Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential t...
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ISBN:
(纸本)9781665439299
Vibration monitoring uses data gathered from accelerometers to study kinetic phenomena in applications such as: structural health monitoring and predictive maintenance. The Internet of Things (IoT) has the potential to greatly expand the range and scope of vibration monitoring applications by delivering long-life wireless sensors that can be cost-effectively embedded in hard to reach places such as;within machines, infrastructure or the built environment. However, achieving this vision is difficult due to the stringent resource constraints of contemporary IoT devices and networks. This has led the research community to develop a creative range of application-specific near-sensorprocessing firmware. However, systematic support for generic vibration monitoring on resource-poor IoT networks remains an open problem. We tackle this challenge by introducing ReFrAEN, a software framework that efficiently enables a wide range of vibration monitoring applications on IoT networks. ReFrAEN achieves this through a deeply configurable combination of compression techniques and data processing algorithms. These features allow end-users to effectively trade-off between resource consumption and data resolution in order to meet battery life constraints while preserving sufficient data quality to support the target application. Our evaluation shows that ReFrAEN is capable of identifying bearing faults, while dramatically improving battery lifetime and reducing latency in comparison to prior approaches.
Non-Intrusive Load Monitoring (NILM) is the disaggregation of the power consumption of individual appliances from agglomerated measurements taken from a single point of measure. The paper proposes a NILM methodology b...
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ISBN:
(纸本)9788887237450
Non-Intrusive Load Monitoring (NILM) is the disaggregation of the power consumption of individual appliances from agglomerated measurements taken from a single point of measure. The paper proposes a NILM methodology based on load signature analysis, and it then suggests that to deploy NILM in the large, a set of interoperable tools should support each stakeholder of the NILM value chain. This tool-chain is made interoperable by a shared domain model based on well-known ontologies, such as Saref and Schema. The paper shows that this approach enables smooth NILM-based industrial innovation, because the toolchain may be easily extended to provide capabilities such as predictive appliance maintenance, appliance aging understanding, fault detection and interaction with the utility companies. The paper proposes a NILM tool-chain development road-map based on an interoperability platform named Arrowhead to increase the value proposition of the NILM device.
Current ultra-low power smart sensing edge devices, operating for years on small batteries, are limited to low-bandwidth sensors, such as temperature or pressure. Enabling the next generation of edge devices to proces...
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ISBN:
(纸本)9781538674796
Current ultra-low power smart sensing edge devices, operating for years on small batteries, are limited to low-bandwidth sensors, such as temperature or pressure. Enabling the next generation of edge devices to process data from richer sensors such as image, video, audio, or multi-axial motion/vibration has huge application potential. However, edge processing of data-rich sensors poses the extreme challenge of squeezing the computational requirements of advanced, machine-learning-based near-sensor data analysis algorithms (such as Convolutional Neural Networks) within the mW-range power envelope of always-ON battery-powered IoT end-nodes. To address this challenge, we propose GAP-8: a multi-GOPS fully programmable RISC-V IoT-edge computing engine, featuring a 8-core cluster with CNN accelerator, coupled with an ultra-low power MCU with 30 mu W state-retentive sleep power. GAP-8 delivers up to 10 GMAC/s for CNN inference (90 MHz, 1.0V) at the energy efficiency of 600 GMAC/s/W within a worst-case power envelope of 75 mW.
We report on the design of the physical layer of a high-speed serial interface for chip-to-chip communication, targeting low cost and ultra-low power (mW) IoT end-nodes. Two differential lanes (one pair per direction)...
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ISBN:
(纸本)9781538648810
We report on the design of the physical layer of a high-speed serial interface for chip-to-chip communication, targeting low cost and ultra-low power (mW) IoT end-nodes. Two differential lanes (one pair per direction) are used to transmit/receive NRZ symbols at 1Gpbs with embedded clock. The energy-per-bit is lower than 1pJ/bit, thanks to a careful selection of termination impedance and voltage swing, tuned for moderate speed and short distance (2cm). The transceiver is designed to tolerate significant clock jitter, so that it can work with a half-rate clock shared with the rest of the chip, thereby minimizing area and power of supporting circuitry.
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