Hibernus++: a self-calibrating and adaptive system for transiently-powered embedded devices

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 work 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 stateof- the-art approaches

From transient computing to transient systems: overcoming application challenges in energy harvesting sensor systems

Energy harvesting is a potential solution to power sensor systems, avoiding periodic battery replacements. Nevertheless, these sources usually incorporate supercapacitors to cope with energy intermittency caused by temporal variation in environmental conditions. Therefore, they do not solve the problem of dealing with the size and cost of energy storage. A relatively new concept termed transient computing, aims to remove big storage and enable systems to operate safely when powered from highly-variable energy harvesting sources. However, certain elementary functions of typical sensor systems, such as working with external peripherals or keeping track of time, represent important challenges in systems that operate transiently. This thesis highlights the challenges that are fundamental to enable transiently-powered system applications and the proposed approaches to address them. This involves a quantitative evaluation of four state-of-the-art approaches to transient computing. The comparison was performed in a system powered by synthesized signals of different harvester sources. Their performances were used to identify the scenarios where one approach outperforms others, and thus, aid designers to choose the most suitable. In order to retain the peripheral state in transiently-powered sensor systems, a generic middleware was proposed. This approach allows an application to keep the coherency between the processing unit and the state of external peripherals from one power cycle to another. The proposed middleware was tested in a transient sensor system with multiple peripherals and was able to successfully operate with I2C and SPI protocols, causing a time overhead of only 0.82% during the complete execution of the sensing application. A novel framework to design transient systems is also presented, including a strategy for keeping track of time. The viability of the framework was proven by designing and implementing a step counter. The experimental validation demonstrated that the step counter is able to calculate step rate, metabolic equivalent and activity time, as well as encrypt and wirelessly transmit data, reducing the required capacitance by up to 60%.<br/

Using sleep states to maximize the active time of transient computing systems

Energy harvesters are widely used to power wireless sensor systems, but the produced power is generally low, and can vary abruptly due to changes in the environment or the device's location. Energy buffers (batteries or supercapacitors) are normally incorporated into systems to smooth out these variations. However, they have a limited lifetime and increase system size and cost. Transient computing aims to address these issues by removing the energy buffer, and powering the system directly from the energy harvester. Approaches such as Hibernus++ deal with the resultant power intermittency by 'hibernating', i.e. saving a snapshot of the system state to non-volatile memory before a power failure, and restoring it after the power recovers. The overheads of this can be particularly costly with a low-current harvester, as the system may wake up and hibernate at a high frequency, doing little useful work in each power cycle.This paper proposes an enhancement to these approaches, providing an efficient method to avoid repeated hibernation. The introduction of a 'sleep' state, which is entered when the power supply is detected to be failing, allows the system's supply voltage to recover without taking a snapshot. Thus, the application can spend more time on useful work rather than checkpointing. If the supply voltage continues to decline, a snapshot will then be taken. The approach has been simulated and experimentally validated, with results demonstrating that the proposed scheme provides up to a 65% improvement in system active run-time with low-current harvesters vs. conventional Hibernus++

A generic middleware for external peripheral state retention in transiently-powered sensor systems

Sensor systems powered by energy harvesting usually include batteries or supercapacitors which impact the system cost and size, need time to be charged and are not environmentally friendly. In recent years, designers have proposed a new concept called transient computing that aims to remove these energy storage units and retain the system’s state between power outages, in order to cope with an unreliable energy source. However, existing approaches cannot retain the state of external peripherals or are specific to certain peripherals, i.e. they are not generic. This poster proposes a generic middleware, capable to retain the state of external peripherals that are connected to a microcontroller through SPI. The validation shows the proposed approach retains the peripheral configuration between power failures with a maximum time overhead of 15% when configuring the peripheral. However, this represents a 0.77% overhead for a complete example application, which is lower than that caused by existing approaches

RESTOP: retaining external peripheral state in intermittently-powered sensor systems

Energy harvesting sensor systems typically incorporate energy buffers (e.g., rechargeable batteries and supercapacitors) to accommodate fluctuations in supply. However, the presence of these elements limits the miniaturization of devices. In recent years, researchers have proposed a new paradigm, transient computing, where systems operate directly from the energy harvesting source and allow computation to span across power cycles, without adding energy buffers. Various transient computing approaches have addressed the challenge of power intermittency by retaining the processor’s state using non-volatile memory. However, no generic approach has yet been proposed to retain the state of peripherals external to the processing element. This paper proposes RESTOP, flexible middleware which retains the state of multiple external peripherals that are connected to a computing element (i.e., a microcontroller) through protocols such as SPI or I2C. RESTOP acts as an interface between the main application and the peripheral, which keeps a record, at run-time, of the transmitted data in order to restore peripheral configuration after a power interruption. RESTOP is practically implemented and validated using three digitally interfaced peripherals, successfully restoring their configuration after power interruptions, imposing a maximum time overhead of 15% when configuring a peripheral. However, this represents an overhead of only 0.82% during complete execution of our typical sensing application, which is substantially lower than existing approaches

Dataset supporting: RESTOP: Retaining External Peripheral State in Intermittently-Powered Sensor Systems

Dataset of the processed information to validate the middleware for the paper titled: RESTOP: Retaining External Peripheral State in Intermittently-Powered Sensor Systems accepted for publication in MDPI Sensors &quot;Low Power Embedded Sensing: Hardware-Software Design and Applications&quot; Journal.</span

Dataset supporting: A Generic Middleware for External Peripheral State Retention in Transiently-Powered Sensor Systems

Dataset of the processed information to validate the middleware for the paper titled: A Generic Middleware for External Peripheral State Retention in Transiently-Powered Sensor Systems accepted for publication in ENSsys 2017.</span

Dataset supporting the Paper titled: Intermittently-Powered Energy Harvesting Step Counter for Fitness Tracking

This Dataset supports the Paper titled Intermittently-Powered Energy Harvesting Step Counter for Fitness Tracking, accepted for publication in IEEE Sensors Applications Symposium (SAS) 2017. Funded by Mexican CONACYT.</span