The development of safe and reliable autonomous autonomous aerial vehicles (AAVs) relies on the ability of the system to recognize and adapt to changes in the local environment based on sensor inputs. State-of-the-art local tracking and trajectory planning are typically performed using camera sensor input to the flight control algorithm, but the extent to which environmental disturbances like rain affect the performance of these systems is largely unknown. In this article, we first describe the development of an open dataset comprising ~335k images to examine these effects for seven different classes of precipitation conditions and show that a worst case average tracking error of 1.5 m is possible for a state-of-the-art visual odometry system (VINS-Fusion). We then use the dataset to train a set of deep neural network models suited to mobile and constrained deployment scenarios to determine the extent to which it may be possible to efficiently and accurately classify these “rainy” conditions. The most lightweight of these models (MobileNetV3 small) can achieve an accuracy of 90% with a memory footprint of just 1.28 MB and a frame rate of 93 FPS, which is suitable for deployment in resource-constrained and latency-sensitive systems. We demonstrate a classification latency in the order of milliseconds using typical flight computer hardware. Accordingly, such a model can feed into the disturbance estimation component of an autonomous flight controller. In addition, data from AAVs with the ability to accurately determine environmental conditions in real time may contribute to developing more granular timely localized weather forecasting.

The early detection of structural malfunctions requires the installation of real-time monitoring systems ensuring continuous access to the damage-sensitive information; nevertheless, it can generate bottlenecks in terms of bandwidth and storage. Deploying data reduction techniques at the edge is recognized as a proficient solution to reduce the systemfs network traffic. However, the most effective solutions currently employed for the purpose are typically based on memory and power-hungry algorithms, making their embedding on resourceconstrained devices very challenging; this is the case of vibration data reduction based on System Identification (SysId) models. This paper presents PARSY-VDD, a fully optimized PArallel end-to-end software framework based on SYstem identification for Vibration-based Damage Detection, as a suitable solution to perform damage detection at the edge in a time and energyefficient manner, avoiding streaming raw data to the cloud. First, we evaluate the damage detection capabilities of PARSYVDD with two benchmarks: a bridge and a wind turbine blade, showcasing the robustness of the end-to-end approach. Then, we deploy PARSY-VDD on both commercial single-core (STM32 family) and a specific multi-core (GAP9) edge device. We introduce an architecture-agnostic algorithmic optimization for SysId, improving the execution by 90× and reducing the consumption by 85× compared with the state-of-the-art SysId implementation on GAP9. Results show that by utilizing the unique parallel computing capabilities of GAP9, the execution time is 751 μ s with the high-performance multi-core solution operating at 370MHz and 0.8V, while the energy consumption is 37 μ J with the low-power solution operating at 240MHz and 0.65V. Compared with other single-core implementations based on STM32 microcontrollers, the GAP9 high-performance configuration is 76× faster, while the low-power configuration is 360× more energy efficient.

Battery-free devices collect the harvested ambient energy in their energy storage capacitors. The size of the storage capacitor is one of the main factors affecting the device’s active time and power failure rate. In fact, a larger capacitor ensures energy autonomy for longer operations, while a smaller capacitor charges faster and shrinks inefficient cold starts. This paper presents CapDYN , a new energy storage architecture that can self-adapt its capacity based on incoming ambient energy. CapDYN automatically reconfigures the size of its capacitor bank to both speed up charging and improve execution rate. CapDYN reduces the startup time by up to 98% and can schedule tasks up to 38% faster, compared to a fixed-size capacitor. CapDYN operates in a fully autonomous manner consuming down to 11.6 µW in its simplest implementation and replaces the power-hungry microcontroller governing the switching operation with a dedicated ultra-low-power circuit built with COTS components. Its power consumption improves the previous state of the art by 73%, all the while featuring uncompromising reactivity. CapDYN can instantly react to sudden power transients without incurring extra power draw by foregoing MCU-driven reconfiguration used in state-of-the-art dynamic energy storages.

This paper introduces a generic filter-based state estimation framework that supports two state-decoupling strategies based on cross-covariance factorization. These strategies reduce the computational complexity and inherently support true modularity – a perquisite for handling and processing meshed range measurements among a time-varying set of devices. In order to utilize these measurements in the estimation framework, positions of newly detected stationary devices (anchors) and the pairwise biases between the ranging devices are required. In this work an autonomous calibration procedure for new anchors is presented, that utilizes range measurements from multiple tags as well as already known anchors. To improve the robustness, an outlier rejection method is introduced. After the calibration is performed, the sensor fusion framework obtains initial beliefs of the anchor positions and dictionaries of pairwise biases, in order to fuse range measurements obtained from new anchors tightly-coupled. The effectiveness of the filter and calibration framework has been validated through evaluations on a recorded dataset and real-world experiments.

Multi-robot patrolling for dynamic coverage in flat environments is proposed, through a systematic simulative analysis between the Greedy Bayesian Strategy and the Expected Reactive algorithm based on the expected idleness. The two approaches are compared against unreliable communications, communication and sensing range, and number of conflicts. In addition, we introduce a new weighting-term for the regions close to a quantity of interest detected by robots, decreasing the passing-time for those regions. Combining the proposed control strategy and a traditional distributed and recursive Weighted Least Square estimation algorithm, the swarm is capable to compute the quantity of interest position with a desired target uncertainty. Extensive simulations and comparisons are reported.

