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EProfiler: High-Precision Power Monitoring System for IoT Devices Featuring Extreme Dynamic Range of Operation

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Auteur
Kazdaridis G., Zografopoulos I., Sidiropoulos N., Symeonidis P., Korakis T.
Date
2020
Language
en
DOI
10.1145/3417308.3430265
Sujet
Analog to digital conversion
Electric power system measurement
Energy efficiency
Energy harvesting
Resistors
Dynamic current consumption
Energy efficient
High speed comparator
High-speed analog
Internet of Things (IOT)
Long term monitoring
Power monitoring
Wide dynamic range
Internet of things
Association for Computing Machinery, Inc
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Résumé
Modern Internet-of-Things (IoT) devices and sensor systems exhibit extreme dynamic current consumption profile, since latest microprocessors and electronics support ultra-low currents in the sleep phase, of only a few nA, while they expend several mA in the active state. Existing power meters are incapable of measuring their expenditure in order to aid the development of energy-efficient schemes. In this work we introduce the eProfiler, a novel in-situ system for measuring the power expenditure of sensing devices that illustrate a wide current range. Our meter features an autoranging shunt-resistor switch that supports ultra-fast alternations of 6ns, formed with the aid of high-speed comparators, that can successfully adapt to any sharp transition. Moreover, a high-speed Analog-to-Digital Converter (ADC) with multiple inputs is employed to simultaneously monitor the shunt-resistors, delivering a speed of 150 kSamples/s at 16 bit resolution. The proposed meter can be leveraged for the real-time, as well as the long-term monitoring of IoT devices with its cost being less than 90 euros. Furthermore, the proposed system can actuate or trace state alternations on I/O pins with a maximum delay of 2 µs, in order to provide correlation capabilities with the obtained power measurements. The eProfiler features a wide dynamic range of 1.000.000:1 while also being able to monitor currents of a few nA. The average obtained error of the meter is 0.45 %, with a maximum error of 1.6 %. © 2020 ACM.
URI
http://hdl.handle.net/11615/74720
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