MEMS-based near-zero power infrared wireless sensor node

Rajaram, Vageeswar; Qian, Zhenyun; Kang, Sungho; McGruer, N.E.; Rinaldi, Matteo

doi:10.1109/memsys.2018.8346470

Cited by 23 publications

(10 citation statements)

References 10 publications

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“…In this line, the work reported in [45] is a demonstration of an infrared wireless sensor node with near zero-power consumption, using MEMS (Microe Electro Mechanical Systems) photoswitches. Authors use the sensor to wake up a node when the signal of interest is detected.…”

Section: Zero-power Sensorsmentioning

confidence: 99%

“…In particular, the works proposed in [40], [41], [45], and [46] provides trade-off solutions to push a greater subsection of the edge computing and embedded management into the extreme devices, with a winning balance between very long-term lifetime and software application development (considering the potential of creating reduced lightweight libraries for node management in such cases). In the meantime, works as [48]- [50] provide ultra-efficient sensing and triggering capabilities in terms of energy savings for critical deployments where the power supply is a primarily concern.…”

Section: E Software Application Development In the Extreme Edgementioning

confidence: 99%

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The Extreme Edge at the Bottom of the Internet of Things: A Review

et al. 2019

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resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This general definition shows the power that cloud computing makes available to the Internet of Things. The basic idea is to collect information, usually big amounts of data, in the point of interest (this so-called the edge, where information is generated) and upload it to be properly processed in the cloud, where ideally permanent and enough processing resources are available. This model may work perfectly with some approaches where the energy resource is not a limitation or the time required for any decision does not impose a very fast reaction. However, the cloud computing paradigm applied directly to IoT presents a set of drawbacks regarding latency, bandwidth and storage [10] because of the huge amount of data that have to be uploaded and processed. Due to these limitations, more layers have been proposed during the last years, getting closer to the source of data.

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Section: Zero-power Sensorsmentioning

confidence: 99%

Section: E Software Application Development In the Extreme Edgementioning

confidence: 99%

The Extreme Edge at the Bottom of the Internet of Things: A Review

et al. 2019

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“…To operate such systems we require energy harvesting techniques, to get what has been called zero or almost zero-power sensors. Some good examples of devices in this layer are [ 11 , 12 ]. Such devices are crucial for a future realistic implementation of the IoT paradigm and will only be able to operate by interworking with edge devices described previously.…”

Section: Related Workmentioning

confidence: 99%

Edge and Fog Computing Platform for Data Fusion of Complex Heterogeneous Sensors

Mujica

Rodriguez-Zurrunero

Wilby

et al. 2018

Sensors

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The explosion of the Internet of Things has dramatically increased the data load on networks that cannot indefinitely increment their capacity to support these new services. Edge computing is a viable approach to fuse and process data on sensor platforms so that information can be created locally. However, the integration of complex heterogeneous sensors producing a great amount of diverse data opens new challenges to be faced. Rather than generating usable data straight away, complex sensors demand prior calculations to supply meaningful information. In addition, the integration of complex sensors in real applications requires a coordinated development from hardware and software teams that need a common framework to reduce development times. In this work, we present an edge and fog computing platform capable of providing seamless integration of complex sensors, with the implementation of an efficient data fusion strategy. It uses a symbiotic hardware/software design approach based on a novel messaging system running on a modular hardware platform. We have applied this platform to integrate Bluetooth vehicle identifiers and radar counters in a specific mobility use case, which exhibits an effective end-to-end integration using the proposed solution.

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“…Therefore, for providing stable power to such a large sensor network or inaccessible areas, it is essential to develop on-site generators that can transform all forms of energy available at that location into electrical energy. Recently, rapid advances in low-power very large-scale integration technology have enabled the implementation of ultra-low-power-consumption integrated circuits (IC) [6][7][8] and sensors, [9][10][11] which operate under only 10-100 µW of power consumption. These electronic devices have increased the feasibility of onboard power sources using energy-harvesting technology.…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric Energy Harvesting Design Principles for Materials and Structures: Material Figure‐of‐Merit and Self‐Resonance Tuning

et al. 2020

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Piezoelectric energy harvesters (PEHs) aim to generate sufficient power to operate targeting device from the limited ambient energy. PEH includes mechanical-to-mechanical, mechanical-to-electrical, and electrical-to-electrical energy conversions, which are related to PEH structures, materials, and circuits, respectively; these should be efficient for increasing the total power. This critical review focuses on PEH structures and materials associated with the two major energy conversions to improve PEH performance. First, the resonance tuning mechanisms for PEH structures maintaining continuous resonance, regardless of a change in the vibration frequency, are presented. Based on the manual tuning technique, the electrically-and mechanicallydriven self-resonance tuning (SRT) techniques are introduced in detail. The representative SRT harvesters are summarized in terms of tunability, power consumption, and net power. Second, the figure-of-merits of the piezoelectric materials for output power are summarized based on the operating conditions, and optimal piezoelectric materials are suggested. Piezoelectric materials with large k ij , d ij , and g ij values are suitable for most PEHs, whereas those with large k ij and Q m values should be used for on-resonance conditions, wherein the mechanical energy is directly supplied to the piezoelectric material. This comprehensive review provides insights for designing efficient structures and selection of proper piezoelectric materials for PEHs.

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MEMS-based near-zero power infrared wireless sensor node

Cited by 23 publications

References 10 publications

The Extreme Edge at the Bottom of the Internet of Things: A Review

The Extreme Edge at the Bottom of the Internet of Things: A Review

Edge and Fog Computing Platform for Data Fusion of Complex Heterogeneous Sensors

Piezoelectric Energy Harvesting Design Principles for Materials and Structures: Material Figure‐of‐Merit and Self‐Resonance Tuning

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