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IPS is a crucial technology that enables medical staff and hospital management to accurately locate and track persons or assets inside medical buildings. Among other technologies, readily available BLE can be exploited to achieve an energy-efficient and low-cost solution. This work presents the design, implementation and comparison of a RSSI-based and a MCPD-based indoor localization system. The implementation is based on a lightweight wkNN algorithm that processes RSSI and MCPD distance data from connection-less BLE Beacons. The designed hardware and firmware are implemented around the state-of-the-art SoC for BLE, the nRF5340 from Nordic Semiconductor. Experimental evaluation with real-time data processing has been evaluated and presented in a 7.3 m × 8.9 m room with furniture and six beacon nodes. The experimental results on randomly chosen validation points within the room show an average error of only 0.50 m for the MCPD approach, whereas the RSSI approach achieved an error of 1.39 m. [ABSTRACT FROM AUTHOR]

As the wind energy industry is maturing and wind turbines are becoming larger, there is an increasing need for cost-effective monitoring and data analysis solutions to understand the complex aerodynamic and acoustic behaviour of the flexible blades. Published measurements on operating rotor blades in real conditions are very scarce due to the complexity of the installation and use of measurement systems. However, recent developments in electronics, wireless communication and MEMS (micro-electromechanical systems) sensors are making it possible to acquire data in a cost-effective and energy-efficient way. In this work, therefore, a costeffective MEMS-based aerodynamic and acoustic wireless measurement system that is thin, non-intrusive, easy to install, low power and self-sustaining is designed and tested in a wind tunnel. The measurement system does not require an electrical connection to the wind turbine and can be mounted and removed without damaging the blade. The results show that the system is capable of delivering relevant results continuously, although work needs to be done on calibrating and correcting the pressure signals as well as on refining the concept for the attachment sleeve for weather protection in the field. Finally, two methods for using the measurements to provide added value to the wind energy industry are developed and demonstrated: (1) inferring the local angle of attack via stagnation point detection using differential pressure sensors near the leading edge and (2) detecting and classifying leading edge erosion using instantaneous snapshots of the measured pressure fields. Ongoing work involves field tests on a 6kW operating wind turbine in Switzerland. [ABSTRACT FROM AUTHOR]

Objective: The bio-impedance (BI) of the body is a powerful parameter to assess the tissue composition and fluid content. Widespread implementation of BI measurement systems is limited due to the inability to detect, interpret and suppress frequently occurring artefacts during continuous use. A major proportion of the artefacts in BI measurements are caused at the skin electrode (SE) interface. Perturbations at the SE interface due to motion, applied pressure, temperature and posture change introduce distortions in the measured data which impede accurate interpretation for physiological monitoring. This paper investigates and accurately presents the effects of the aforementioned actions on the SE interface to develop effective algorithms for artefact identification and suppression at multiple frequencies in-field. Methods: Data collected from 20 subjects is used to analyze artefact associated features due to SE perturbations in BI measurements between 10kHz to 100kHz. Results: High variance noise, concept drifts and abrupt changes in measured BI baselines were identified as the main characteristics associated with artefacts due to SE interface perturbation. Artefacts were identified with a precision of upto 95% and a recall of upto 96% for all observed artefact labels. Conclusion: The characterization of artefacts due SE interface perturbations and high performance algorithms for artefact identification and suppression allow artefact free continuous monitoring of BI. Significance: The presented work enables continuous measurement of BI for non-invasive monitoring with wearable low power devices and permits accurate interpretation of many physiological processes, such as, edema onset, dehydration or urinary bladder filling, even in the presence of artefacts. [ABSTRACT FROM AUTHOR]