With the progress in energy harvesting circuits and the decrease in power requirements of processing, sensing, and communication hardware, we have the potential of freeing the Internet of Things devices from their batteries. However, removing batteries introduces frequent power failures due to the irregular power availability from the environment. This situation leads devices to compute intermittently under transient environmental power. Intermittent computing requires significant microarchitectural modifications on existing processor designs to ensure automatic computation progress despite the power failures. For example, built-in nonvolatile memory components should be integrated in processor architectures. Consequently, different microarchitectural automatic backup strategies need to be implemented. In this work, we introduce different processor state backup strategies based on an interrupt-based software approach, which do not need modifications to the microarchitecture of existing processors. Therefore, we present a systematic approach to emulate different processor architectures with varying backup strategies under transient power. To justify our claims, we make Ibex RISC-V core, a popular ultralow-power processor architecture, suitable for intermittent computing. This is the first attempt to make a variety of existing and future ultralow-power processor architectures easily exploitable for transiently powered computing systems.

Backing up the intermediate results of hardware-accelerated deep inference is crucial to ensure the progress of execution on batteryless computing platforms. However, hardware accelerators in low-power AI platforms only support the one-shot atomic execution of one neural network inference without any backups. This article introduces a new toolchain for MAX78000, which is a brand-new microcontroller with a hardware-based convolutional neural network (CNN) accelerator. Our toolchain converts any MAX78000-compatible neural network into an intermittently executable form. The toolchain enables finer checkpoint granularity on the MAX78000 CNN accelerator, allowing for backups of any intermediate neural network layer output. Based on the layer-by-layer CNN execution, we propose a new backup technique that performs only necessary (urgent) checkpoints. The method involves the batteryless system switching to ultra-low-power mode while charging, saving intermediate results only when input power is lower than ultra-low-power mode energy consumption. By avoiding unnecessary memory transfer, the proposed solution increases the inference throughput by 1.9× for simulation and by 1.2× for real-world setup compared to the coarse-grained baseline execution.

Recent engineering efforts gave rise to the emergence of devices that operate only by harvesting power from ambient energy sources, such as radiofrequency and solar energy. Due to the sporadic ambient energy sources, frequent power failures are inevitable for these devices that rely only on energy harvesting. These devices lose the values maintained in volatile hardware state elements upon a power failure. This situation leads to intermittent execution, which prevents the forward progress of computing operations. To countermeasure power failures, these devices require non-volatile memory elements, e.g., FRAM, to store the computational state. However, hardware designers can only represent volatile state elements using FPGAs in the market and current hardware description languages. As of now, there is no existing solution to fast-prototype non-volatile digital logic. This paper enables FPGA-based emulation of any custom non-volatile digital logic for intermittent computing. Therefore, our proposal can be a standard part of the current FPGA libraries provided by the vendors to design and validate future non-volatile logic designs targeting intermittent computing.

Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing. However, traditional TinyML methods can only perform inference, limited to static environments or classes. Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model is no more suitable. For this reason, pre-trained models reduce accuracy and reliability during their lifetime because the data recorded slowly becomes obsolete or new patterns appear. Continual learning strategies maintain the model up to date, with runtime fine-tuning of the parameters. This paper compares four state-of-the-art algorithms in two real applications: i) gesture recognition based on accelerometer data and ii) image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs, with a drop in the accuracy of a few percentage points with respect to the original models for unconstrained computing platforms.

Energy-harvesting IoT devices that operate without batteries paved the way for sustainable sensing applications. These devices force applications to run intermittently since the ambient energy is sporadic, leading to frequent power failures. Unexpected power failures introduce several challenges to wireless communication since nodes are not synchronized and stop operating during data transmission. This paper presents a novel self-powered autonomous circuit design to remedy this problem. This circuit uses visible-light communication (VLC) to enable synchronization for time-slotted energy-aware transiently powered communication. Therefore, it aligns the activity phases of the batteryless sensors so that energy status communication occurs when these nodes are active simultaneously. Evaluations showed that our circuit has an ultra-low power consumption, can work with zero energy cost by relying only on the harvested energy, and supports efficient intermittent communication over intermittently powered nodes.

Modern real-time structural health monitoring (SHM) systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This article presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., principal component analysis (PCA), fully connected autoencoder (FC-AE), and convolutional autoencoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by$\approx 8\cdot 10^{5}\times $, from 780 kB/h to less than 10 Bytes/h for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A real-life case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart preprocessing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.

Today’s intermittent computing systems operate by relying only on harvested energy accumulated in their tiny energy reservoirs, typically capacitors. An intermittent device dies due to a power failure when there is no energy in its capacitor and boots again when the harvested energy is sufficient to power its hardware components. Power failures prevent the forward progress of computation due to the frequent loss of computational state. To remedy this problem, intermittent computing systems comprise built-in fast non-volatile memories with high write endurance to store information that persists despite frequent power failures. However, the lack of design tools makes fast-prototyping these systems difficult. Even though FPGAs are common platforms for fast prototyping and behavioral verification of continuously powered architectures, they do not target prototyping intermittent computing systems. This article introduces a new FPGA-based framework, named NORM ( N on-volatile mem OR y e M ulator), to emulate and verify the behavior of any intermittent computing system that exploits fast non-volatile memories. Our evaluation showed that NORM can be used to emulate and validate FeRAM-based transiently powered hardware architectures successfully.

