Energy Harvesting Sensor Research Articles

Corrosion Monitoring Sensor Networks With Energy Harvesting

Reinforcing concrete (RC) structures are applied extensively in civil engineering. The corrosion of RC steel, often called the cancer of the steel, however, deteriorates the durability of RC structures greatly and then degrades the serviceability severely. The usage of sensors in the RC corrosion monitoring is one potential method where two problems have to be carefully considered. First, the long-term power provision of sensor is very hard to be implemented in microsensor systems; second, the data delivery of corrosion monitoring ought not to impact the original structure where sensors are embedded. In this letter, we discuss and design a framework of energy-harvesting sensor network to monitor the steel corrosion in RC structure. Our framework provides insights about the corrosion-monitoring sensor system to store its ambient energy, and about effectively exploiting wireless sensor network to deliver the corrosion data to the end user.

Energy Harvesting Sensor Nodes: Survey and Implications

Sensor networks with battery-powered nodes can seldom simultaneously meet the design goals of lifetime, cost, sensing reliability and sensing and transmission coverage. Energy-harvesting, converting ambient energy to electrical energy, has emerged as an alternative to power sensor nodes. By exploiting recharge opportunities and tuning performance parameters based on current and expected energy levels, energy harvesting sensor nodes have the potential to address the conflicting design goals of lifetime and performance. This paper surveys various aspects of energy harvesting sensor systems- architecture, energy sources and storage technologies and examples of harvesting-based nodes and applications. The study also discusses the implications of recharge opportunities on sensor node operation and design of sensor network solutions.

Characterization of ferroelectric material properties of multifunctional lead zirconate titanate for energy harvesting sensor nodes

We propose a microsystem integration technique that is ideal for low-cost fabrication of vibration energy harvesting sensor nodes. Our approach exploits diverse uses of sol-gel deposited lead zirconate titanate, effectively combining fabrication of several microsystem components into a single process and significantly reducing manufacturing cost and time. Here, we measure and characterize thin film parameters—such as the piezoelectric coefficient e31 (−4.0 C/m2), the dielectric constant εr-eff (219 at 3.3 V), and the total switching polarization (2Pr;52 μC/cm2)—in order to verify this material’s potential for energy harvesting, energy storage, and nonvolatile memory applications simultaneously on the same device.

A redistribution method to conserve data in isolated energy-harvesting sensor networks

In ambient monitoring applications, the sensing field may be so far away from the data center that causes the direct relay routes between the sensor network and the data center impossible. Typically, in such isolated sensor network, data is stored in a distributed manner and collected by data mule. To improve the efficiency, sensed data is normally stored near the area where the mule will pass by with respect to storage limitation. However, previous researches didn?t consider the energy constraint and energy harvesting capability of nodes. The purpose of this paper is to design a solution for fair data storage under space and energy limitation only based on local information. We propose a heuristic Distributed Energy-aware Data Conservation method (DEDC), which considers following two issues: i)where to store data with respect to energy and space storage, ii) how to prioritize the transmission of important data. Simulation has shown that the method is effective, energy efficient and robustness.

An Ultralow-Power Receiver for Wireless Sensor Networks

An ultralow-power super-regenerative receiver for BFSK modulated signals has been designed and fabricated in a standard 0.18-μm CMOS process. The use of BFSK modulation allows the receiver to operate at higher data rates and also gives an approximate 3-dB SNR performance increase over the more traditional OOK modulation. A fast calibration scheme and the absence of an external inductor make it ideal for ultralow-power sensor networks. A power consumption of 215 μW from a 0.65-V supply and an area of 0.55 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> make it ideal for highly integrated energy harvesting sensor nodes. At 2 Mb/s, the receiver consumes 0.18 nJ/b, making it the lowest energy integrated super-regenerative receiver to date.

