Energy-aware Approaches for Energy Harvesting Powered Wireless Sensor Systems

Energy harvesting (EH) powered wireless sensor systems (WSSs) are gaining increasing popularity since they enable the system to be self-powering, long-lasting, almost maintenance-free, and environmentally friendly. However, the mismatch between energy generated by harvesters and energy demanded by WSS to perform the required tasks is always a bottleneck as the ambient environmental energy is limited, and the WSS is power hunger. Therefore, the thesis has proposed, designed, implemented, and tested the energy-aware approaches for wireless sensor motes (WSMs) and wireless sensor networks (WSNs), including hardware energy-aware interface (EAI), software EAI, sensing EAI and network energy-aware approaches to address this mismatch. The main contributions of this thesis to the research community are designing the energy-aware approaches for EH Powered WSMs and WSNs which enables a >30 times reduction in sleep power consumption of WSNs for successful EH powering WSNs without a start-up issue in the condition of mismatch between the energy generated by harvesters and energy demanded by WSSs in both mote and network systems. For EH powered WSM systems, the energy-aware approaches have (1) enabled the harvested energy to be accumulated in energy storage devices to deal with the mismatch for the operation of the WSMs without the start-up issue, (2) enabled a commercial available WSMs with a reduced sleep current from 28.3 μA to 0.95 μA for the developed WSM, (3) thus enabled the WSM operations for a long active time of about 1.15 s in every 7.79 s to sample and transmit a large number of data (e.g., 388 bytes), rather than a few ten milliseconds and a few bytes. For EH powered WSN systems, on top of energy-aware approached for EH powered WSM, the network energy-aware approaches have presented additional capabilities for network joining process for energy-saving and enabled EH powered WSNs. Once the EH powered WSM with the network energy-aware approach is powered up and began the network joining process, energy, as an example of 48.23 mJ for a tested case, has been saved in the case of the attempt to join the network unsuccessfully. Once the EH-WSM has joined the network successfully, the smart programme applications that incorporate the software EAI, sensing EAI and hardware EAI allow the EH powered WSM to achieve (4) asynchronous operation or (5) synchronised operation based on the energy available after the WSM has joined the network.Through designs, implementations, and analyses, it has been shown that the developed energy-aware approaches have provided an enabled capability for EH successfully powering WSS technologies in the condition of energy mismatch, and it has the potential to be used for wide industrial applications

Power Management ICs for Internet of Things, Energy Harvesting and Biomedical Devices

This dissertation focuses on the power management unit (PMU) and integrated circuits (ICs) for the internet of things (IoT), energy harvesting and biomedical devices. Three monolithic power harvesting methods are studied for different challenges of smart nodes of IoT networks. Firstly, we propose that an impedance tuning approach is implemented with a capacitor value modulation to eliminate the quiescent power consumption. Secondly, we develop a hill-climbing MPPT mechanism that reuses and processes the information of the hysteresis controller in the time-domain and is free of power hungry analog circuits. Furthermore, the typical power-performance tradeoff of the hysteresis controller is solved by a self-triggered one-shot mechanism. Thus, the output regulation achieves high-performance and yet low-power operations as low as 12 µW. Thirdly, we introduce a reconfigurable charge pump to provide the hybrid conversion ratios (CRs) as 1⅓× up to 8× for minimizing the charge redistribution loss. The reconfigurable feature also dynamically tunes to maximum power point tracking (MPPT) with the frequency modulation, resulting in a two-dimensional MPPT. Therefore, the voltage conversion efficiency (VCE) and the power conversion efficiency (PCE) are enhanced and flattened across a wide harvesting range as 0.45 to 3 V. In a conclusion, we successfully develop an energy harvesting method for the IoT smart nodes with lower cost, smaller size, higher conversion efficiency, and better applicability. For the biomedical devices, this dissertation presents a novel cost-effective automatic resonance tracking method with maximum power transfer (MPT) for piezoelectric transducers (PT). The proposed tracking method is based on a band-pass filter (BPF) oscillator, exploiting the PT’s intrinsic resonance point through a sensing bridge. It guarantees automatic resonance tracking and maximum electrical power converted into mechanical motion regardless of process variations and environmental interferences. Thus, the proposed BPF oscillator-based scheme was designed for an ultrasonic vessel sealing and dissecting (UVSD) system. The sealing and dissecting functions were verified experimentally in chicken tissue and glycerin. Furthermore, a combined sensing scheme circuit allows multiple surgical tissue debulking, vessel sealer and dissector (VSD) technologies to operate from the same sensing scheme board. Its advantage is that a single driver controller could be used for both systems simplifying the complexity and design cost. In a conclusion, we successfully develop an ultrasonic scalpel to replace the other electrosurgical counterparts and the conventional scalpels with lower cost and better functionality

