below 10 nodes compared to widely used commercial wireless sensor nodes. The paper also presents an experimental evaluation of scalability (up to 10 nodes) and power consumption of the proposed solution.

A novel trend to achieve long-lifetime or even self-sustainable wearable devices is to exploit energy harvesting from environmental or body energy. Their power management circuits need to be carefully designed to enable a small-size energy-efficient wearable system, supporting multiple power domains ranging from mW in an active mode to nW in sleep modes. This work presents a fully configurable smart power unit suitable for kinetic energy harvesting transducers, Kinetron MGS, enabling self-sustainability for wearable devices. The proposed power management circuit hosts a low power microcontroller to manage the energy harvesting, voltage conversion from batteries, and wake-up circuits to exit low power states automatically. The source and the load power points are decoupled with multiple DC-DC converters aiming to supply loads with adaptive voltage scaling and high reliability. Experimental results using commercial micro-kinetic generators show the flexibility and efficiency of this approach: the proposed power supply unit achieves a quiescent current of 57 nA and a maximum load current of 300 mA, delivered with a harvesting efficiency of 79 %. We evaluate the proposed system with commercial kinetic energy harvesting transducer generating 1.18 mW when worn on the ankle during walking.

Body bio-impedance is a unique parameter to monitor changes in body composition non-invasively. Continuous measurement of bio-impedance can track changes in body fluid content and cell mass and has widespread applications for physiological monitoring. State-of-the-art implementation of bio-impedance sensor devices is still limited for continuous use, in part, due to artefacts arising at the skin-electrode (SE) interface. Artefacts at the SE interface may arise due to various factors such as motion, applied pressure on the electrode surface, changes in ambient conditions or gradual drying of electrodes. This paper presents a novel bio-impedance sensor node that includes an artefact aware method for bio-impedance measurement. The sensor node enables autonomous and continuous measurement of bio-impedance and SE contact impedance at ten frequencies between 10 kHz to 100 kHz to detect artefacts at the SE interface. Experimental evaluation with SE contact impedance models using passive 2R1C electronic circuits and also with non-invasive in vivo measurements of SE contact impedance demonstrated high accuracy (with maximum error less than 1.5%) and precision of 0.6 Ω. The ability to detect artefacts caused by motion, vertically applied pressure and skin temperature changes was analysed in proof of concept experiments. Low power sensor node design achieved with 50mW in active mode and only 143 μW in sleep mode estimated a battery life of 90 days with a 250 mAh battery and duty-cycling impedance measurements every 60 seconds. Our method for artefact aware bio-impedance sensing is a step towards autonomous and unobtrusive continuous bio-impedance measurement for health monitoring at-home or in clinical environments.

Sound event detection (SED) is a hot topic in consumer and smart city applications. Existing approaches based on Deep Neural Networks are very effective, but highly demanding in terms of memory, power, and throughput when targeting ultra-low power always-on devices. Latency, availability, cost, and privacy requirements are pushing recent IoT systems to process the data on the node, close to the sensor, with a very limited energy supply, and tight constraints on the memory size and processing capabilities precluding to run state-of-the-art DNNs. In this paper, we explore the combination of extreme quantization to a small-footprint binary neural network (BNN) with the highly energy-efficient, RISC-V-based (8+1)-core GAP8 microcontroller. Starting from an existing CNN for SED whose footprint (815 kB) exceeds the 512 kB of memory available on our platform, we retrain the network using binary filters and activations to match these memory constraints. (Fully) binary neural networks come with a natural drop in accuracy of 12-18% on the challenging ImageNet object recognition challenge compared to their equivalent full-precision baselines. This BNN reaches a 77.9% accuracy, just 7% lower than the full-precision version, with 58 kB (7.2 times less) for the weights and 262 kB (2.4 times less) memory in total. With our BNN implementation, we reach a peak throughput of 4.6 GMAC/s and 1.5 GMAC/s over the full network, including preprocessing with Mel bins, which corresponds to an efficiency of 67.1 GMAC/s/W and 31.3 GMAC/s/W, respectively. Compared to the performance of an ARM Cortex-M4 implementation, our system has a 10.3 times faster execution time and a 51.1 times higher energy-efficiency., Comment: 6 pages conference

2020 IEEE 18th International Conference on Industrial Informatics (INDIN), ISBN:978-1-7281-4964-6

The Internet of Things (IoT) is a promising vision to cover a wide range of applications. However, battery-operated devices at the edge of the Internet, have a limited lifetime or involve significant maintenance effort. Energy Harvesting (EH) is one of the most investigated technologies to extend the battery lifetime. Nevertheless, the further miniaturization of devices poses challenges, mainly due to the small form factor needed. In particular, harvesting energy from miniaturized transducers (i.e., millimeter-sized solar panels) is significantly more challenging. Among other EH technologies, transmitting power wirelessly is growing in popularity as it can be controlled, and it is less unpredictable than environmental sources. This work investigates different technologies to supply small size and low power smart devices for IoT applications with wireless power transmission. The paper illustrates with experimental measurements, the power acquired with a commercial radiofrequency power transmission system from Powercaset. According to this evaluation, the RF power transfer to the battery ranged from mW to μW using millimeter-sized antennas up to meters of distance. Thus the system is an effective solution for the supply of small size IoT devices.