Multi-layer compounds with integrated actor-sensor-functionality

A method is presented to integrate the cost and time consuming afterwards-joining technologies of piezo actors and sensors direct in the forming processes for metal blank structures. Possible applications for such parts are vibration/ noise damping, deformable shape control, energy harvesting or several sensor tasks. Different forming processes are experimentally investigated and the limits according to deformation of the brittle piezo components discussed. In the numerical research the piezomodule components (piezo fibre, electrodes and plastics embeddings) are homogenized to create a computation-time reducing simplified material model. In a back-transfer of global loads in the forming simulation a representative volume element (RVE) with cyclic boundary conditions is used to evaluate the loading of the piezoceramic material to describe the function degradation due to forming operation. The comparison of numerically and experimentally determined results in a linear manner lead to the necessarity of further numerical research. The location of maximum piezo-patch loading corresponds well with the numerical investigation. The numerical integral model for function degradation shows a large difference in comparison to the integral experimentally determined values. Therefore extensive experimental research direct on the piezomodule outside the forming compound is planned to fit the degradation model in a nonlinear manner.

HOLLOWS: A Power-aware Task Scheduler for Energy Harvesting Sensor Nodes

Energy harvesting sensor nodes (EHSNs) have stringent low-energy consumption requirements, but they need to concurrently execute several types of tasks (processing, sensing, actuation, etc.). Furthermore, no accurate models exist to predict the energy harvesting income in order to adapt at run-time the executing set of prioritized tasks. In this article, we propose a novel power-aware task scheduler for EHSNs, namely, HOLLOWS: Head-of-Line Low-Overhead Wide-priority Service. HOLLOWS uses an energy-constrained prioritized queue model to describe the residence time of tasks entering the system and dynamically selects the set of tasks to execute, according to system accuracy requirements and expected energy. Moreover, HOLLOWS includes a new energy harvesting prediction algorithm, that is, weather-conditioned moving average (WCMA), which we have developed to estimate the solar panel energy income. We have tested HOLLOWS using the real-life working conditions of Shimmer, a sensor node for structural health monitoring. Our results indicate that HOLLOWS accurately predicts the energy available in Shimmer to guarantee a certain damage monitoring quality for long-term autonomous scenarios. Also, HOLLOWS is able to adjust the use of the incoming energy harvesting to achieve high accuracy for rapid event damage assessment (after earthquakes, fires, etc.).

Energy Efficiency in Wireless Sensor Networks: Selected Papers from IEEE PIMRC 2008

Wireless Sensor Networks (WSNs) have emerged as perhaps the most important networking paradigm of recent times, both in terms of their commercial potential and also from a scientific point of view. WSNs are important commercially because of their many applications, spanning from civil to military applications such as structural monitoring of bridges, monitoring of wildlife, tracking of contaminants in the soil and atmosphere. Some of these applications have already proved to be commercially viable and indeed financially very attractive. WSNs are also very important from a scientific point of view, because of their unique features with respect to other types of wireless networks: nodes are typically immobile, and are required to carry a specific type of traffic (which is advantageous), have extreme restrictions on the energy they consume, the processing power they have, and the antennas they can use (which is disadvantageous); in addition, the data created at different sensors may be strongly correlated, which can be beneficial but requires only good algorithms that take advantage of it. Therefore, in the last few years we have experienced an important shift in research activities, moving from general purpose wireless networks to more focused topics specifically on WSNs, taking into account their specific advantages and peculiarities. An important recurring theme in research in this field is the need for energy efficiency, which is the thread of this special issue. The papers which appear in this issue have been carefully selected from the best IEEE PIMRC 2008 conference papers addressing the issue of energy efficiency in WSNs. The respective authors have been asked to provide a significantly extended version of the conference paper, which has subsequently undergone a rigorous review process according to the IJWIN publication standards prior to acceptance. The final set of papers addresses issues at physical, medium access control and networking layers, and to some extent at the application layer. All of these papers take into account the unique peculiarities, advantages, and shortcomings of wireless sensor networks, and propose algorithms and analyses that significantly advance research in the field. We sincerely hope you will enjoy reading them as much as we did. The author of the first paper ‘‘Power Management and Data Rate Maximization in Wireless Energy Harvesting Sensors’’ by Chandra R. Murthy, deals with the problem of power management and throughput maximization for energy neutral operation when using increasingly important but so far rarely analyzed Energy Harvesting Sensors (EHS) to send data over wireless links. The EHS is assumed to be able to harvest energy at a constant rate, and use a fixed part of the energy harvested in a slot for measuring the channel state. The rest of the harvested energy is available for transmission, but it can also be stored in an Wireless Sensor Networks (WSNs) have emerged as perhaps the most important networking paradigm of recent times, both in terms of their commercial potential and also from a scientific point of view. The papers that appear in this issue have been carefully selected from the best IEEE PIMRC 2008 conference papers which addressed the issue of energy efficiency in WSNs. The final set of papers addresses issues at physical, medium access control, networking and to some extend application layers. All of these papers take into account the unique peculiarities, advantages, and shortcomings of wireless sensor networks, and propose algorithms and analyses that significantly advance research in the field. Note that this activity has been partly supported by the NoE-216715 NEWCOM++.

