Hybrid Energy Harvester for Medical Sensor Node toward Real-Time Healthcare Monitoring

In healthcare applications, the remote monitoring for moving patients depends on medical sensor nodes, which should be mobile. Thus, the power mains of medical sensor nodes should be disconnected most of the time to monitor natural movements of patients. In this paper, a self-sustainable medical sensor node is proposed for healthcare monitoring applications. The node implementation consists of a microcontroller unit (MCU), a photo-plethysmography (PPG) sensor, a Bluetooth low energy (BLE) module, and a MPU module that includes a gyroscope with accelerometer. The power supply of the node is a hybrid energy harvester developed to provide a sustainable energy for the sensor node. The harvester is composed of a photovoltaic (PV) panel, a thermoelectric generator (TEG) module, a DC-DC converter, and a super-capacitor. Experimental results illustrate that the proposed node can monitor a physiological data on a mobile device using the BLE Terminal application

An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System

In this paper, an autonomous wearable sensor node is developed for long-term continuous healthcare monitoring. This node is used to monitor the body temperature and heart rate of a human through a mobile application. Thus, it includes a temperature sensor, a heart pulse sensor, a low-power microcontroller, and a Bluetooth low energy (BLE) module. The power supply of the node is a lithium-ion rechargeable battery, but this battery has a limited lifetime. Therefore, a photovoltaic (PV) energy harvesting system is proposed to prolong the battery lifetime of the sensor node. The PV energy harvesting system consists of a flexible photovoltaic panel, and a charging controller. This PV energy harvesting system is practically tested outdoor under lighting intensity of 1000 W/m2. Experimentally, the overall power consumption of the node is 4.97 mW and its lifetime about 246 hours in active-sleep mode. Finally, the experimental results demonstrate long-term and sustainable operation for the wearable sensor node

An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System

oai:ojs.ijet.ise.pw.edu.pl:article/2503In this paper, an autonomous wearable sensor node is developed for long-term continuous healthcare monitoring. This node is used to monitor the body temperature and heart rate of a human through a mobile application. Thus, it includes a temperature sensor, a heart pulse sensor, a low-power microcontroller, and a Bluetooth low energy (BLE) module. The power supply of the node is a lithium-ion rechargeable battery, but this battery has a limited lifetime. Therefore, a photovoltaic (PV) energy harvesting system is proposed to prolong the battery lifetime of the sensor node. The PV energy harvesting system consists of a flexible photovoltaic panel, and a charging controller. This PV energy harvesting system is practically tested outdoor under lighting intensity of 1000 W/m2. Experimentally, the overall power consumption of the node is 4.97 mW and its lifetime about 246 hours in active-sleep mode. Finally, the experimental results demonstrate long-term and sustainable operation for the wearable sensor node

Internet of Harvester Nano Things: A Future Prospects

The advancements in nanotechnology, material science, and electrical engineering have shrunk the sizes of electronic devices down to the micro/nanoscale. This brings the opportunity of developing the Internet of Nano Things (IoNT), an extension of the Internet of Things (IoT). With nanodevices, numerous new possibilities emerge in the biomedical, military fields, and industrial products. However, a continuous energy supply is needed for these devices to work. At the micro/nanoscale, batteries cannot supply this demand due to size limitations and the limited energy contained in the batteries. Internet of Harvester Nano Things (IoHNT), a concept of Energy Harvesting (EH), which converts the existing different energy sources, which otherwise would be dissipated to waste, into electrical energy via electrical generators. Sources for EH are abundant, from sunlight, sound, water, and airflow to living organisms. IoHNT methods are significant assets to ensure the proper operation of the IoNT; thus, in this review, we comprehensively investigate the most useful energy sources and IoHNT principles to power the nano/micro-scaled electronic devices with the scope of IoNT. We discuss the IoHNT principles, material selections, challenges, and state-of-the-art applications of each energy source for both in-vivo and in vitro applications. Finally, we present the latest challenges of EH along with future research directions to solve the problems regarding constructing continuous IoNT containing various self-powered nanodevices. Therefore, IoHNT represents a significant shift in nanodevice power supply, leading us towards a future where wireless technology is widespread. Hence, it will motivate researchers to envision and contribute to the advancement of the following power revolution in IoNT, providing unmatched simplicity and efficiency

