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Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management a...
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Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management algorithms do not exploit prior knowledge of these variations for making better decisions. This paper presents PREAcT, the first energy-management algorithm that exploits time-varying utility to optimize application performance. PREAcT'S design combines strategic long-term planning of future energy utilization with feedback control to compensate for deviations from the expected conditions. We implement Pre-act on a low-power microcontroller and compare it against the state of the art on multiple years of real-world data. Our results demonstrate that PREAcT is up to 53 % more effective in utilizing harvested solar energy and significantly more robust to uncertainties and inefficiencies of practical systems. These gains translate into an improvement of 28 % in the end-to-end performance of a real-world application we investigate when using PREAcT.
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摘要 :
Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management a...
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Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management algorithms do not exploit prior knowledge of these variations for making better decisions. This paper presents PREAcT, the first energy-management algorithm that exploits time-varying utility to optimize application performance. PREAcT'S design combines strategic long-term planning of future energy utilization with feedback control to compensate for deviations from the expected conditions. We implement Pre-act on a low-power microcontroller and compare it against the state of the art on multiple years of real-world data. Our results demonstrate that PREAcT is up to 53 % more effective in utilizing harvested solar energy and significantly more robust to uncertainties and inefficiencies of practical systems. These gains translate into an improvement of 28 % in the end-to-end performance of a real-world application we investigate when using PREAcT.
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The high power consumption of inertial activity sensors limits the battery lifetime of today’s wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements...
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The high power consumption of inertial activity sensors limits the battery lifetime of today’s wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements into electrical energy while using the harvesting signal to replace conventional activity sensors. However, in human-centric applications, the amount of harvested kinetic energy is not enough to power a real-time activity recognition algorithm and run the wearable device perpetually. In this paper, we propose Solar based human Activity Recognition (SolAR), which uses solar cells simultaneously as an activity sensor as well as an energy source. Our key observation is that the power available from a wrist-worn solar cell changes dynamically while a person moves, encoding information about the underlying activity. We collect empirical solar energy data to explore its activity sensing potential and implement the activity recognition pipeline on an ultra low-power micro-controller unit to evaluate the end-to-end power consumption of the system. Our analysis reveals that SolAR improves activity recognition accuracy by up to 8.3% and harvests more than one order of magnitude higher power compared to its kinetic counterpart. This enables SolAR to generate more energy than required for the entire activity recognition pipeline, which we term as energy positive activity recognition, achieving uninterrupted, autonomous, self-powered and real-time operation.
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The high power consumption of inertial activity sensors limits the battery lifetime of today’s wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements...
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The high power consumption of inertial activity sensors limits the battery lifetime of today’s wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements into electrical energy while using the harvesting signal to replace conventional activity sensors. However, in human-centric applications, the amount of harvested kinetic energy is not enough to power a real-time activity recognition algorithm and run the wearable device perpetually. In this paper, we propose Solar based human Activity Recognition (SolAR), which uses solar cells simultaneously as an activity sensor as well as an energy source. Our key observation is that the power available from a wrist-worn solar cell changes dynamically while a person moves, encoding information about the underlying activity. We collect empirical solar energy data to explore its activity sensing potential and implement the activity recognition pipeline on an ultra low-power micro-controller unit to evaluate the end-to-end power consumption of the system. Our analysis reveals that SolAR improves activity recognition accuracy by up to 8.3% and harvests more than one order of magnitude higher power compared to its kinetic counterpart. This enables SolAR to generate more energy than required for the entire activity recognition pipeline, which we term as energy positive activity recognition, achieving uninterrupted, autonomous, self-powered and real-time operation.
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Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinet...
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Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.
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摘要 :
Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinet...
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Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes.
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Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the envi...
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Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the environmental energy into electrical energy. Kinetic Energy Harvesting (KEH) converts the ambient motion/vibration energy into electrical energy to power the IoT sensor nodes. However, most previous works employ KEH without dynamically tracking the optimal operating point of the transducer for maximum power output. In this paper, we systematically analyze the relation between the operating point of the transducer and the corresponding energy yield. To this end, we explore the voltage-current characteristics of the KEH transducer to find its Maximum Power Point (MPP). We show how this operating point can be approximated in a practical energy harvesting circuit. We design two hardware circuit prototypes to evaluate the performance of the proposed mechanism and analyze the harvested energy using a precise load shaker under a wide set of controlled conditions typically found in human-centric applications. We analyze the dynamic current-voltage characteristics and specify the relation between the MPP sampling rate and harvesting efficiency which outlines the need for dynamic MPP tracking. The results show that the proposed energy harvesting mechanism outperforms the conventional method in terms of generated power and offers at least one order of magnitude higher power than the latter.
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摘要 :
Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the envi...
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Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the environmental energy into electrical energy. Kinetic Energy Harvesting (KEH) converts the ambient motion/vibration energy into electrical energy to power the IoT sensor nodes. However, most previous works employ KEH without dynamically tracking the optimal operating point of the transducer for maximum power output. In this paper, we systematically analyze the relation between the operating point of the transducer and the corresponding energy yield. To this end, we explore the voltage-current characteristics of the KEH transducer to find its Maximum Power Point (MPP). We show how this operating point can be approximated in a practical energy harvesting circuit. We design two hardware circuit prototypes to evaluate the performance of the proposed mechanism and analyze the harvested energy using a precise load shaker under a wide set of controlled conditions typically found in human-centric applications. We analyze the dynamic current-voltage characteristics and specify the relation between the MPP sampling rate and harvesting efficiency which outlines the need for dynamic MPP tracking. The results show that the proposed energy harvesting mechanism outperforms the conventional method in terms of generated power and offers at least one order of magnitude higher power than the latter.
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Experimentation with computer networks under realistic conditions is a necessary step in debugging, profiling and validation towards real deployments and applications. Although the definition of relevant experimentation scenarios ...
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Experimentation with computer networks under realistic conditions is a necessary step in debugging, profiling and validation towards real deployments and applications. Although the definition of relevant experimentation scenarios is usually relatively straightforward, their implementation and execution are unfortunately difficult and tedious. Generation of extensive experiment documentation assuring replicability is increasingly challenging even for experienced researchers. In this paper, we explain how a typical experimentation workflow can be supported using properly selected tools and components from the DevOps ecosystem, leading to repeatable, well-defined measurements. We start with a general approach using ad-hoc setups. Next, we show how the featured set of tools can be used with, and benefit from, existing testbeds.
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摘要 :
Experimentation with computer networks under realistic conditions is a necessary step in debugging, profiling and validation towards real deployments and applications. Although the definition of relevant experimentation scenarios ...
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Experimentation with computer networks under realistic conditions is a necessary step in debugging, profiling and validation towards real deployments and applications. Although the definition of relevant experimentation scenarios is usually relatively straightforward, their implementation and execution are unfortunately difficult and tedious. Generation of extensive experiment documentation assuring replicability is increasingly challenging even for experienced researchers. In this paper, we explain how a typical experimentation workflow can be supported using properly selected tools and components from the DevOps ecosystem, leading to repeatable, well-defined measurements. We start with a general approach using ad-hoc setups. Next, we show how the featured set of tools can be used with, and benefit from, existing testbeds.
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