Perpetual Sensing for the Built Environment

Campbell, Bradford; Clark, Meghan; DeBruin, Samuel; Ghena, Branden; Jackson, Neal; Kuo, Ye-Sheng; Dutta, Prabal

doi:10.1109/mprv.2016.66

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…More importantly, its computing subsystem is designed around an ARM Cortex-M4 core, which does not support byte-addressable non-volatile memory, making this system unsuitable for long-lifetime, energyharvesting applications. Other bateryless systems [10,12,26,28,37,64,80,81] are designed to collect low-datarate time series data and do not support image collection. Additionally, by relying on backscater, Bluetooth Low Energy (BLE), or other short range communciation media, these devices do not communicate over long distances, making them inapplicable to Camaroptera.…”

Section: Bateryless Sensing and Communicationmentioning

confidence: 99%

Camaroptera: A Long-range Image Sensor with Local Inference for Remote Sensing Applications

Desai

Nardello

Brunelli

et al. 2022

ACM Trans. Embed. Comput. Syst.

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Batteryless image sensors present an opportunity for long-life, long-range sensor deployments that require zero maintenance and have low cost. Such deployments are critical for enabling remote sensing applications, e.g. instrumenting national highways, where individual devices are deployed far (kms away) from supporting infrastructure. In this work, we develop and characterize Camaroptera, the first batteryless image-sensing platform to combine energy-harvesting with active, long-range (LoRa) communication. We also equip Camaroptera with a Machine Learning-based processing pipeline to mitigate costly, long-distance communication of image data. This processing pipeline filters out uninteresting images, and only transmits the images interesting to the application. We show that compared to running a traditional Sense-and-Send workload, Camaroptera’s Local Inference pipeline captures and sends upto 12 × more images of interest to an application. By performing Local Inference , Camaroptera also sends upto 6.5 × fewer uninteresting images, instead using that energy to capture upto 14.7 × more new images, increasing its sensing effectiveness and availability. We fully prototype the Camaroptera hardware platform in a compact, 2cm x 3cm x 5cm volume. Our evaluation demonstrates the viability of a batteryless, remote, visual-sensing platform in a small package that collects and usefully processes acquired data and transmits it over long distances (kms), while being deployed for multiple decades with zero maintenance.

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Section: Bateryless Sensing and Communicationmentioning

confidence: 99%

Camaroptera: A Long-range Image Sensor with Local Inference for Remote Sensing Applications

Desai

Nardello

Brunelli

et al. 2022

ACM Trans. Embed. Comput. Syst.

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“…VIII. RELATED WORK Works related to passive event detection are summarized in [4], [5], [8] and include door-opening [5], airflow [9], [10], water usage [6], [11], occupancy [5], power usage [7], humanwalking [12] detection. As mentioned in [8], all these works have a common drawback that the event itself must be able to captured by the harvester to produce a trigger signal.…”

Section: Limitation and Future Workmentioning

confidence: 99%

“…Existing works [4]- [12] that deal with event detection on energy harvesting systems are generally passive event detectors. For example, [5] proposes a batteryless system that detects door-opening events, where the action of opening the door itself vibrates a piezoelectric sensor to generate the trigger signal that wakes the MCU up.…”

Section: Introductionmentioning

confidence: 99%

SmartON: Just-in-Time Active Event Detection on Energy Harvesting Systems

Luo

Nirjon

2021

2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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We propose SmartON, a batteryless system that learns to wake up proactively at the right moment in order to detect events of interest. It does so by adapting the duty cycle to match the distribution of event arrival times under the constraints of harvested energy. While existing energy harvesting systems either wake up periodically at a fixed rate to sense and process the data, or wake up only in accordance with the availability of the energy source, SmartON employs a threephase learning framework to learn the energy harvesting pattern as well as the pattern of events at run-time, and uses that knowledge to wake itself up when events are most likely to occur. The three-phase learning framework enables rapid adaptation to environmental changes in both short and long terms. Being able to remain asleep more often than a CTID (charging-thenimmediate-discharging) wake-up system and adapt to the event pattern, SmartON is able to reduce energy waste, increase energy efficiency, and capture more events. To realize Smar-tON we have developed a dedicated hardware platform whose power management module activates capacitors on-the-fly to dynamically increase its storage capacitance. We conduct both simulation-driven and real-system experiments to demonstrate that SmartON captures 1X-7X more events and is 8X-17X more energy-efficient than a CTID system.

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“…To meet the lightweight requirement, MicroGen [9] has introduced a semiconductorbased MEMS (Micro-Electro-Mechanical Systems energy harvesting chip with a form-factor of 1.0cm 2 which is compatible with today's wearable and mobile devices. More broadly, the developments in kinetic-powered devices are fostering new pervasive computing research that consider kinetic energy harvester not only as a source of power, but also as a motion sensor to detect a wide range of contexts [10], [11], [12], [13]. This new sensing architecture utilizes the voltage output of energy harvester as an alternative to accelerometer, thereby saving energy that would have been consumed by the accelerometer in long-term monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, different from accelerometers that provide signal of three axis, energy harvesters have only one-axis of output. In this regard, KEH based sensing usually exhibits a performance deficiency compared to accelerometerbased system, and thus, they are mainly used for coarsegrained classification [11], [13], [14] at the moment.…”

Section: Introductionmentioning

confidence: 99%

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

Lan

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 hours of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

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Perpetual Sensing for the Built Environment

Cited by 11 publications

References 16 publications

Camaroptera: A Long-range Image Sensor with Local Inference for Remote Sensing Applications

Camaroptera: A Long-range Image Sensor with Local Inference for Remote Sensing Applications

SmartON: Just-in-Time Active Event Detection on Energy Harvesting Systems

EnTrans: Leveraging Kinetic Energy Harvesting Signal for Transportation Mode Detection

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