Power Management and Data Rate Maximization in Wireless Energy Harvesting Sensors

This paper considers the problem of power management and throughput maximization for energy neutral operation when using an energy harvesting sensor (EHS) to send data over a wireless link. The EHS is assumed to be able to harvest energy at a constant rate, and use a fixed part of the energy harvested in a slot for measuring the channel state. The rest of the energy harvested is available for transmission, however, it can be stored in an inefficient battery if it is not fully utilized. The key constraint that the EHS needs to satisfy is energy neutrality, i.e., the expected energy drawn from the battery should equal the expected energy deposited into the battery. In this scenario, two popular models for data transmission are contrasted: the constant bit rate (CBR) model and the variable bit rate (VBR) model. In the CBR model, it is assumed that the EHS are designed to transmit data at a constant rate (using a fixed modulation and coding scheme) but are power-controlled. In the VBR model, the EHS selects both the transmit power and the data rate of transmission in each slot based on the channel instantiation. A framework under which the system designer can optimize several parameters of the EHS that determine the average data rate performance when the channel is Rayleigh fading is developed. Using this framework, the two transmission schemes are contrasted. It is shown that, with the right choice of parameter settings, the CBR scheme can perform nearly as well as the VBR scheme at significantly lower complextiy. The usefulness and validity of the framework developed is illustrated through simulations for specific examples.

Design and evaluation of a hybrid sensor network for cane toad monitoring

This article investigates a wireless acoustic sensor network application—monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing, wide area sensing coverage and long-lived unattended operation. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to ninth frog species found in northern Australia. Our first prototype consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates four algorithms designed to account for the sampling, processing, energy, and communication bottlenecks of the Mica2s (1) high frequency sampling, (2) thresholding and noise reduction, to reduce data transmission by up to 90%, (3) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate, and (4) harvesting-aware energy management, which exploits sensor energy harvesting capabilities to extend the system lifetime. Our evaluation shows the performance of our systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

Energy Harvesting Chip and the Chip Based Power Supply Development for a Wireless Sensor Network.

In this study, an energy harvesting chip was developed to scavenge energy from artificial light to charge a wireless sensor node. The chip core is a miniature transformer with a nano-ferrofluid magnetic core. The chip embedded transformer can convert harvested energy from its solar cell to variable voltage output for driving multiple loads. This chip system yields a simple, small, and more importantly, a battery-less power supply solution. The sensor node is equipped with multiple sensors that can be enabled by the energy harvesting power supply to collect information about the human body comfort degree. Compared with lab instruments, the nodes with temperature, humidity and photosensors driven by harvested energy had variation coefficient measurement precision of less than 6% deviation under low environmental light of 240 lux. The thermal comfort was affected by the air speed. A flow sensor equipped on the sensor node was used to detect airflow speed. Due to its high power consumption, this sensor node provided 15% less accuracy than the instruments, but it still can meet the requirement of analysis for predicted mean votes (PMV) measurement. The energy harvesting wireless sensor network (WSN) was deployed in a 24-hour convenience store to detect thermal comfort degree from the air conditioning control. During one year operation, the sensor network powered by the energy harvesting chip retained normal functions to collect the PMV index of the store. According to the one month statistics of communication status, the packet loss rate (PLR) is 2.3%, which is as good as the presented results of those WSNs powered by battery. Referring to the electric power records, almost 54% energy can be saved by the feedback control of an energy harvesting sensor network. These results illustrate that, scavenging energy not only creates a reliable power source for electronic devices, such as wireless sensor nodes, but can also be an energy source by building an energy efficient program.

Optimal energy management policies for energy harvesting sensor nodes

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue.We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.