Low power and long-range communications are crucial features of the Internet of Things (IoT) paradigm that is becoming essential even for industrial applications. Today, the most promising long-range communication technologies are LoRaWAN and Narrow Band IoT (NB-IoT), which are driving a large IoT ecosystem. In this article, we evaluate the performance of LoRaWAN and NB-IoT with accurate in-field measurements using the same application context for a fair comparison in terms of energy efficiency, lifetime, quality of service, and coverage. The NB-IoT energy transmission is scarcely dependent on the payload length. Thus applications that can tolerate buffering and caching techniques on the node are favored. On the other hand, LoRaWAN consumes 10 × lower energy compared to NB-IoT for occasional and latency-sensitive communications, for which it enables much end-device lifetime. Finally, this paper provides design guidelines for future industrial applications with stringent requirements of long-range and low power wireless connectivity.

High-precision global navigation satellite system (GNSS) is a crucial geolocalization feature enabling a wide range of applications, from mobile Internet-of-Things devices to autonomous drones and self-driving vehicles. Real-time kinematic (RTK) is a GNSS technology that attracting increased interest due to the centimeter precision achievable when wireless communication is present on the devices. On the other hand, sending continuously wireless data increases the energy consumption and the cost of the solution, especially when communication is carried over the 4G network. Due to those drawbacks, RTK is not much exploited in the localization of battery-operated devices. This work combines RTK with low-power long-range communication to achieve submeter precision in an energy-efficient RTK-based system. The proposed system exploits a state-of-the-art RTK-GNSS module combined with a long range (LoRa) to achieve geolocalization with minimal wireless radio infrastructure requirements. An energy-efficient algorithm is proposed and implemented in a microcontroller to have a quick startup and high accuracy. We evaluate three different GNSS modules and compare their performance in terms of power and accuracy. Experimental results, with in-field measurements, show that an average geolocalization precision of tens of centimeters is achievable on a battery-operated wireless end node connected to a single base station used as a geostationary reference anchor placed at kilometers of distance. The peak precision measured is below 10 cm. © 2020 IEEE., IEEE Transactions on Instrumentation and Measurement, 70, ISSN:0018-9456, ISSN:1557-9662

Ultra-wideband (UWB) communication is attracting increased interest for its high-accuracy distance measurements. However, the typical current consumption of tens to hundreds of mA during transmission and reception might make the technology prohibitive to battery-powered devices in the Internet of Things. The IEEE 802.15.4 standard specifies two UWB physical layer interfaces (PHYs), with low- and high-rate pulse repetition (LRP and HRP, respectively). While the LRP PHY allows a more energy-efficient implementation of the UWB transceiver than its HRP counterpart, the question is whether some ranging quality is lost in exchange. We evaluate the trade-off between power and energy consumption, on the one hand, and distance measurement accuracy and precision, on the other hand, using UWB devices developed by Decawave (HRP) and 3db Access (LRP). We find that the distance measurement errors of 3db Access devices have at most 12 cm higher bias and standard deviation in line-of-sight propagation and 2-3 times higher spread in non-line-of-sight scenarios than those of Decawave devices. However, 3db Access chips consume 10 times less power and 125 times less energy per distance measurement than Decawave ones. Since the LRP PHY has an ultra-low energy consumption, it should be preferred over the HRP PHY when energy efficiency is critical, with a small penalty in the ranging performance. acceptedVersion

IEEE Transactions on Instrumentation and Measurement, 67 (4), ISSN:0018-9456, ISSN:1557-9662

This paper focuses on ultra-low power embedded classification of neural activities. The machine learning (ML) algorithm has been trained using evoked local field potentials (LFPs) recorded with an implanted 16x16 multi-electrode array (MEA) from the rat barrel cortex while stimulating the whisker. Experimental results demonstrate that ML can be successfully applied to noisy single-trial LFPs. We achieved up to 95.8% test accuracy in predicting the whisker deflection. The trained ML model is successfully implemented on a low-power embedded system with an average consumption of 2.6mW., 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), ISBN:978-1-5386-3603-9, ISBN:978-1-5386-3604-6

Bio-impedance is a method to assess the body composition safely, non-invasively and inexpensively. This method finds application for assessing body fluid and body composition for multiple disease scenarios in clinical environments and for at-home monitoring of chronic ailments. Bio-impedance sensors require higher power than most other bio-signal acquisition systems due to need of high frequency current and voltage management. Currently used bio-impedance devices are bulky due to incorporation of large batteries and cannot be used for long term monitoring, especially for wearable applications. This limits the widespread implementation of bio-impedance measurement devices. We present the design and implementation of a wireless wearable bio-impedance sensor node, ImpediSense, which has a low power system design that achieves long duration operability without compromising on sensor measurement accuracy and precision. Experimental evaluation show a battery life of several months for measuring bio-impedance with power duty cycling every 1 min over ten frequencies in the range of 10 kHz–100 kHz, using a small form factor 250 mA h Li-ion battery. The lifetime is achieved due to several power optimization implemented in system design of hardware and firmware resulting in active power of 53 mW and idle power of 15.7 μW. Additionally, the presented sensor node shows high performance in terms of accuracy of impedance measurement with an error less than 1.5 % and precision of 0.6 Ω when measuring tetrapolar bio-impedance of the human body. With the inclusion of a small sized battery, ImpediSense has a compact form factor with dimensions 3 cm × 1.8 cm × 0.6 cm, making it more conducive for incorporation in wearable systems.