Batteryless image sensors present an opportunity for long-life, long-range sensor deployments that require zero maintenance, and have low cost. Such deployments are critical for enabling remote sensing applications, e.g., instrumenting national highways, where individual devices are deployed far (kms away) from supporting infrastructure. In this work, we develop and characterize Camaroptera, the first batteryless image-sensing platform to combine energy-harvesting with active, long-range (LoRa) communication. We also equip Camaroptera with a Machine Learning-based processing pipeline to mitigate costly, long-distance communication of image data. This processing pipeline filters out uninteresting images and only transmits the images interesting to the application. We show that compared to running a traditional Sense-and-Send workload, Camaroptera’s Local Inference pipeline captures and sends upto \( 12\times \) more images of interest to an application. By performing Local Inference , Camaroptera also sends upto \( 6.5\times \) fewer uninteresting images, instead using that energy to capture upto \( 14.7\times \) more new images, increasing its sensing effectiveness and availability. We fully prototype the Camaroptera hardware platform in a compact, 2 cm \( \times \) 3 cm \( \times \) 5 cm volume. Our evaluation demonstrates the viability of a batteryless, remote, visual-sensing platform in a small package that collects and usefully processes acquired data and transmits it over long distances (kms), while being deployed for multiple decades with zero maintenance.

Nonintrusive load monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-art approaches are based on machine learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost resource-constrained microcontroller unit (MCU)-based meters is currently an open challenge. This article addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing state-of-the-art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline's implementation on an MCU-basedSmart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most accurate feature vector deployment (96.19%) while achieving up to 5.45× speedup and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware (HW) design.

Reliable communication and synchronization for transiently-powered batteryless sensors are still open challenges. This paper presents a method to synchronize and ensure reliable communication over batteryless sensors with zero energy cost requirements. Our preliminary design combines visible light communication (VLC) and radio-frequency (RF) backscatter into a self-powered autonomous circuit. We enable energy status sharing and communication scheduling services, which provide the fundamental building blocks for future batteryless communication protocols.

Principal component analysis (PCA) is a well-established approach commonly used for dimensionality reduction. However, its computational cost and memory requirements hamper the adoption of PCA in heavily resource-constrained embedded platforms. Streaming approaches have been proposed that may enable embedded implementations of the PCA. Among them, the history PCA (HPCA) algorithm stands out for its robustness to the variability in parameters and accuracy. This article presents a parallel and memory-efficient implementation of HPCA in a structural health monitoring (SHM) application based on a heterogeneous network with sensor nodes measuring three-axial accelerations and gateways collecting measurements from several nodes and sending them to the cloud storage and analytic facility. In the targeted application, standard PCA reaches 15x compression factor with an average reconstruction signal-to-noise ratio of 16 dB and a negligible impact on the accuracy in the tracking of structural modal frequencies. By embedding HPCA on our SHM network gateways, we achieve the same compression factor as standard PCA, with more than 1000x reduction in data memory footprint for running the algorithm. Furthermore, we parallelize HPCA on the gateway, and we achieve a speedup of 7.1x (on 8 cores). Finally, we explore a fixed-point HPCA implementation on sensors (network end nodes), that maximally distributes compression workload, minimizes required communication bandwidth, and maintains the same quality of reconstruction as HPCA in floating point, with a compression factor of 10x.

Interest in quantum computing is rapidly growing in the scientific community as such technology could be the key to enable intensive use of machine learning and big data algorithms. Quantum computers can be conceived from basic photonic elements, such as Mach-Zehnder interferometers (MZI), but they still need control mechanisms and sensing elements from traditional VLSI technology. In this study, we present an ongoing study on a potential architecture for these basic photonic circuits. We realized two Photonic Integrated Circuit (PIC) test structures embedding metallic thermistors as phase shifters and silicon photodiodes as output detectors. We present a complete characterization of the phase shifter elements for an effective on-chip PIC interaction. To induce a consistent phase shift still retaining coherence of light, we act on the phase-shifters by means of a closed-loop control. By controlling through Pulse-Width Modulation the phase shifters, we drive the path of photons accurately in the PIC, demonstrating the effectiveness of the proposed configuration for the management of a photonic chip.

This paper describes a system for soil moisture measurement based on a waveguide spectrometer that acquires “gain” and “phase” spectra, with a frequency range of 1.5 - 2.7 GHz. The fundamental components of the system are a waveguide, containing TX and RX antennas, and an electronic circuit, generating the electromagnetic waves and elaborating data, driven by a microcontroller (MCU). This system was previously tested on samples created in laboratory, but a study of system behavior in environmental conditions was missing. Therefore, acquisition of measurements and samples were conducted on real soil, and these data were used to obtain a good calibration model between spectra and soil moisture values. Calibration coefficients were used to implement the moisture calculation directly in the MCU, obtaining a non-invasive and stand-alone system. The architecture shows promising results and prove the capability of electromagnetic waves to perform accurate and fast silty clay loam moisture assessments on a real environment.

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.