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

Ultra-Low Power Transmitter and Power Management for Internet-of-Things Devices

Two of the most critical components in an Internet-of-Things (IoT) sensing and transmitting node are the power management unit (PMU) and the wireless transmitter (Tx). The desire for longer intervals between battery replacements or a completely self-contained, battery-less operation via energy harvesting transducers and circuits in IoT nodes demands highly efficient integrated circuits. This dissertation addresses the challenge of designing and implementing power management and Tx circuits with ultra-low power consumption to enable such efficient operation. The first part of the dissertation focuses on the study and design of power management circuits for IoT nodes. This opening portion elaborates on two different areas of the power management field: Firstly, a low-complexity, SPICE-based model for general low dropout (LDO) regulators is demonstrated. The model aims to reduce the stress and computation times in the final stages of simulation and verification of Systems-on-Chip (SoC), including IoT nodes, that employ large numbers of LDOs. Secondly, the implementation of an efficient PMU for an energy harvesting system based on a thermoelectric generator transducer is discussed. The PMU includes a first-in-its-class LDO with programmable supply noise rejection for localized improvement in the suppression. The second part of the dissertation addresses the challenge of designing an ultra- low power wireless FSK Tx in the 900 MHz ISM band. To reduce the power consumption and boost the Tx energy efficiency, a novel delay cell exploiting current reuse is used in a ring-oscillator employed as the local oscillator generator scheme. In combination with an edge-combiner PA, the Tx showed a measured energy efficiency of 0.2 nJ/bit and a normalized energy efficiency of 3.1 nJ/(bit∙mW) when operating at output power levels up to -10 dBm and data rates of 3 Mbps. To close this dissertation, the implementation of a supply-noise tolerant BiCMOS ring-oscillator is discussed. The combination of a passive, high-pass feedforward path from the supply to critical nodes in the selected delay cell and a low cost LDO allow the oscillator to exhibit power supply noise rejection levels better than –33 dB in experimental results

Efficient power management circuits for energy harvesting applications

Low power IoT devices are growing in numbers and by 2020 there will be more than 25 Billion of those in areas such as wearables, smart homes, remote surveillance, transportation and industrial systems, including many others. Many IoT electronics either will operate from stand-alone energy supply (e.g., battery) or be self-powered by harvesting from ambient energy sources or have both options. Harvesting sustainable energy from ambient environment plays significant role in extending the operation lifetime of these devices and hence, lower the maintenance cost of the system, which in turn help make them integral to simpler systems. Both for battery-powered and harvesting capable systems, efficient power delivery unit remains an essential component for maximizing energy efficiency. The goal of this research is to investigate the challenges of energy delivery for low power electronics considering both energy harvesting as well as battery-powered conditions and to address those challenges. Different challenges of energy harvesting from low voltage energy sources based on the limitations of the sources, the type of the regulator used and the pattern of the load demands have been investigated. Different aspects of the each challenges are further investigated to seek optimized solutions for both load specific and generalized applications. A voltage boost mechanism is chosen as the primary mechanism to investigate and to addressing those challenges, befitting the need for low power applications which often rely on battery voltage or on low voltage energy harvesting sources. Additionally, a multiple output buck regulator is also discussed. The challenges analyzed include very low voltage start up issues for an inductive boost regulator, cascading of boost regulator stages, and reduction of the number of external component through reusing those. Design techniques for very high conversion ratio, bias current reduction with autonomous bias gating, battery-less cold start, component and power stage multiplexing for reconfigurable and multi-domain regulators are presented. Measurement results from several silicon prototypes are also presented.Ph.D