To enable pervasive exploitation of wireless sensor networks (WSNs), semi-passive wake-up radios (WuR) are proposed to minimize the active time of the energy-hungry main communication radio. The most challenging feature is to enhance their sensitivity, the weakest activation signal the receiver is able to sense, that is limited by the minimum turn-on voltage of diode-based detector. In order to operate the detector at even lower power levels, a strategy to optimize the modulated WuR excitations is presented, exploiting high-peak intermittent continuous waves (ICW), while preserving the required average power per bit. The design and implementation of an ultra-low-power detector, fed by a dual-band antenna and loaded by the WuR back-end is presented: 2.45-GHz and 868-MHz operations of the same WuR are demonstrated, with added flexibility and interoperability among different communication bands. We show that a correct WuR activation is possible with an average power per bit as low as -63 dBm at 2.45 GHz and -65 dBm at 868 MHz. This is experimentally verified by a lab-level set-up and confirmed by a system implementation based on off-the-shelf components only.

Optically trapped nanoparticles are used in various fields ranging from biophysics to precision sensing. An optically trapped nanoparticle can be regarded as a harmonic oscillator driven by the thermal fluctuations of its environment. Unlocking the potential of optically levitated systems for precision measurements in the classical and the quantum regime requires cooling of the particle motion. In parametric feedback cooling, the center-of-mass motion of a nanoparticle optically levitated in a vacuum is reduced by temporally modulating the optical trapping potential. This technique requires a precise measurement of the particle's motion to derive the feedback signal and is prone to measurement noise and inevitable thermal process noise. In a state-of-the-art implementation, the feedback signal is derived from a simple phase-locked loop (PLL). Kalman filters are regularly deployed in a variety of application scenarios to improve system performance under noisy conditions. In this paper, we investigate theoretically and experimentally the performance of parametric feedback cooling, where the measurement signal is Kalman filtered before entering the PLL. Compared to sole PLL cooling, our numerical full-system simulations show a 20% reduction of the residual motional energy of a trapped nanoparticle in the presence of the Kalman filter. We detail a field-programmable gate array-based implementation of a Kalman filter and evaluate its performance in-field.

Low-power and long-range communication have emerged as a promising technology for long lifetime Internet of Things devices and applications. LoRa is one of the most popular solution for low-power and long-range communication in IoT, providing mW power consumption and km range with few kB of throughput. Recently Semtech, the only LoRa transceiver producer, released the SX126X, a novel and optimized version of its popular SX1276 transceiver. Semtech promises lower power and higher range for the new transceiver. This paper analyzes and compares the power consumption of the two transceivers with experimental in-field measurements. Moreover, the paper presents a smart sensor node for the long term and long-range monitoring that use the novel transceiver to transmit data to a remote gateway. Experimental evaluation shows the improvements of the new SX126x transceiver with up to 72% of power saved in transmission over the same conditions. Furthermore, we show the lifetime of the whole senosr node, achieving up to 4.5 years with a 2000mAh battery while sensing and transmitting once per 10 minutes.

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade&rsquo, s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.

Energy Harvesting (EH) is a mature solution to extend the short lifetime of many wireless sensor nodes and IoT devices. However, miniaturizing EH devices to supply low power and small form factor IoT devices is still challenging and not always effective. In fact, EH from the miniaturized is more challenging due to strict constraints in terms of size, weight, and cost. Among other technologies, wireless power transmission is growing of popularity due to the fact that it can be controlled and it is less unpredictable than environments sources. This paper presents a review of technologies for wireless power transmission that can supply cm-scale low power IoT devices. Additionally, this paper provides in-field evaluation with mm-scale antennas of radio frequency power transmission using a commercial system: Powercast. Experimental results focus on evaluating the power flowing in the battery, which also includes the conversion losses, for different distances and antennas. Infield evaluation shows that it is possible to achieve mW of usable power to one meter and hundreds of µW at few meters. Those powers are enough for a wide range of low power and miniaturized IoT devices and IoT applications. In conclusion, this paper shows that RF transmission is an effective and alternative way to supply perpetually low power IoT devices.