Principal component analysis (PCA) is a powerful data reduction method for Structural Health Monitoring. However, its computational cost and data memory footprint pose a significant challenge when PCA has to run on limited capability embedded platforms in low-cost IoT gateways. This paper presents a memory-efficient parallel implementation of the streaming History PCA algorithm. On our dataset, it achieves 10x compression factor and 59x memory reduction with less than 0.15 dB degradation in the reconstructed signal-to-noise ratio (RSNR) compared to standard PCA. Moreover, the algorithm benefits from parallelization on multiple cores, achieving a maximum speedup of 4.8x on Samsung ARTIK 710.

This paper addresses the problem of target detection and localisation in a limited area using multiple coordinated agents. The swarm of Unmanned Aerial Vehicles (UAVs) determines the position of the dispersion of stack effluents to a gas plume in a certain production area as fast as possible, that makes the problem challenging to model and solve, because of the time variability of the target. Three different exploration algorithms are designed and compared. Besides the exploration strategies, the paper reports a solution for quick convergence towards the actual stack position once detected by one member of the team. Both the navigation and localisation algorithms are fully distributed and based on the consensus theory. Simulations on realistic case studies are reported.

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 10× less energy to transmit a payload equivalent to that of NB-IoT, thereby allowing longer end-device lifetime.

Sensory evaluation is used to assess the consumer acceptance of foods or other consumer products, so as to improve industrial processes and marketing strategies. The procedures currently involved are time-consuming because they require a statistical approach from measurements and feedback reports from a wide set of evaluators under a well-established measurement setup. In this paper, we propose to collect directly the signal of the perceived quality of the food from Event-related potentials (ERPs) that are the outcome of the processing of visual stimuli. This permits to narrow the number of evaluators since errors related to psychological factors are by-passed. We present the design of a wearable system for ERP measurement and we present preliminary results on the use of ERP to give a quantitative measure to the appearance of a food product. The system is developed to be wearable and our experiments demonstrate that is possible to use it to identify and classify the grade of acceptance of the food.

To tackle the modern need for gas sensing, mobile and miniaturized sensors have been deployed for many use cases, including on remote driven vehicles. Unmanned aerial vehicles (UAVs) are very promising for this type of measurement because of the possibility to sense hard to reach areas, without exposing the operator to risks. In this article, we present a coordinated swarm of UAVs equipped with gas sensors for industrial air pollution monitoring. The UAVs are coordinated by a ground control station to control the total working volume. We developed a coverage algorithm that elaborates and assigns the paths to each drone controlled by the swarm manager during the mission. The swarm of drones was tested with a real gas leakage and on a simulator, correctly moving into the area and detecting the leakage.

Structural health monitoring (SHM) systems are becoming increasingly widespread and are in some cases mandated by law. A major factor limiting the diffusion of such systems is the lack of low-cost low-power sensor nodes, which can be deployed in large numbers in hard-to-reach areas, while providing high-quality precise measurements over their entire lifespan with minimum maintenance and withstanding climatic stress. In this paper, we present a cost-effective wireless component for Structural Health Monitoring (SHM) that measure and track cracks in concrete and other construction materials. The sensor combines a microprocessor with LoRaWAN wireless communication, an analog transducer, and a solar energy harvester, allowing long-term remote monitoring with easy plug and play installation. Experimental results demonstrate that we achieved about 1μm accuracy and an expected lifetime of more than 10 years, with stable measurements across a-IS - 65°C temperature range.

LoRaWAN is one of the most promising standards for IoT applications. Nevertheless, the high density of end-devices expected for each gateway, the absence of an effective synchronization scheme between gateway and end-devices, challenge the scalability of these networks. In this article, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA instead of the classic ALOHA approach used by LoRa. The implementation is an overlay on top of the standard LoRaWAN; thus no modification in pre-existing LoRaWAN firmware and libraries is necessary. Our method is based on a novel distributed synchronization service that is suitable for low-cost IoT end-nodes. S-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.

This paper presents a low-cost system that permits to provide structural modal analysis from a number of synchronized MEMS accelerometers distributed along the structure. The communication and computational effort is distributed among the nodes and data are provided real-time. Synchronization between sensors and accuracy of the measures permit to elaborate detailed modal analysis of the buildings. Experiments on real testbeds and structures demonstrate that the accuracy of the modal analysis is similar to the simulations executed using the models of the structures, and confirm that complex structural health evaluations are possible from a set of low-cost sensors.

Going low power and having a low or neutral impact on the environment is key for embedded systems, as pervasive and wearable consumer electronics is growing. In this paper, we present a self-sustaining, ultra-low power device, supplied by a Plant-Microbial Fuel Cell (PMFC) and capable of smart sensing and long-range communication. The use of a PMFC as a power source is challenging but has many advantages like the only requirement of watering the plant. The system uses aggressive power management thanks to FRAM technology exploited to retain microcontroller status and to shutdown electronics without losing context information. Experimental results show that the proposed system paves the way to energy neutral sensors powered by biosystems available almost anywhere on Earth.

