Deterministic 40 year battery lifetime through a hybrid perpetual sensing platform (HyPer)

Thangarajan, Ashok Samraj; Yang, Fan; Joosen, Wouter; Hughes, Danny

doi:10.1145/3410992.3411028

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Compared to traditional sensors powered from a single primary battery, introducing energy harvesting capabilities can significantly increase the lifetime of the sensor by many years [9], [10]. Although this is highly dependent on the chosen transducer and the primary energy provided by the environment, photovoltaics are generally regarded to have the highest power densities available [11].…”

Section: Exploring Discontinuous Sensor Operationmentioning

confidence: 99%

Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning

Gherman

Cheng

Gómez

et al. 2021

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Popular low-cost air quality sensors embedded into IoT and mobile devices are based on metal oxides (MOX) that change their electrical resistance in response to ambient pollutants emitted as gases. Operating MOX sensors continuously is expensive, since it requires to heat up and maintain a hotplate at several hundred degrees. To save energy, sensors are commonly duty cycled with short on-times and long off-times. However, doing so adversely affects the sensor's chemical reactions, which have slower transients as the off-time increases. As a result, sensor sensitivity to various gases deviates from a continuously powered sensor. In this paper, we show that it is possible to recover accurate continuous-sensor measurements from transient responses obtained from a duty cycled sensor and compensate for an altered multi-gas cross-sensitivity profile using machine learning methods. On a test set, we achieve a mean absolute error (MAE) of 24 ppb between continuous ground-truth measurements and obtained model predictions of tVOC. This results in estimating 86.6% of Indoor Air Quality (IAQ) levels correctly compared to 68.1% if no correction is used. Our models are invariant to minor baseline shifts and work for both tVOC and CO2-eq signals provided by the sensor. Thanks to our models, 98.5 % of the energy consumption can be reduced while maintaining high accuracy. This optimization enables energy-harvesting-based operation of IAQ sensors in indoor IoT scenarios.

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Section: Exploring Discontinuous Sensor Operationmentioning

confidence: 99%

Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning

Gherman

Cheng

Gómez

et al. 2021

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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“…IoT end nodes have a limited energy budget that is constrained by their battery size or energy harvesting capability [24], [26]. This limits the computational and memory resources of the devices and forms a barrier to the use of the complex algorithms that are required for vibration analysis.…”

Section: B Processing Requirementsmentioning

confidence: 99%

“…the data resolution that they obtain does not support classic fault identification algorithms [41], [42], [50], (ii.) the solution is tailored for a specific application and is not possible to re-task the node to perform other applications over their long lifetime [26], [51], (iii.) large-scale deployments require a history of the machine health, which is not facilitated by pure on-device fault identification.…”

Section: Related Workmentioning

confidence: 99%

ReFrAEN: a Reconfigurable Vibration Analysis Framework for Constrained Sensor Nodes

Thangarajan

Yang

Joosen

et al. 2021

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

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“…In order to maximize deployment flexibility and minimize installation costs, the end-nodes which form the data collection fabric of the IoT are most often battery powered. Decades of Wireless Sensor Network (WSN) and Internet of Things (IoT) research has resulted in battery lifetimes of several years within reasonable form factors as reported by Yang et al (2015), Kim et al (2018), Jackson et al (2019), and Thangarajan et al (2020). However, while battery lifetimes have improved, the maintenance effort required to change them still cannot be scaled to support the coming wave of billions of new devices.…”

Section: Introductionmentioning

confidence: 99%

“…By harvesting energy from the environment and storing it in capacitors, battery lifetime limits may be eliminated as shown by Hester and Sorber (2017), Colin et al (2018), and Yang et al (2019). Alternatively, as was presented by Thangarajan et al (2020), harvested energy may be used to expand the energy envelope of an end-node which also uses a long-life battery for reliable operation in industrial deployments.…”

Section: Introductionmentioning

confidence: 99%

Static: Low Frequency Energy Harvesting and Power Transfer for the Internet of Things

Thangarajan

Nguyen

Liu

et al. 2022

Front. Signal Process.

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The Internet of Things (IoT) is composed of wireless embedded devices which sense, analyze and communicate the state of the physical world. To achieve truly wireless operation, today’s IoT devices largely depend on batteries for power. However, this leads to high maintenance costs due to battery replacement, or the environmentally damaging concept of disposable devices. Energy harvesting has emerged as a promising approach to delivering long-life, environmentally friendly IoT device operation. However, with the exception of solar harvesting, it remains difficult to ensure sustainable system operation using environmental power alone. This paper tackles this problem by contributing Static, a Radio Frequency (RF) energy harvesting and wireless power transfer platform. Our approach comprises autonomous energy management techniques, adaptive power transfer algorithms and an open-source hardware reference platform to enable further research. We evaluate Static in laboratory conditions and show that 1) ambient RF energy harvesting can deliver sustainable operation using common industrial sources, while 2) wireless power transfer provides a simple means to power motes at a range of up to 3 m through a variety of media.

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Deterministic 40 year battery lifetime through a hybrid perpetual sensing platform (HyPer)

Cited by 6 publications

References 12 publications

Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning

Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning

ReFrAEN: a Reconfigurable Vibration Analysis Framework for Constrained Sensor Nodes

Static: Low Frequency Energy Harvesting and Power Transfer for the Internet of Things

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