Energy-autonomous wireless sensor systems have the potential to enable condition monitoring without the need for a wired electrical infrastructure or capacity-limited batteries. In this paper, a robust and low-cost energy-autonomous wireless rotational speed sensor is presented, which harvests energy from the rotary motion of its host using the variable reluctance principle. A microelectromechanical system (MEMS) gyroscope is utilized for angular velocity measurements, and a Bluetooth Low Energy System-on-Chip (SoC) transmits the acquired samples wirelessly. An analysis on the individual subsystems is performed, investigating the output of the energy transducer, the required energy by the load, and energy losses in the whole system. The results of simulations and experimental measurements on a prototype implementation show that the system achieves energy-autonomous operation with sample rates between 1 to 50 Hz already at 10 to 40 rotations per minute. Detailed investigations of the system modules identify the power management having the largest potential for further improvements.

Positioning based on Ultra-wideband (UWB) wireless communication promises multipath immunity, high resolution, and strong wall penetration. Hence, many academic and industrial researchers are proposing UWB positioning systems especially to track and locate objects and person in indoor environments. UWB indoor localization, based on stationary "anchors" tracking mobile "tags", is already a reality in many application scenarios. However, most of the available systems either perform tracking on servers which collect data from tags and anchors or delegate to the anchors to calculate the actual position of the tags. Both approaches imply long latency and limited throughput in terms of position fixes per unit time. In this paper, we present the design and the implementation of a highly accurate and low latency asymmetric double-sided two-way ranging algorithm that runs at very modest energy budget on low-power tags based on ARM Cortex-M4F microcontrollers (MCUs). We implemented a complete tag platform, which hosts the MCU and the UWB module, and we evaluated the algorithm in the field. Experimental results demonstrate high accuracy (4.3cm) and position fix rate (20Hz), the energy cost of only 4.9mJ for a single fix, and a cold-start first position fix in only 81ms.

The goal or the motivation for paraglider pilots is to stay as long in the air as possible. Therefore paraglider pilots are always searching for thermal upwinds that allow them to gain altitude. These thermal lifts are difficult to detect as we cannot see them with our eyes and we can only feel the vertical acceleration but not the vertical speed with our body. Therefore devices which indicate the vertical speed (so called Variometers) are widely used among paraglider pilots. The main goal for this paper is to design and to implement a low-power, low-cost and wearable variometer with a minimal formfactor but long lifetime, which can display and indicate the vertical velocity. Experimental results show a power consumption of 6.27mW at full load. We managed to achieve a price point of 30.2 CHF per unit and a device volume of 47x47x25 [mm] at a weight of 43 grams. The wearable variometer is designed around the novel Ambiq Apollo2 microcontroller, a commercial high accuracy MEMS sensor and includes a software Kalman filter.

International audience; Wireless sensor networks (WSNs) with energy harvesting capabilities have drawn increasing attention in the last few years, as they enable long-term monitoring applications. However, the level of power harvested is usually limited to few mW. To improve the energy efficiency of WSNs, many power management techniques have been proposed to adjust the quality of service according to the harvested energy fluctuations. As wireless communications consume a major fraction of the available energy, numerous medium access control (MAC) protocols have been proposed to minimize energy consumption, latency, and data collisions. In this paper, we present an innovative MAC protocol for energy-harvesting based WSNs exploiting ultralow-power wake-up radios. To overcome the limited range typical of wake-up radios, a multi-hop wake-up scheme based on a dual radio system is proposed enabling asynchronous communications between a base station and any node of the network while maintaining a low latency and a high energy efficiency. To reduce energy consumption, wake-up calls and data packets are transmitted using two distinct data rates. Combined with destination address decoding, using a higher data rate for data transmission also minimizes the risk of collisions. Our approach has been applied to monitoring applications composed of autonomous sensor nodes powered by indoor light energy. OMNeT++ simulation results demonstrate the benefits of our wake-up radio-based approach in terms of energy, latency, and collisions when compared with the state-of-the-art duty-cycled MAC protocols. Experiments performed with real WSN platforms equipped with a wake-up radio prototype confirm the efficiency of our approach.

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic and firmware implementations. This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-V-based Parallel Ultra-Low-Power (PULP) platform. The toolkit takes multi-layer perceptrons trained with FANN and generates code targeted at execution on low-power microcontrollers either with a floating-point unit (i.e., ARM Cortex-M4F and M7F) or without (i.e., ARM Cortex M0-M3 or PULP-based processors). This paper also provides an architectural performance evaluation of neural networks on the most popular ARM Cortex-M family and the parallel RISC-V processor called Mr. Wolf. The evaluation includes experimental results for three different applications using a self-sustainable wearable multi-sensor bracelet. Experimental results show a measured latency in the order of only a few microseconds and a power consumption of few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22x and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

We present a fully programmable ultra-low-power embedded platform that hosts an "electronic skin" (E-skin) arrays of tactile sensors with up to 64 channels, ECG/EMG sensors up to 8 channels, inertial sensors, and a Bluetooth Low Energy 5.0 module. The platform’s compute engine is a heterogeneous multi-core parallel ultra-low power (PULP) processor based on RISC-V, capable of delivering up to 2.5 GOPS, within a 55 mW power consumption envelope, which makes the platform ideal for battery-powered always-on operation. Experimental results show a peak of 38.3x energy efficiency increase (0.7 V, 85 MHz) compared to ARM-Cortex-M microcontrollers with similar power budgets.