Wake-up radios (WuRs) are usually designed as a secondary near-zero power radio receiver used to trigger the main radio when a new communication is started over the air. Despite the tight relation with the main radio functions, it is usually implemented with separate hardware, firmware, and it is far from being integrated in the most diffused standard wireless protocols for sensors networks and for the Internet of Things (IoT). In this work, we want to check in-deep the coexistence between the Bluetooth Low Energy (BLE), which is one of the most used low-power short-range wireless standards for mobile and wearable communication, and a wake-up facility implemented in the same band. We focus on the analysis of the limits, advantages and drawbacks of adding this interesting feature in the BLE standard, and we discuss about the improvements in terms of power saving and latency of the communication. We target IoT applications and scenarios with a high density of BLE devices, analyzing the standard protocol and how WuRs can enhance its performance. Two common communication schemes will help us to show the limits and to define a design methodology for the application. Simulation results will show the trade-offs of the communication performance and the difference from a normal usage of the BLE. Finally, considerations about the usability of BLE with WuRs and guidelines will be provided as concluding remarks.

Transient computing systems do not have energy storage, and operate directly from energy harvesting. These systems are often faced with the inherent challenge of low-current or transient power supply. In this paper, we propose “power-neutral” operation, a new paradigm for such systems, whereby the instantaneous power consumption of the system must match the instantaneous harvested power. Power neutrality is achieved using a control algorithm for dynamic frequency scaling, modulating system performance gracefully in response to the incoming power. Detailed system model is used to determine design parameters for selecting the system voltage thresholds where the operating frequency will be raised or lowered, or the system will be hibernated. The proposed control algorithm for power-neutral operation is experimentally validated using a microcontroller incorporating voltage threshold-based interrupts for frequency scaling. The microcontroller is powered directly from real energy harvesters; results demonstrate that a power-neutral system sustains operation for 4%-88% longer with up to 21% speedup in application execution.

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.

Power line communication is becoming important as a leading technology in Smart Grid applications. This paper presents a new system to reduce the power consumption of the power line modems, implementing a zero-power stand-by feature. The incoming wake-up signal is as simple as a PLC frame and its associated energy is used to supply the wake-up logic. The system is mainly conceived for street lighting systems that use power line communication and where the stand-by power represents an issue because there are thousands of lamps in a city, and the total power consumption of the modems in stand-by mode is not negligible. This paper presents the architecture of the system, as well as the design choices. Experimental results of the system applied to a street light network show that it is possible to wake-up selectively a cluster of lamps up to a distance of about 250m.

Complementing the topics of ACM SenSys 2015, the 3rd International Workshop on Energy Harvesting and Energy Neutral Sensing Systems (ENSsys) 2015 brings together researchers to explore the challenges, issues and opportunities in the research, design, and engineering of energy-harvesting and energy-neutral sensing systems. These are an enabling technology for future applications in smart energy, transportation, environmental monitoring and smart cities. Innovative solutions in hardware for energy scavenging, adaptive algorithms, and power management policies are needed to enable uninterrupted operation. This one-day workshop features invited and peer reviewed talks on these areas, and provides a forum for feedback, discussion and networking.

We present a real-time optimization framework to manage Hybrid Residential Electrical Systems (HRES) with multiple Energy sources and heterogeneous storage units. HRES represents urban buildings where photovoltaic (PV) or other renewable sources are installed along with the traditional connection to the main grid. In this paper heterogeneous storage units are used to realize energy buffers for the exceeding energy produced by the renewable when buildings and the grid are not available to accept it. We considered two different battery banks as electric energy storage, in particular lead-acid as the primary one for its low price and low self-discharge rate; while the lithium-ion chemistry is used as secondary bank because of the higher energy density and higher number of cycles. The proposed optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill by managing the variability of the PV source, in price-varying scenarios. We used a Dynamic-Programming (DP) algorithm to schedule off-line the use of the lead-acid bank minimizing the number of cycles and the Depth-of-Discharge (DoD) under given irradiance forecasts and user load profiles. Forecasts of the user loads and of the renewable energy intake are introduced in the optimization. Moreover a Real-Time scheme is introduced to manage the lithium bank and to minimize the need and the purchase of energy from the Grid when the actual demand does not fit the forecast. Our simulation results outperform the state of the art where the efficiency of both banks is not taken into consideration, even if complex approaches based on DP are used.

Compressive sensing (CS) is a new approach to simultaneous sensing and compressing that is highly promising for fully distributed compression in wireless sensor networks (WSNs). While a wide investigation has been performed about theory and practice of CS for individual signals, real and practical cases, in general, involve multiple signals, extending the problem of compression from 1-D single-sensor to 2-D multiple-sensors data. In this paper the two most prominent frameworks on sparsity and compressibility of multidimensional signals and signal ensembles, Distributed compressed sensing (DCS) and Kronecker compressive sensing (KCS), are investigated. In this paper we compare these two frameworks against a common set of artificial signals properly built to embody the main characteristics of natural signals. We further investigate how, in a real deployment, DCS can be used to reduce the power consumption and to prolong lifetime. In particular an extensive analysis is performed using real commercial off-the-shelf (COTS) hardware evaluating how different kind of compression matrices can affect the jointly reconstruction, trying to achieve the better tradeoff between quality and energy expenditure.