Leveraging Backscatter for Ultra-low Power Wireless Sensing Systems

The past few years have seen a dramatic growth in wireless sensing systems, with millions of wirelessly connected sensors becoming first-class citizens of the Internet. The number of wireless sensing devices is expected to surpass 6.75 billion by 2017, more than the world\u27s population as well as the combined market of smartphones, tablets, and PCs. However, its growth faces two pressing challenges: battery energy density and wireless radio power consumption. Battery energy density looms as a fundamental limiting factor due to slow improvements over the past several decades (3x over 22 years). Wireless radio power consumption is another key challenge because high-speed wireless communication is often far more expensive energy-wise than computation, storage and sensing. To make matters worse, wireless sensing devices are generating an increasing amount of data. These challenges raise a fundamental question --- how should we power and communicate with wireless sensing devices. More specifically, instead of using batteries, can we leverage other energy sources to reduce, if not eliminate, the dependence on batteries? Similarly, instead of optimizing existing wireless radios, can we fundamentally change how radios transmit wireless signals to achieve lower power consumption? A promising technique to address these questions is backscatter --- a primitive that enables RF energy harvesting and ultra-low-power wireless communication. Backscatter has the potential to reduce dependence on batteries because it can obtain energy by rectifying the wireless signals transmitted by a backscatter reader. Backscatter can also work by reflecting existing wireless signals (WiFi, BLE) when these are available nearby. Because signal reflection only consumes uWs of power, backscatter can enable ultra-low-power wireless communication. However, the use of backscatter for communicating with wireless sensing devices presents several challenges. First, decreasing RF power across distance limits the operational range of micro-powered backscatter devices. This raises the question of how to maintain a communication link with a backscatter device despite tiny amount of harvested power. Second, even though the backscatter RF front-end is extremely power-efficient, the computational and sensing overhead on backscatter sensors limit its ability to operate with a few micro-Watts of power. Such overhead is a negligible factor of overall power consumption for platforms where radio power consumption is high (e.g. WiFi or Bluetooth based devices). However, it becomes the bottleneck for backscatter based platforms. Third, backscatter readers are not currently deployed in existing indoor environments to provide a continuous carrier for carrying backscattered information. As a result, backscatter deployment is not yet widespread. This thesis addresses these challenges by making the following contributions. First, we design a network stack that enables continuous operation despite decreasing harvested power across distance by employing an OS abstraction --- task fragmentation. We show that such a network stack enables packet transfer even when the whole system is powered by a 3cmx3cm solar panel under natural indoor light condition. Second, we design a hardware architecture that minimizes the computational overhead of backscatter to enable over 1Mbps backscatter transmission while consuming less than 100uWs of power, a two order of magnitude improvement over the state-of-the-art. Finally, we design a system that can leverage both ambient WiFi and BLE signals for backscatter. Our empirical evaluation shows that we can backscatter 500bps data on top of a WiFi stream and 50kbps data on top of a Bluetooth stream when the backscatter device is 3m away from the commercial WiFi and Bluetooth receivers

Análise e Implementação de Conversores DC-DC para uma Célula Fotovoltaica Orgânica