Electronic-skin aiming to mimic human skin is becoming a reality and systems able to process data close to the sensors are required to reduce latency and power consumption. This paper presents the design and implementation of an energy efficient smart system for tactile sensing based on a RISC-V parallel ultra-low power platform (PULP). The PULP processor, called Mr. Wolf, performs the on-board classification of different touch modalities. This demonstrates the promising use of on-board classification for emerging robot and prosthetic applications. Experimental results demonstrate the effectiveness of the platform on improving the energy efficiency of the online classification. In our experiments, Mr. Wolf runs 3.6 times faster than an ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15× better energy efficiency, than the classification done on the STM32F40, consuming only 81mJ per classification.

We present FANNCortexM, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight neural networks on ARM Cortex-M series microcontrollers. The toolkit takes a neural network trained with FANN and generates code targeted at execution on low-power microcontrollers either with a floating-point unit (i.e., ARM Cortex-M4F and M7F) or without a floating-point unit (i.e., ARM Cortex M0-M3). The toolkit is optimized in terms of memory and computational resources. We demonstrate its functionality on the basis of a sample application scenario performing stress detection on a wearable multi-sensor bracelet. Experimental results show a high classification accuracy of 96% for the target application scenario, and low latency of only a few microseconds while keeping the memory requirements (11kB flash storage, 36kB RAM) far below the limitations of the device. Power measurements show a power consumption of only 1.6mW, thus allowing continuous stress detection for 29 days.

Medical devices measure vital parameters such as pulse, respiration rate, and blood oxygenation, over periods of days or weeks in a continuous manner. Traditional systems only support such requirements in stationary applications where a constant power supply is available. Trends toward remote healthcare and telemedicine require wearable devices, able to provide similar functionalities in wireless mode. Miniaturized and thin form factors, desirable in wearable applications, set stringent constraints on the available power, and consequently on the accuracy and lifetime. Energy harvesting combined with low-power design and energy efficient processing can significantly extend the lifetime of wearable devices. This paper presents a wearable pulse oximeter assembled in a 3D ring-like geometry that achieves self-sustainability by exploiting efficient power management, solar energy harvesting, and ultra-low power processing in a multi-core microcontroller. The design strategy of combining onboard processing to monitor blood oxygenation and the transmission of only relevant information via a Bluetooth low-energy (BLE) interface, significantly reduces the overall energy consumption. Experimental results on the designed and developed prototype demonstrate that measuring the blood oxygenation once every minute with a sampling rate of 100 samples/s achieve accurate results at the daily energy consumption of 28 J including hourly BLE transmissions. The low-power design allows the system to be self-sustainable with just 64 min of sunlight per day or 12 hrs. of indoor home light., IEEE Access, 7, ISSN:2169-3536

A WSN for extended monitoring of beehive activity and condition has been developed.Data collected from a beehive were analysed from a multi-disciplinary perspective.A decision tree algorithm describing hive/colony status was proposed and evaluated.An algorithm for predicting short term rainfall local to the hive was also proposed.The algorithms were deployed in network with a minimal energy increase (5.35%). United Nations reports throughout recent years have stressed the growing constraint of food supply for Earth's growing human population. Honey bees are a vital part of the food chain as the most important pollinator for a wide range of crops. It is clear that protecting the population of honey bees worldwide, as well as enabling them to maximise their productivity, is an important concern. In this paper heterogeneous wireless sensor networks are utilised to collect data on a range of parameters from a beehive with the aim of accurately describing the internal conditions and colony activity. The parameters measured were: CO2, O2, pollutant gases, temperature, relative humidity, and acceleration. Weather data (sunshine, rain, and temperature) were also collected to provide an additional analysis dimension. Using a data set from a deployment at a field-deployed beehive, a biological analysis was undertaken to classify ten important hive states. This classification led to the development of a decision tree based classification algorithm which could describe the beehive using sensor network data with 95.38% accuracy. Finally, a correlation between meteorological conditions and beehive data was observed. This led to the development of an algorithm for predicting short term rain based on the parameters within the hive. Envisioned applications of this algorithm include agricultural and environmental monitoring for short term local forecasts (95.4% accuracy). Experimental results shows the low computational and energy overhead (5.35% increase in energy consumption) of the classification algorithm when deployed on one network node, which allows the node to be a self-sustainable intelligent device for smart bee hives.