Scaling self-timed systems powered by mechanical vibration energy harvesting

Passive energy harvesting from mechanical vibration has wide application in wearable devices and wireless sensors to complement or replace batteries. Energy harvesting efficiency can be increased by eliminating AC/DC conversion. A test chip demonstrating self-timing, power-on reset circuitry, and dynamic memory for energy harvesting AC voltages has been designed in 180 nm CMOS and tested. An energy scalable DSP architecture implements FIR filters that consume as little as 170 pJ per output sample. The on-chip DRAM retains data for up to 28 ms while register data is retained down to a supply voltage of 153 mV. Circuit operation is confirmed for supply frequencies between 60 Hz and 1 kHz with power consumption below 130 μW. Reaching the limits of miniaturization will require approaching the limits of power dissipation. We extrapolate from this DSP architecture to find the minimum volume required for mechanical vibration energy harvesting sensors.

Power management in energy harvesting sensor networks

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy efficiently. However, in some of the sensor networking applications, an additional facility is available to ameliorate the energy problem: harvesting energy from the environment. Certain considerations in using an energy harvesting source are fundamentally different from that in using a battery, because, rather than a limit on the maximum energy, it has a limit on the maximum rate at which the energy can be used. Further, the harvested energy availability typically varies with time in a nondeterministic manner. While a deterministic metric, such as residual battery, suffices to characterize the energy availability in the case of batteries, a more sophisticated characterization may be required for a harvesting source. Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity. In a distributed application, the same end-user performance may be achieved using different workload allocations, and resultant energy consumptions at multiple nodes. In this case, it is important to align the workload allocation with the energy availability at the harvesting nodes. We consider the above issues in power management for energy-harvesting sensor networks. We develop abstractions to characterize the complex time varying nature of such sources with analytically tractable models and use them to address key design issues. We also develop distributed methods to efficiently use harvested energy and test these both in simulation and experimentally on an energy-harvesting sensor network, prototyped for this work.

Real-time scheduling for energy harvesting sensor nodes

Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines 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 have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First scheduling.

Cooperative and Reliable ARQ Protocols for Energy Harvesting Wireless Sensor Nodes

One class of wireless sensor networks makes use of sensor nodes that recharge their batteries by harvesting energy from the surrounding environment. Being continuously recharged, the battery does not need to be replaced regularly and the sensor node is maintenance-free. A key module in such sensor network solutions is the data link automatic repeat request (ARQ) protocol, which must be designed to reliably deliver sensor nodes data at the minimum energy cost. With this objective in mind, two ARQ protocol classes are compared. In one class, each sensor node operates individually. In the other, the concept of cooperative communications is adopted, whereby neighboring sensor nodes help each other during the retransmission process. It is shown that the use of cooperative ARQ protocols in energy harvesting sensor networks enables sensor nodes to balance their energy consumption to match their own battery recharge rate. In turn, a balanced energy consumption-to-recharge rate ratio has the potential to improve the network throughput. Both classes of ARQ protocols are analyzed and compared. Estimated throughput gains are discussed under various network scenarios.

DSPs for Energy Harvesting Sensors: Applications and Architectures

Energy harvesting from human or environmental sources shows promise as an alternative to battery power for embedded digital electronics. Digital signal processors that harvest power from ambient mechanical vibration are particularly promising for sensor networks.

A prognostic model for predicting the disappearance of left atrial thrombi among candidates for percutaneous transvenous mitral commissurotomy

In this paper we consider a wireless IoT network with energy harvesting sensors and finite battery capacity which obtain their energy from a wireless power transmitter and communicates with a fusion center. We study a specific system model in which one of the sensors with higher battery capacity performs as a central node and obtains the main data from the environment while the other sensors observe the variations of data. Since, fusion center needs fresh data in real time applications and old useless data would be discarded, thus, the Age of Information (AoI) can be considered as one of the major parameters for such networks. In the present paper, we proposes a new protocol based on NOMA and we obtain a closed-form equation for the age of information for whole network. Then, we optimize the age of information on power schedule parameter. As well, the age of whole network is calculated for the same network under TDMA protocol and is optimized on time fraction parameter. Finally, the numerical results are obtained for several parameters and the optimized parameters are applied to compare the age under both protocols. The results show that using NOMA will improve AoI performance compared to TDMA scheme.