Nodes in wireless sensor networks (WSNs) typically have limited power supply and networks are often expected to be functional for extended periods. Therefore, the minimization of energy consumption and the maximization of network lifetime are key objectives in WSN. This paper proposes an overlay, energy optimized, sensor network to extend the functional lifetime of an energy-intensive sensor network application. The overlay network consists of additional nodes that exploit recent advances in energy harvesting and wake-up radio technologies, coupled with an application specific, complementary, ultra-low power sensor. The experimental results and simulations demonstrate that this approach can ensure survivability of energy-inefficient sensor networks. Simulating applications using energy-intensive video cameras and air quality sensors, combined with the proposed overlayed ultra-low power sensor network, demonstrates that this approach can increase functional lifetime toward perpetual operation and is suitable for WSN applications in which complementarity exists between the required energy-intensive sensors and low-cost sensors that can be used as triggers.

One of the popular structural health monitoring (SHM) applications of both automotive and aeronautic fields is devoted to the non-destructive localization of impacts in plate-like structures. The aim of this paper is to develop a miniaturized, self-contained and low power device for automated impact detection that can be used in a distributed fashion without central coordination. The proposed device uses an array of four piezoelectric transducers, bonded to the plate, capable to detect the guided waves generated by an impact, to a STM32F4 board equipped with an ARM Cortex-M4 microcontroller and a IEEE802.15.4 wireless transceiver. The waves processing and the localization algorithm are implemented on-board and optimized for speed and power consumption. In particular, the localization of the impact point is obtained by cross-correlating the signals related to the same event acquired by the different sensors in the warped frequency domain. Finally the performance of the whole system is analysed in terms of localization accuracy and power consumption, showing the effectiveness of the proposed implementation.

The recent research efforts in smart grids and residential power management are oriented to monitor pervasively the power consumption of appliances in domestic and non-domestic buildings. Knowing the status of a residential grid is fundamental to keep high reliability levels while real time monitoring of electric appliances is important to minimize power waste in buildings and to lower the overall energy cost. Wireless Sensor Networks (WSNs) are a key enabling technology for this application field because they consist of low-power, non-invasive and cost-effective intelligent sensor devices. We present a wireless current sensor node (WCSN) for measuring the current drawn by single appliances. The node can self-sustain its operations by harvesting energy from the monitored current. Two AAA batteries are used only as secondary power supply to guarantee a fast start-up of the system. An active ORing subsystem selects automatically the suitable power source, minimizing power losses typical of the classic diode configuration. The node harvests energy when the power consumed by the device under measurement is in the range 10W÷10kW, which also corresponds to the range of current 50mA÷50A drawn directly from the main. Finally the node features a low-power, 32-bit microcontroller for data processing and a wireless transceiver to send data via the IEEE 802.15.4 standard protocol.

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, ‘hybrid’ energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straight-forward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field.

Data reduction strategy is one of the schemes employed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application of WSN are compared with well-known prediction algorithms such as ARIMA, LMS and WMA models. We implemented a real-world deployment using 32bit mote-class device. Overall, up to 96% transmission reduction is achieved using our Naive method, while still able to maintain a considerable level of accuracy at 0.5°C error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.

Data reduction strategy is one of the schemes employed to extend network lifetime. In this paper we present an implementation of a light-weight forecasting algorithm for sensed data which saves packet transmission in the network. The proposed Naive algorithm achieves high energy savings with a limited computational overhead on a node. Simulation results from realistic Building monitoring application of WSN are compared with well-known prediction algorithms such as ARIMA, LMS and WMA models. We implemented a real-world deployment using 32bit mote-class device. Overall, up to 96% transmission reduction is achieved using our Naive method, while still able to maintain a considerable level of accuracy at 0.5 °C error bound and it is comparable in performance to the more complex models such as ARIMA, LMS and WMA.

This paper presents the design, implementation and characterization of an energy-efficient smart power unit for a wireless sensor network with a versatile nano-Watt wake up radio receiver. A novel Smart Power Unit has been developed featuring multi-source energy harvesting, multi-storage adaptive recharging, electrochemical fuel cell integration, radio wake-up capability and embedded intelligence. An ultra low power on board microcontroller performs maximum power point tracking (MPPT) and optimized charging of supercapacitor or Li-Ion battery at the maximum efficiency. The power unit can communicate with the supplied node via serial interface (I2C or SPI) to provide status of resources or dynamically adapt its operational parameters. The architecture is very flexible: it can host different types of harvesters (solar, wind, vibration, etc.). Also, it can be configured and controlled by using the wake-up radio to enable the design of very efficient power management techniques on the power unit or on the supplied node. Experimental results on the developed prototype demonstrate ultra-low power consumption of the power unit using the wake-up radio. In addition, the power transfer efficiency of the multi-harvester and fuel cell matches the state-of-the-art for Wireless Sensor Networks.

The modeling of energy storage devices such as supercapacitors for wireless sensor networks is important for assessing performance of harvesting-aware routing protocols that could be key to a green energy future. In this paper, we present a circuit-based model (CBM), which is a good fit to the empirical data available in the existing literature. We compare it with a linear energy model (LEM) that is often used in literature, and show that the choice of models implies a significant difference in the throughput per node.