Wireless sensor networks (WSNs) have received significant attention in recent years and have found a wide range of applications, including structural and environmental monitoring, mobile health, home automation, Internet of Things, and others. As these systems are generally battery operated, major research efforts focus on reducing power consumption, especially for communication, as the radio transceiver is one of the most power-hungry components of a WSN. Moreover, with the advent of energy-neutral systems, the emphasis has shifted toward research in microwatt (or even nanowatt) communication protocols or systems. A significant number of wake-up radio receiver (WUR) architectures have been proposed to reduce the communication power of WSN nodes. In this work, we present an optimized ultra-low power (nanowatt) wake-up receiver for use in WSNs, designed with low-cost off-the-shelf components. The wake-up receiver achieves power consumption of 152 nW (with $-32\,{\text {dBm}}$ sensitivity), sensitivity up to $-55\,{\text {dBm}}$ (with maximum power of $1,2 \upmu{\text {W}}$ ), latency from $8\;\upmu\text{s}$ , tunable frequency, and short commands communication. In addition, a low power solution, which includes addressing capability directly in the wake-up receiver, is proposed. Experimental results and simulations demonstrate low power consumption, functionality, and benefits of the design optimization compared with other solutions, as well as the benefits of addressing false positive (FP) outcomes reduction.

Internet of Things (IoT) sensors are becoming more and more intelligence and unobtrusive, surrounding us in everyday life. A key requirement for those devices is to be “always-on” performing “smart” detection to catch only the events of interest when they occur. A recent and trend is to design low power circuit to extract features close to the sensors without an analog-digital conversion. The goal is to achieve place-and-forget deployment with perpetual operation: this poses the challenge of achieving micro-watt power consumption of the always-on analog event detector and of harvesting energy from environmental sources, to supply the smart sensor. This paper presents A2Event (Analog to Event), a microwatt programmable pattern recognition circuit for analog sensors, with up to 128 simultaneous time-frequency features exploiting mixed-signal low power design, which is able to generate an event when a targeted pattern is recognized. Moreover, the paper proposes a self-sustaining always-on smart audio detector that combines A2Event with energy harvesting to detect events of interests perpetually. Experimental results show that A2Event consumes only 26.89 μW in always-on mode, during the time-frequency feature-extraction, while the whole system consumes only 63 μW during pattern recognition including the power for a commercial MEMS microphone and the energy harvesting subsystem. We demonstrate that the whole smart sensor operates perpetually when powered with a small form factor flexible photovoltaic panel in indoor lighting conditions, achieving a detection accuracy of 100% in the detection of two different audio streams.

Self-sustainable always-on sensors are crucial for the Internet of Things and its emerging applications. However, achieving perpetual work with active sensors poses many challenges, especially in ultra-low power design and micro-power energy harvesting that can supply the sensors. This paper presents a self-sustaining programmable smart microphone, combining energy harvesting and a micro-power event-driven sensor. The proposed solution can achieve programmable pattern recognition with up to 128 simultaneous time-frequency features exploiting mixed-signal low power design. Experimental results show that the designed event-driven circuit consumes only 26.89 μW in always-on mode, during the time-frequency feature-extraction, while the whole system consumes only 63 μW during pattern recognition including the power for a commercial MEMS microphone and the energy harvesting subsystem. We demonstrate that the sensor can operate perpetually powered with a small form factor flexible photovoltaic panel in indoor lighting conditions. Finally, the smart sensors achieved an accuracy of 100% in the detection of two different audio streams.

The ‘smart cities’ concept is now becoming a reality: cities are increasingly connected and intelligent, with rapid advances in diverse areas including transportation, utilities, and municipal services. These can allow services to be delivered more efficiently and reliably, enriching residents' and visitors' experiences, and the data generated can be used for innovative new applications. While many sensors enabling these applications can be grid-powered, there is an increasing need for autonomous distributed or wearable sensing devices, which may also perform edge analytics. While these systems are typically powered by batteries so that they can be deployed quickly and cheaply, this comes with the cost of periodic battery replacement. This paper surveys the state-of-the-art in smart city sensing applications and considers their future directions, focusing on the power demands of sensors, and considerations for using energy harvesting.

Micro-energy harvesting from environmental sources is a promising technology for mobile or unobtrusive battery powered Internet of Things devices with constrains in terms of size and weight. Special integrated circuits aim to manage the voltage matching and power control in between the energy harvesting transducer and the storage. Hence, models to simulate the energy behaviour of these systems are important tools for system engineers. In this paper, a neuronal network model is presented to predict the performance of two state-of-the-art integrated circuits. Required model inputs are voltage and current from the energy harvesting transducer and the voltage level of the storage element. Model provides conversion efficiency as its output. Three different datasets have been collected with in-field experiments to build and evaluate the proposed model. Evaluation of the developed model on all datasets shows efficiency prediction root mean square error of less than 2 %.

Intra-body and extra-body communication is an attractive wireless communication method for wearable devices and their applications. In this work, we present the architecture, the design and the implementation of a novel zero-power receiver optimized for intra-body and extra-body communication and on-off keying modulation. The receiver includes an energy-harvesting subsystem that extracts energy from the received message to supply the low power circuits. The proposed zero-power receiver is able to receive and to parse data, as well as wake up external logic when a specific address is detected. The receiver can be used both as an always-on touch sensor to be deployed in the field and as a body communication wake up radio to other nodes on the human body. Experimental results with in-field measurements demonstrate the zero-power of the proposed device and that it is possible to achieve more than 1.70m in intra-body communication and hand-shake information exchange without the use of any external battery.