Acompanhando o desenvolvimento da sociedade moderna e da Internet of Things (IoT), observa-se que uma pessoa possui, em média, quatro dispositivos eletrónicos como smartphones, tablets ou gadgets [1]. Também novas tecnologias associadas às fontes de energia renováveis têm vindo a ser estudas. Como tal, verifica-se a necessidade de recolher e armazenar essa energia. Para tal, é apresentada nesta dissertação a análise e implementação de uma unidade de gestão de potência aplicada a um sistema de recolha de energia baseado em células fotovoltaicas orgânicas. Relativamente à implementação da PMU, esta foi realizada na tecnologia de circuito integrado CMOS de 0,13 μm e optimizada de quatro formas distintas. Das quatro soluções propostas, duas foram completamente integradas, enquanto que as restantes careceram da introdução componentes discretos. No processo de optimização da unidade de gestão de potência foram considerados dois graus de liberdade, a frequência de comutação do conversor DC-DC e a tensão de saída disponibilizada pela PMU. Relativamente à frequência de operação da arquitetura, foram consideradas as frequências de 100 kHz, 100 MHz e 500 MHz para uma gama de tensões de entrada que varia de 100 mV a 900 mV. Quanto à tensão de saída da unidade, esta é do tipo standard (1,2 V ou 2,4 V). Adicionalmente, como base comparativa, efectuou-se uma análise experimental à PMU baseada em condensadores comutados apresentada em [32], aplicada às células fotovoltaicas orgânicas. Esta dissertação faz ainda parte do projecto de investigação científica μFlexBat. Este projecto tem por objectivo estudar e implementar uma unidade de gestão de potência para IoT capaz de converter e armazenar a energia proveniente de uma célula fotovoltaica orgânica. Este concerne-se ao estudo de uma PMU flexível do ponto de vista mecânico, com baixos custos de implementação, autossuficiente e capaz de gerar uma tensão standard à sua saída cuja funcionalidade será alimentar um dispositivo eletrónico de muito baixa potência, como um sensor de monitoração de parâmetros biomédicos. Do ponto de vista dos resultados obtidos, nas optimizações a 100 kHz, a PMU revelou-se autossuficiente e com um rendimento de 65,66% na arquitetura a 1,2 V, e de 84,22% na PMU a 2,4 V. Relativamente às soluções de 100 MHz e 500 MHz, o rendimento apresentado por estas é de 20,21%, para a proposta de 100 MHz, e de 26,73% para o sistema a operar a 500 MHz. Quanto à PMU baseada em condensadores comutados proposta em [32], obteve-se, considerando as células fotovoltaicas orgânicas como fontes de energia, um rendimento máximo de 75,76%

Runtime Hardware Reconfiguration in Wireless Sensor Networks for Condition Monitoring

The integration of miniaturized heterogeneous electronic components has enabled the deployment of tiny sensing platforms empowered by wireless connectivity known as wireless sensor networks. Thanks to an optimized duty-cycled activity, the energy consumption of these battery-powered devices can be reduced to a level where several years of operation is possible. However, the processing capability of currently available wireless sensor nodes does not scale well with the observation of phenomena requiring a high sampling resolution. The large amount of data generated by the sensors cannot be handled efficiently by low-power wireless communication protocols without a preliminary filtering of the information relevant for the application. For this purpose, energy-efficient, flexible, fast and accurate processing units are required to extract important features from the sensor data and relieve the operating system from computationally demanding tasks. Reconfigurable hardware is identified as a suitable technology to fulfill these requirements, balancing implementation flexibility with performance and energy-efficiency. While both static and dynamic power consumption of field programmable gate arrays has often been pointed out as prohibitive for very-low-power applications, recent programmable logic chips based on non-volatile memory appear as a potential solution overcoming this constraint. This thesis first verifies this assumption with the help of a modular sensor node built around a field programmable gate array based on Flash technology. Short and autonomous duty-cycled operation combined with hardware acceleration efficiently drop the energy consumption of the device in the considered context. However, Flash-based devices suffer from restrictions such as long configuration times and limited resources, which reduce their suitability for complex processing tasks. A template of a dynamically reconfigurable architecture built around coarse-grained reconfigurable function units is proposed in a second part of this work to overcome these issues. The module is conceived as an overlay of the sensor node FPGA increasing the implementation flexibility and introducing a standardized programming model. Mechanisms for virtual reconfiguration tailored for resource-constrained systems are introduced to minimize the overhead induced by this genericity. The definition of this template architecture leaves room for design space exploration and application- specific customization. Nevertheless, this aspect must be supported by appropriate design tools which facilitate and automate the generation of low-level design files. For this purpose, a software tool is introduced to graphically configure the architecture and operation of the hardware accelerator. A middleware service is further integrated into the wireless sensor network operating system to bridge the gap between the hardware and the design tools, enabling remote reprogramming and scheduling of the hardware functionality at runtime. At last, this hardware and software toolchain is applied to real-world wireless sensor network deployments in the domain of condition monitoring. This category of applications often require the complex analysis of signals in the considered range of sampling frequencies such as vibrations or electrical currents, making the proposed system ideally suited for the implementation. The flexibility of the approach is demonstrated by taking examples with heterogeneous algorithmic specifications. Different data processing tasks executed by the sensor node hardware accelerator are modified at runtime according to application requests