Systems that Sustain Themselves: Energy Harvesting Sensor Nodes for Monitoring the Environment

Singh, Kaumudi; K, Nithesh Nayak; Kedilaya, Anup A.; Prabhakar, T. V.; Kuri, Joy

doi:10.1109/ficloud.2019.00045

Cited by 1 publication

(5 citation statements)

References 11 publications

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“…While sampling with different resolutions, we encounter an average error of 5.2%, 7.04%, 12.3%, and 18.18% for 12‐bit, 10‐bit, 9‐bit, and 7‐bit resolutions, respectively. We observed an energy saving of 62.12% and a 53.33% reduction in the amount of data bytes that are sampled 9 …”

Section: Discussionmentioning

confidence: 86%

“…Nodes can also be powered by simultaneously harvesting from multiple energy sources 6 . Since multiharvester systems are generally immune to fluctuations in energy sources, our implemented system harvests solar and vibrational energy 9 . EHS nodes utilize storage buffers such as supercapacitors to operate when the harvesting source(s) are unavailable.…”

Section: Related Workmentioning

confidence: 99%

“…AR based adaptive sampling reduces the amount of sampled data and has a simpler implementation as compared to compression algorithms. Additionally, the power required for sampling is reduced 9 . Extending this, we implement a regulating function based transmission scheme where we refrain from transmitting every single sampled value and the amount of transmission is not only governed by the energy available at the node but also the characteristics of data being sampled, viz., rate of change of data and data priority.…”

Section: Related Workmentioning

confidence: 99%

“…In Adaptive zone, the sampling resolution and rate are set adaptively. The sampling rate (

R_{AD C_{s}}

) depends on the slope and standard deviation of the temperature variations over time 9 . The sampling resolution depends on the subrange of temperature being monitored.…”

Section: Adaptive Samplingmentioning

confidence: 99%

“…This demonstrates that our AR model can predict the changes in the temperature with acceptable accuracy. Employing adaptive sampling also leads to savings in energy required for sampling and amount of data sampled 9 …”

Section: Insights From Evaluationsmentioning

confidence: 99%

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Judicious data management for sustaining an energy harvesting sensor node

Singh

Shukla

Sachin

et al. 2020

Concurrency and Computation

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Summary Perpetual lifetimes and low maintenance are few of the attractive aspects of sensor nodes that harvest ambient energy. However, their operation depends heavily on the energy profiles of their harvesting source(s). In this work, we study the suitability of energy harvesting sensor (EHS) Nodes, powered using indoor lighting and vibrations, for a simple temperature monitoring application. To help these nodes sustain, we have proposed schemes that allow the nodes to sample and transmit data judiciously. Along with an adaptive sampling based on autoregressive (AR) model, we have proposed a regulating function based transmission scheme that regulates the amount of transmitted data based on the energy available at the node and the characteristics of the data so that even with limited transmissions the fidelity of the data is not lost. With the help of thorough evaluations, we can conclude that an EHS node fares quite well. Results show that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but they also reduce the amount of data generated and accumulated in a network.

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Section: Discussionmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In Adaptive zone, the sampling resolution and rate are set adaptively. The sampling rate (

R_{AD C_{s}}

) depends on the slope and standard deviation of the temperature variations over time 9 . The sampling resolution depends on the subrange of temperature being monitored.…”

Section: Adaptive Samplingmentioning

confidence: 99%

Section: Insights From Evaluationsmentioning

confidence: 99%

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