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

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

Energy harvesting and wireless transfer in sensor network applications: Concepts and experiences

Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite, yet intermittent, energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about

SUSTAINABLE ENERGY HARVESTING TECHNOLOGIES – PAST, PRESENT AND FUTURE

Chapter 8: Energy Harvesting Technologies: Thick-Film Piezoelectric Microgenerato

On-Chip Solar Energy Harvester and PMU With Cold Start-Up and Regulated Output Voltage for Biomedical Applications

This paper presents experimental results from a system that comprises a fully autonomous energy harvester with a solar cell of 1 mm 2 as energy transducer and a Power Management Unit (PMU) on the same silicon substrate, and an output voltage regulator. Both chips are implemented in standard 0.18 μm CMOS technology with total layout areas of 1.575 mm 2 and 0.0126 mm 2 , respectively. The system also contains an off-the-shelf 3.2 mm × 2.5 mm × 0.9 mm supercapacitor working as an off-chip battery or energy reservoir between the PMU and the voltage regulator. Experimental results show that the fast energy recovery of the on-chip solar cell and PMU permits the system to replenish the supercapacitor with enough charge as to sustain Bluetooth Low Energy (BLE) communications even with input light powers of 510 nW. The whole system is able to self-start-up without external mechanisms at 340 nW. This work is the first step towards a self-supplied sensor node with processing and communication capabilities. The small form factor and ultra-low power consumption of the system components is in compliance with biomedical applications requirementsThis work was supported in part by the Spanish Government (Ministerio de Ciencia, Innovación y Universidades) under Project RTI2018-097088-B-C32 and Project RTI2018-095994-B-I00 (MICINN/FEDER), in part by the Xunta de Galicia, in part by the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08 and reference competitive group 2017-2020, ED431C 2017/69) and European Regional Development Fund (ERDF), and in part by the Junta de Extremadura and the ERDF, under Grant IB 18079S

Inductively Coupled CMOS Power Receiver For Embedded Microsensors

Inductively coupled power transfer can extend the lifetime of embedded microsensors that save costs, energy, and lives. To expand the microsensors' functionality, the transferred power needs to be maximized. Plus, the power receiver needs to handle wide coupling variations in real applications. Therefore, the objective of this research is to design a power receiver that outputs the highest power for the widest coupling range. This research proposes a switched resonant half-bridge power stage that adjusts both energy transfer frequency and duration so the output power is maximally high. A maximum power point (MPP) theory is also developed to predict the optimal settings of the power stage with 98.6% accuracy. Finally, this research addresses the system integration challenges such as synchronization and over-voltage protection. The fabricated self-synchronized prototype outputs up to 89% of the available power across 0.067%~7.9% coupling range. The output power (in percentage of available power) and coupling range are 1.3× and 13× higher than the comparable state of the arts.Ph.D

Optimizing Embedded Software of Self-Powered IoT Edge Devices for Transient Computing

IoT edge computing becomes increasingly popular as it can mitigate the burden of cloud servers significantly by offloading tasks from the cloud to the edge which contains the majority of IoT devices. Currently, there are trillions of edge devices all over the world, and this number keeps increasing. A vast amount of edge devices work under power-constrained scenarios such as for outdoor environmental monitoring. Considering the cost and sustainability, in the long run, self-powering through energy harvesting technology is preferred for these IoT edge devices. Nevertheless, a common and critical drawback of self-powered IoT edge devices is that their runtime states in volatile memory such as SRAM will be lost during the power outage. Thanks to the state-of-the-art non-volatile processor (NVP), the runtime volatile states can be saved into the on-chip non-volatile memory before the power outage and recovered when harvesting power becomes available. Yet the potential of a self-powered IoT edge device is still hindered by the intrinsic low energy efficiency and reliability. In order to fully exert the potentials of existing self-powered IoT edge devices, this dissertation aims at optimizing the energy efficiency and reliability of self-powered IoT edge devices through several software approaches. First, to prevent execution progress loss during the power outage, NVP-aware task schedulers are proposed to maximize the overall task execution progress especially for the atomic tasks of which the unfinished progress is subjected to loss regardless of having been checkpointed. Second, to minimize both the time and energy overheads of checkpointing operations on non-volatile memory, an intelligent checkpointing scheme is proposed which can not only ensure a successful checkpointing but also predict the necessity of conducting checkpointing to avoid excessive checkpointing overhead. Third, to avoid inappropriate runtime CPU clock frequency with low energy utility, a CPU frequency modulator is proposed which adjusts the runtime CPU clock frequency adaptively. Finally, to thrive in ultra-low harvesting power scenarios, a light-weight software paradigm is proposed to help maximize the energy extraction rate of the energy harvester and power regulator bundle. Besides, checkpointing is also optimized for more energy-efficient and light-weight operation