During recent years, there has been a growing interest on wireless sensor networks (WSNs) and on the opportunities opened by this technology. Since the energy consumption is a bottleneck in WSNs, reducing it has a significant impact on the applicability of this technology. Typically, the energy consumed by wireless communication and by power-hungry sensors as CMOS imagers or Gas sensors, is dominant over the power required for computation or other activities of the node. Hence, an efficient management of the resources leading to a reduction of unnecessary communication and minimizing the use of power-hungry sensor while keeping the same performance is desirable to extend the life-time of the network. In this paper we address the challenges of exploiting wake-up receivers and heterogeneous sensors in WSN applications to reduce the average power consumption of individual nodes. In particular, we show how to configure a WSN which includes Pyroelectric InfraRed (PIR) sensors, smart camera sensors and a nano-Watt wake up radio as secondary radio receiver to efficiently extend the autonomy of the system. The evaluation of the proposed approach shows a significant reduction of the activity of the primary radio and of the high power sensor while keeping the same accuracy. We prototyped and tested the nodes, and used their characterization to demonstrate through simulations the power consumption reduction and the life-time extension of the network in a typical surveillance application.

The problem of data sampling and collection in wireless sensor networks (WSNs) is becoming critical as larger networks are being deployed. Increasing network size poses significant data collection challenges, for what concerns sampling and transmission coordination as well as network lifetime. To tackle these problems, in-network compression techniques without centralized coordination are becoming important solutions to extend lifetime. In this paper, we consider a scenario in which a large WSN, based on ZigBee protocol, is used for monitoring (e.g., building, industry, etc.). We propose a new algorithm for in-network compression aiming at longer network lifetime. Our approach is fully distributed: each node autonomously takes a decision about the compression and forwarding scheme to minimize the number of packets to transmit. Performance is investigated with respect to network size using datasets gathered by a real-life deployment. An enhanced version of the algorithm is also introduced to take into account the energy spent in compression. Experiments demonstrate that the approach helps finding an optimal tradeoff between the energy spent in transmission and data compression.

A major issue for video surveillance embedded systems is the need to continuously perform a number of highly demanding operations even when the analyzed scene does not show peculiar or interesting features, so that power consumption is a critical issue. In this paper we present a low-power multimodal embedded video surveillance system aimed at detecting objects abandoned/removed in/from a static monitored scene. Energy-awareness is achieved by means of an efficient and scalable objects abandon/removal detection algorithm, a Linux governor that controls CPU frequency and operating mode so as to establish an optimal trade-off between fulfilling the application efficiency-accuracy requirements and maximizing battery life and, finally, a pyroelectric infrared sensor that allows to wake up the CPU only when video processing is actually needed.

Small autonomous embedded systems powered by means of energy harvesting techniques, have gained momentum in industry and research. This paper presents a simple, yet effective and complete energy harvesting solution which permits the exploitation of an arbitrary number of ambient energy sources. The proposed modular architecture collects energy from each of the connected harvesting subsystems in a concurrent and independent way. The possibility of connecting a lithium-ion or nickel-metal hydride rechargeable battery protects the system against long periods of ambient energy shortage and improves its overall dependability. The simple, fully analogue design of the power management and battery monitoring circuits minimizes the component count and the parasitic consumption of the harvester. The numerical simulation of the system behavior allows an in-depth analysis of its operation under different environmental conditions and validates the effectiveness of the design.

Advances in sensor technology, wireless mesh networking and embedded processors are pushing development of new technologies in Wireless Sensor Networks (WSN). The wide variety of potential applications and platforms makes hard to port and develop new applications to different platforms keeping smart and efficient behavior of the nodes of WSN. For this reason nowadays middlewares are emerging as a valid alternative to the resource-hungry Operating Systems for WSN. In particular, frameworks are valid tools to provide hardware abstraction by creating an intermediate layer that improves interoperability and reduces the development time. Frameworks make available a set of libraries, utilities and functions used to build up distributed systems and algorithms where energy and computational limitations of the individual sensors can be overcome. In this paper we present two interesting real case applications using SPINE2, a framework for signal processing in node environment, ported on Ember EM250 ZigBee platform. We show how to obtain a general behavior on the system that is not achievable without using a suitable framework.

GENESI develops structural health monitoring systems for critical infrastructures such as tunnels, bridges, dams, private and public buildings, providing cutting edge green wireless sensor networks technology. The main goal of the project is that of overcoming once and for the barriers that make current wireless sensor network-based monitoring systems unfit for many applications. GENESI will provide solutions for sensor network technology enabling virtually infinite network lifetime. The resulting Green sEnsor NEtworks for Structural monitoring will be truly pervasive, robust and will be able to automatically adapt to application requirements and end-users demands. This poster paper provides an agile synopsis of the structure, aims and objectives of GENESI, providing also its vision for structural health monitoring and expected outcomes.

One of the major challenges in embedded system is reduction of power consumption. So far most of the microprocessors provide power saving mechanisms by changing the power operating mode as well as the frequency and core voltage at runtime. One of best methods is the management of available resources and operating systems like Linux define subsystems for power consumption management which require coordination and cooperation of hardware, kernel, and user-space applications, offering power savings options when the CPU is active as well as when it is inactive. In this paper we present a multimodal embedded visual surveillance system for the detection of abandoned/removed objects which exploits a Linux governor to control CPU frequency and operating mode to establish an optimal trade-off between fulfilling the application response time and accuracy requirements and maximizing battery life. To adopt an aggressive power management and to keep the whole system in sleep mode, a pyroelctric infrared sensor is also used to wake up the CPU only when video processing is actually needed.