Wearable, intelligent, and unobtrusive sensor nodes that monitor the human body and the surrounding environment have the potential to create valuable data for preventive human-centric ubiquitous healthcare. To attain this vision of unobtrusiveness, the smart devices have to gather and analyze data over long periods of time without the need for battery recharging or replacement. This article presents a software-configurable kinetic energy harvesting and power management circuit that enables self-sustainable wearable devices. By exploiting the kinetic transducer as an energy source and an activity sensor simultaneously, the proposed circuit provides highly efficient context-aware control features. Its mixed-signal nano-power context awareness allows reaching energy neutrality even in energy-drought periods, thus significantly relaxing the energy storage requirements. Furthermore, the asynchronous sensing approach also doubles as a coarse-grained human activity recognition frontend. Experimental results, using commercial micro-kinetic generators, demonstrate the flexibility and potential of this approach: the circuit achieves a quiescent current of 57 nA and a maximum load current of 300 mA, delivered with a harvesting efficiency of 79%. Based on empirically collected motion data, the system achieves an energy surplus of over 232 mJ per day in a wrist-worn application while executing activity recognition at an accuracy of 89% and a latency of 60 s. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Animals can generate sounds that serve a wide range of vital functions such as to defend themselves or their territories, to attract a partner, to maintain contact with other members of their social group, and to help themselves and their partner/group during navigation. Ethologists are interested in recording and analyzing these sounds, many of which are vocalizations. Advances in sensing and wireless technology permit today acoustic data acquisition and transmission in a wireless manner. In many applications, the wireless sensor needs to be placed on the animal’s body and should be unobtrusive, light-weight, small, and long-lasting. This paper presents the design and development of an ultra-low power miniaturized and lightweight wireless sensor node for monitoring captive zebra finches. The node is designed to be worn with minimal effort by small-sized birds to collect, process, and send/receive data to/from a remote host via Bluetooth Low-Energy. The main feature of the developed node is the capability to stream compressed or uncompressed audio and temperature data continuously. Multiple nodes can monitor several birds simultaneously and acquire and transmit high-quality audio streams, one for each bird, with low audio interference. Due to the combination of low-power hardware and software techniques and technologies, the 1.4 g node achieves a lifetime of up to 24 h at 4 kHz sampling rate on a single zinc-air battery. Experimental results on birds confirm the functionality of the developed wireless node and the lifetime benefits of compression. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rockfalls have over the last decades become a serious and frequent hazard, especially due to larger variations in precipitation and temperatures, destabilizing rocky slopes in mountainous regions. Hence, civil engineers are applying the latest simulation tools to perform risk assessments and plan mitigation strategies. These tools are based on various models with many parameters that should be calibrated and evaluated with real-world in-field measurement data. In this paper, we present a rugged low-power multisensor node termed StoneNode that has been designed to acquire and log accurate inertial sensor measurements during induced in-field experiments with falling rocks. The node hosts low-power microelectromechanical system sensors with high dynamic ranges sampled up to 1 kHz, and provides a long battery lifetime of up to 56 h, enabling long-lasting field studies with a duration of several working days. Exhaustive in-field experiments have been carried out with several differently shaped rocks on typical terrain in the Swiss alpine region. The experiments comprise more than 100 induced tests with several heavy impacts of >400 g. This paper gives a detailed summary of these results, including unprecedented in situ data of rockfall trajectories and postexperimental validation where we compare simulated rockfall deposition distributions and motion traces with in-field measurements after calibration of the simulation module. Our results and experience gained in-field confirm that the StoneNode is a reliable easy-to-use device, which greatly facilitates the data acquisition process. Further, the results obtained with the calibrated simulation tool show good quantitative and qualitative congruence with the experiments, further reaffirming our methodological approach. [ABSTRACT FROM PUBLISHER]

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 \textW in sleep mode and 1.7mW in active mode, and has the capability to achieve perpetual monitoring once deployed. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

MEMS sensor technology and advances in electronics, low-power processors and communication have enabled ubiquitous monitoring, providing significant opportunities for a wide range of applications including wearable devices for fitness and health tracking. However, due to the limited form factor required, there remains a challenging issue that limits even more the success of wearable devices: the limited lifetime due to the small energy storages that supply the devices. This limitation affects usability and forces the data processing to keep low-complexity to match the power constraints. As wireless communication is typically the most power hungry activity in wearable sensors devices, many techniques focus on reducing the communication power consumption. For this reason, advanced power management can be exploited to increase the lifetime of the devices. In this work, we present a wireless body area network with an adaptive power management strategy combining an ultra-low power wake up radio with context awareness. The context aware power manager is based on activity recognition, which is evaluated to decide which other nodes must be activated. The nano-power wake up receiver is used to reduce the idle listening power of the main radio and enable an asynchronous ultra-low power protocol. In order to evaluate the benefit, we present a real world application to assist elderly people in gait rehabilitation through a closed loop feedback. Experimental results demonstrate the benefit of the proposed power management in terms of energy efficiency. We evaluate the overall power consumption of the system and the lifetime extension, which can increase up to a factor of 4 depending on the amount of time the system can be placed in sleep mode. [ABSTRACT FROM AUTHOR]

