REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices

@article{Bhat2019REAPRE,
  title={REAP: Runtime Energy-Accuracy Optimization for Energy Harvesting IoT Devices},
  author={Ganapati Bhat and Kunal Bagewadi and Hyung Gyu Lee and {\"U}mit Y. Ogras},
  journal={2019 56th ACM/IEEE Design Automation Conference (DAC)},
  year={2019},
  pages={1-6}
}
The use of wearable and mobile devices for health and activity monitoring is growing rapidly. These devices need to maximize their accuracy and active time under a tight energy budget imposed by battery and form-factor constraints. This paper considers energy harvesting devices that run on a limited energy budget to recognize user activities over a given period. We propose a technique to co-optimize the accuracy and active time by utilizing multiple design points with different energy-accuracy… 

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References

SHOWING 1-10 OF 20 REFERENCES

Near-optimal energy allocation for self-powered wearable systems

This paper presents a near-optimal runtime energy management technique by considering the harvested energy and shows that the results of the proposed algorithm are, on average, within 3% of the optimal solution computed offline.

Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks

A new technique for solving the problem of adaptive duty-cycling based on results from adaptive control theory is introduced and it is shown that it achieves better performance than previous approaches on a broader class of energy source data sets.

Energy harvesting in wireless sensor networks: A comprehensive review

Power management in energy harvesting sensor networks

Power management is an important concern in sensor networks, because a tethered energy infrastructure is usually not available and an obvious concern is to use the available battery energy

Powering the Internet of Things

The design of ultra-low power hardware platforms that integrate computing, sensing, storage, and wireless connectivity in a tiny form factor, the development of intelligent system-level power management techniques, and the use of environmental energy harvesting to make IoT devices self-powered, thus decreasing - in some cases, even eliminating - their dependence on batteries are highlighted.

Dynamic power management for long-term energy neutral operation of solar energy harvesting systems

This work proposes a novel approach to dynamically adjust the system's performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved, and achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.

Online Human Activity Recognition using Low-Power Wearable Devices

This paper presents the first HAR framework that can perform both online training and inference, and starts with a novel technique that generates features using the fast Fourier and discrete wavelet transforms of a textile-based stretch sensor and accelerometer data.

A Survey on Human Activity Recognition using Wearable Sensors

The state of the art in HAR based on wearable sensors is surveyed and a two-level taxonomy in accordance to the learning approach and the response time is proposed.

Pack & Cap: Adaptive DVFS and thread packing under power caps

Pack & Cap is proposed, a control technique designed to make optimal DVFS and thread packing control decisions in order to maximize performance within a power budget and is implemented and validated on a real quad-core system running the PARSEC parallel benchmark suite.

Physical Activity Monitoring for Assisted Living at Home

This work proposes a methodology to determine the occurrence of falls from among other common human movements by employing classifiers based on neural networks and k-nearest neighbors and explores several effective key features that can be used for classification of physical movements.