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that minimize the power consumption subject to performance constraints, we are concerned with optimizing the performance of an application while respecting the limited and time-varying amount of available power. In this paper, we address power management of, e.g., wireless sensor nodes which receive their energy from solar cells. Based on a prediction of the future available energy, we adapt parameters of the application in order to maximize the utility in a long-term perspective. The paper presents a formal model of the corresponding optimization problem including constraints concerning buffer sizes, timing, and rates. Instead of solving the optimization problem online which may be prohibitively complex in terms of running time and energy consumption, we apply multiparametric programming to precompute the application parameters offline for different environmental conditions and system states. In order to guarantee sustainable operation, we propose a hierarchical software design which comprises a worst-case prediction of the incoming energy. As a further contribution, we suggest a new method for approximate multiparametric linear programming which substantially lowers the computational demand and memory requirement of the embedded software. Our approaches are evaluated using long-term measurements of solar energy in an outdoor environment.

Low-cost and low-power video surveillance systems based on networks of wireless video sensors will enter soon the marketplace with the promise of flexibility, quick deployment and providing accurate and real-time visual data. Energy autonomy and efficiency of the implemented algorithms are undoubtedly the primary design challenges to be addressed on systems subject to low computational capabilities and memory constraints. In this paper we present a low-power video sensor node designed for low-cost video surveillance which is able to detect abandoned and removed objects. The system exploits multi-modal sensor integration which saves on-board power consumption. In particular a pyroelectric infrared (PIR) sensor is exploited to optimize the use of the camera, grabbing images only when required in order to obtain the maximum efficiency from event recognition. Our fixed-point ARM-based approach is characterized in terms of runtime execution and power consumption, while efficiency is demonstrated by experimental results and compared with floating point implementations.

Solar harvesting circuits have been recently proposed to increase the autonomy of embedded systems. One key design challenge is how to optimize the efficiency of solar energy collection under non stationary light conditions. This paper proposes a scavenger that exploits miniaturized photovoltaic modules to perform automatic maximum power point tracking at a minimum energy cost. The system adjusts dynamically to the light intensity variations and its measured power consumption is less than 1 mW. Experimental results show increments of global efficiency up to 80%, diverging from ideal situation by less than 10%, and demonstrate the flexibility and the robustness of our approach.

This paper is concerned with solar driven sensors deployed in an outdoor environment. We present feedback controllers which adapt parameters of the application such that a maximal utility is obtained while respecting the time-varying amount of available energy. We show that already simple applications lead to complex optimization problems, involving unacceptable running times and energy consumptions for resource constrained nodes. In addition, naive designs are highly susceptible to energy prediction errors. We address both issues by proposing a hierarchical control approach which both reduces complexity and increases robustness towards prediction uncertainty. As a key component of this hierarchical approach, we propose a new worst-case energy prediction algorithm which guarantees sustainable operation. All methods are evaluated using long-term measurements of solar energy in an outdoor setting. Furthermore, we measured the implementation overhead on a real sensor node.

Building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. We present a multi-modal video sensor node designed for low-power and low-cost video surveillance, traffic control and people detection based on wireless sensor networks. It is equipped with a solar energy harvesting unit, which extends the autonomy ofthe nodes considerably using a solar cell of 70 cm2 and exploits CMOS video camera and Pyroelectric InfraRed (PIR) sensors to reduce remarkably the power consumption of the system in absence of events. The on-board microprocessor enables image classification using algorithms basedon support vector machines (SVM). We describe hardware-software architecture of the video sensor node and characterization in terms of power consumption and accuracy. Finally simulation results demonstrate the effectiveness of multimodal video sensors powered by harvesting circuits.

Audio streaming over LR-WPAN is different from data communication because such wireless channels are limited in bandwidth and prone to errors. Nevertheless there are several scenarios such as distributed surveillance and emergency response, where lightweight multimedia applications are compelling solutions for pervasive and ubiquitous computing. This paper investigates the feasibility of transmitting voice over ZigBee compliant platforms using commercial off-the-shelf (COTS) hardware. We present an implementation of a ZigBee Push-To-Talk application which allows to investigate the capability of ZigBee protocol for low-rate voice streaming and to analyze important streaming metrics such as packet loss over a multi-hop sensor network, evaluating Unicast and Broadcast addressing. Finally, we analyze the trade-off between audio quality and power consumption comparing different compression algorithms and we draw hard conclusions about specific improvements which would be needed to support higher data rates.

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal utility is obtained while respecting the limited and time-varying amount of available energy. Instead of solving the optimization problem on-line which may be prohibitively complex in terms of running time and energy consumption, we propose a parameterized specification and the computation of a corresponding optimal on-line controller. The efficiency of the new approach is demonstrated by experimental results and measurements on a sensor node

This paper investigates real-time scheduling in a system whose energy reservoir is replenished by an environmental power source. The execution of tasks is deemed primarily energy-driven, i.e., a task may only respect its deadline if its energy demand can be satisfied early enough. Hence, a useful scheduling policy should account for properties of the energy source, capacity of the energy storage as well as power dissipation of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we state and prove optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Furthermore, an offline schedulability test for a set of periodic or even bursty tasks is presented. Finally, we validate the proposed theory by means of simulation and compare our algorithms with the classical earliest deadline first algorithm