Recent advances in biomaterials, thin film processing, and nanofabrication offer the opportunity to design electronics with novel and unique capabilities, including high mechanical stability and biodegradation, which are relevant in medical implants, environmental sensors, and wearable and disposable devices. Combining reliable electrical performance with high mechanical deformation and chemical degradation remains still challenging. This work reports temperature sensors whose material composition enables full biodegradation while the layout and ultrathin format ensure a response time of 10 ms and stable operation demonstrated by a resistance variation of less than 0.7% when the devices are crumpled, folded, and stretched up to 10%. Magnesium microstructures are encapsulated by a compostable-certified flexible polymer which exhibits small swelling rate and a Young's modulus of about 500 MPa which approximates that of muscles and cartilage. The extension of the design from a single sensor to an array and its integration onto a fluidic device, made of the same polymer, provides routes for a smart biodegradable system for flow mapping. Proper packaging of the sensors tunes the dissolution dynamics to a few days in water while the connection to a Bluetooth module demonstrates wireless operation with 200 mK resolution prospecting application in food tracking and in medical postsurgery monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To enable pervasive exploitation of wireless sensor networks, semipassive wake-up radios (WuRs) 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, which is limited by the minimum turn-on voltage of the 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 while preserving the required average power per bit. The design and implementation of an ultralow-power detector, fed by a dual-band antenna and loaded by the WuR backend 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 setup and confirmed by a system implementation based on off-the-shelf components only. [ABSTRACT FROM PUBLISHER]

Air quality monitoring is gaining importance in public health due to the increasing level of the pollution in cities. In addition, people are more concerned about their personal exposure and they are interested to know the concentration levels of the pollutants, which surround them. In recent years, a wearable technology can be useful for continuous air quality monitoring when people are moving in urban and industrial environments. As wearable systems are usually battery-powered and gas sensors are power-hungry, energy-efficient design and power management are required. In this paper, we present a two-stage gas sensing concept where novel multiple-single-walled carbon nanotubes (SWCNT) are proposed as detectors for an energy-hungry metal–oxide (MOX)–semiconductor gas sensor. We simulate the system performance combining the low power consumption of SWCNT gas sensors and the more mature MOX sensor to achieve an energy-efficient wearable device able to monitor the air quality continuously while achieving long lifetime. We perform the simulations using measured power consumptions for two event-driven scenarios to evaluate the power consumption reduction and lifetime extension in a wearable mobile context. Our results show that the proposed approach prolongs node lifetimes by 30 times compared with adaptive duty-cycling with only MOX gas sensors. We also propose that the nanotube recovery time issue can be overcome by using four single nanotubes on the same chip, which results in an extension of lifetime. [ABSTRACT FROM PUBLISHER]

Wearable technology is gaining popularity, with people wearing everything “smart” from clothing to glasses and watches. Present-day wearables are typically battery-powered, and their limited lifetime has become the critical issue. Most devices need recharging every few days or even hours, falling short of the expectations for a truly satisfactory user experience. This paper presents the design, implementation and in-field evaluation of InfiniTime , a novel sensor-rich smart bracelet powered by energy harvesting. It is designed to achieve self-sustainability using solar cells with only modest indoor light levels and thermoelectric generators (TEG’s) with small temperature gradients from the body heat. The wearable device is equipped with an ultra-low power camera and a microphone, in addition to accelerometer and temperature sensors commonly used in commercial devices. Experimental characterization of the fully operational prototype demonstrates a wide range of energy optimization techniques used to achieve self-sustainability with harvested energy only. Our experiments in real-world scenarios show an average of up to 550 μW for photovoltaic in indoor and 98 μW for TEG with only 3 ° temperature gradient and up to 250 μW for 5° gradient. Simulations using energy intake measurements from solar and TEG modules confirm that InfiniTime achieves self-sustainability with indoor lighting levels and body heat for several realistic applications featuring data acquisition from the on-board camera and multiple sensors, as well as visualization and wireless connectivity. The highly optimized low-power architecture of the presented prototype features image acquisitions at 1.15 frames per second, powered only from the energy harvesters. [ABSTRACT FROM AUTHOR]

Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology inWireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. [ABSTRACT FROM AUTHOR]

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\texts, 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. [ABSTRACT FROM PUBLISHER]