High precision Global Navigation Satellite System (GNSS) is a crucial feature for geo-localization to enhance future applications such as self-driving of vehicles. Real Time Kinematic (RTK) is a promising technology to achieve centimeter precision in GNSS, however it requires radio communication and to reduce power consumption this is done at meters range, reducing the use in navigation systems. In this work, we present a high precision low power systems that can be energetically autonomous. The proposed approach exploits a GNSS module with RTK combined with a long-range communication radio (LoRa) to achieve a high-precision localization system with minimal wireless radio infrastructure requirements. Wireless sensor nodes, designed to be energy efficient, comprise the system and they include a solar energy harvesting for self-sustainability. Preliminary experimental results, with in-field measurements, show an average accuracy below 1 meter up to more than 1km distance of the end-node from the geostationary reference anchor; with a peak accuracy of only 10cm. Low power consumption is also presented with in-filed measurements.

International audience; Wireless sensor and actuator networks play a central role in the Internet of Things, and a lot of effort is devoted to enable energy efficient and low latency communications. In the recent years, low power communications has evolved towards multi-kilometer ranges and low bit-rate approaches such as LoRa TM. However, the medium access layer protocols rely on the well-known duty-cycling schemes, which require a trade-off between power consumption and latency for message transfer from the gateway to the nodes. 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. Emerging ultra-low-power wake-up receivers enable pure-asynchronous communications, allowing both low latency and low power consumption, but at the cost of a lower sensitivity and lower range than traditional wireless receivers and LoRa TM. 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. A hardware architecture as well as a protocol is proposed to exploit the benefits of these two communication schemes. Experimental measurements and analytical comparisons show that the proposed approach remove the need for a trade-off between power consumption and latency.

For wearable devices, the availability of energy is one of the main limiting factors of performance and lifetime. To overcome this issue, micro-energy harvesting circuits, which extract energy from the environment, are very promising. Among other environmental sources, kinetic energy could significantly improve the energy availability in wearable applications. However, the majority of kinetic energy harvesting circuits do not perform well with the low-frequency patterns found in human motion and are therefore not suited for wearable devices. This paper aims to overcome this limitation by developing a high-efficiency energy harvesting system, which is optimized for frequencies occurring in human motion. A Micro Generator System (MGS) 26.4, by Kinetron, has been exploited as a kinetic transducer to generate energy. The implemented kinetic harvesting system has been designed to maximize the energy conversion efficiency and supply and recharge wearable devices. The final design has been implemented and field-tested in different positions on the human body. Experimental measurements demonstrate the end-to-end efficiency of up to 84%, and an average power of up to $\mathbf{624\mu W}$ , which is superior to the state-of-art for the type of MGS. Moreover, a preliminary evaluation of the correlation between acceleration and power harvested is presented. Using the values obtained from experimental data, we estimate that two hours of walking and 30 minutes of running per day can provide 1.4 joules of electrical energy.

Low-power and long-range communication tech- nologies such as LoRa are becoming popular in IoT applications due to their ability to cover kilometers range with milliwatt of power consumption. One of the major drawbacks of LoRa is the data latency and the traffic congestion when the number of devices in the network increases. Especially, the latency arises due to the extreme duty cycling of LoRa end-nodes for reducing the overall energy consumption. To overcome this drawback, we propose a heterogeneous network architecture and an energy-efficient On-demand TDMA communication scheme improving both the device lifetime and the data latency of standard LoRa networks. We combine the capabilities of micro- watt wake-up receivers to achieve ultra-low power states and pure asynchronous communication together with the long-range connectivity of LoRa. Experimental results show a data reliability of 100% and a round-trip latency on the order of milliseconds with end devices dissipating less than 46 mJ when active and 1.83 {\mu}W during periods of inactivity, lasting up to 3 years on a 1200 mAh Lithium battery., Comment: Accepted at WiMob 2018

One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multi-electrode-arrays (MEAs) to record brain signals at high spatiotemporal resolution. Data processing is needed to extract useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, machine learning approaches are increasingly used in many application scenarios. This paper focuses on processing data of evoked local field potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16 16 MEA. We evaluated machine learning algorithms and trained an optimized classifier to detect at which cortical depth the neural activity is measured. We demonstrate with experimental results that machine learning can be applied successfully to noisy single-trial LFPs offering up to 99.11% of test accuracy in classifying signals acquired from different cortical layers. As such, the method is a very promising starting point toward realtime decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications., 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), ISBN:978-1-5386-4294-8, ISBN:978-1-5386-9348-3, ISBN:978-1-5386-4295-5

This work focuses on nano-watt wake-up radio architectures designed to improve the energy efficiency of medium range wireless communication. An integrated solution is presented with the aim of pushing the sensitivity vs. power trade-off to the limit. The key blocks are an envelope detector based on a MOSFET operated in the subthreshold region and an amplifier partly sharing the same bias current. According to simulations, a design based on the STMicroelectronics 90-nm BCD technology achieves an estimated −54-dBm sensitivity at 1.2-V supply with 11-nA total bias current for the envelope detector, the amplifier and the Schmitt trigger. We also present a comparison between integrated solutions and a solution based on discrete components in terms of power consumption, bitrate, addressing capabilities and sensitivity.

With the recent improvement of flexible electronics, wearable systems are becoming more and more unobtrusive and comfortable, pervading fitness and health-care applications. Wearable devices allow non-invasive monitoring of vital signs and physiological parameters, enabling advanced Human Machine Interaction (HMI) as well. On the other hand, battery lifetime remains a challenge especially when they are equipped with bio-medical sensors and not used as simple data logger. In this paper, we present a flexible wristband for EMG gesture recognition, designed on a flexible Printed Circuit Board (PCB) strip and powered by a small form-factor flexible solar energy panel. The proposed wristband executes a Support Vector Machine (SVM) algorithm reaching 94.02 % accuracy in recognition of 5 hand gestures. The system targets healthcare and HMI applications, and can be used to monitor patients during rehabilitation from stroke and neural traumas as well as to enable a simple gesture control interface (e.g. for smart-watches). Experimental results show the accuracy achieved by the algorithm and the lifetime of the device. By virtue of the low power consumption of the proposed solution and the on-board processing that limits the radio activity, the wristband achieves more than 500 hours with a single 200 mAh battery, and perpetual work with a small-form factor flexible solar panel.

Achieving zero-power always-on sensing is an attractive challenge for academic and industrial researchers. Long-term and perpetual monitoring are particularly important for battery-operated systems, such as wearable and IoT devices and necessary in a wide range of applications. Zero-power sensing is crucial for devices that are supposed to collect data and important events in inaccessible places, such as under the water, where the replacement of batteries is almost impossible or inconvenient. In this paper, we present a novel ultra-low power always-on event-driven acoustic sensor able to feature pattern recognition with up to eight simultaneous time-frequency features exploiting mixed-signal low power design. Moreover, this paper achieves a zero-power acoustic smart sensor combining the event-driven acoustic detector with a microbial fuel cell, aiming long-term monitoring in underwater applications. Experimental results show that our solution is able to recognize a specific audio pattern in less than 1 seconds with 50μW to 55μW power consumption for single and multi-frequency detection respectively. Finally, we achieve a zero-power smart sensor able to work perpetually when powered with microbial fuel cells providing only 0.4mW of continuous power.

Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open- source to the research community., Comment: Accepted at WiMob 2018

Measuring radiation dosage rates is becoming more and more important in many applications scenarios. Continuously monitoring radiation in contaminated and poorly accessible areas is challenging due to the frequent data collection/transmission combined with long life requirements. We present a self-sustainable wireless sensor node for low power, high precision radiation dosage rate monitoring. We propose an energy-efficient data acquisition algorithm that can reduce the energy per measurement, while guaranteeing minimal loss of precision. The proposed node is designed to work in collaboration with an unmanned aerial vehicle used for two essential mission steps: air-deployment of the wireless sensor nodes at suitable locations, and acquiring data logs via low-power, short-range radio communication in fly-by mode after a wake-up command. The system uses off-the-shelf components for defining the mission, drop-zone, and trajectory, for compressing data and managing communication. The node is equipped with a novel low-power nuclear radiation sensor, and has been designed and implemented with self-sustainability in mind as it will be deployed in hazardous, inaccessible areas. To this end, the proposed node uses a combination of complementary techniques: a low-power microcontroller with non-volatile memory, energy harvesting, adaptive power management, and a nano-watt wake-up radio. Experimental results demonstrate the precision and the low energy consumption of the radiation sensor, the energy efficiency of the whole solution, and the acquisition algorithms. The node consumes only 31 $\mu \text{W}$ in sleep mode and 1.7mW in active mode, and has the capability to achieve perpetual monitoring once deployed.

Electronic skin (e-skin) is playing a key role in medicine, prosthetic and robot applications. E-skin tries to sense the world like the human skin does, exploiting arrays of sensors that produce a huge amount of data to be processed. Processing tactile data close to the e-skin imposes big challenges in terms of energy efficiency and real-time functionality. Energy efficient is particularly important for wearable and prosthetic applications where e-skin is mainly used as a wearable device supplied by battery. The major challenges it to find a sweet spot between power consumption and performance as tactile data decoding implementation requires high amount of computational power. This paper presents the hardware-software implementation of a complete low power embedded system that matches the computational requirements and the energy efficiency exploiting an ultralow power parallel processor. Tactile data decoding is directly performed on the embedded system, with a support vector machine (SVM) based tensor kernel algorithm, which classifies the input touch modalities. The paper presents the implementation of the algorithm in details and the power performance of the whole system based on experimental evaluation. Experimental results show that the energy efficiency is 9 times more than system based on a low power ARM-Cortex M4. Finally, the simulation on the energy consumption demonstrated that the developed system is able to last for 19.8 h in continuous mode with a single 2 Ah Lithium Polymer battery.