Energy efficient IO stack design for wearable device

Kim, Jung-Hoon; Kim, Sundoo; Yun, Juseong; Won, Youjip

doi:10.1145/3297280.3297491

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…Conversely, J. Kim et al conducted a study that reveals the significant contribution of storage IO activities to the power consumption of wearable devices. Recognizing this, they developed a set of techniques, including Metadata Embedding, Selective Directory Sync, and Flushless Durability Guarantee that aim to enhance battery life by 3% [20]. Moreover, H. Wu et al have created a tool called SENTINEL capable of generating automatic tests detecting sensor leaks in Android and Android Wear [40]; while R. Lutze et al present an innovative approach to the creation of apps for smartwatches based on the need for dialogue management, a distributed and layered architecture and keeping maintenance costs to a minimum [25].…”

Section: Related Workmentioning

confidence: 99%

Real-time Energy-efficient Sensor Libraries for Wearable Devices

Calisti,

Lattanzi

2023

Preprint

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The growing popularity of wearable technology has led to a surge in smartwatch usage among the general public. These devices offer a range of features, including internet connectivity, fitness tracking, and real-time notifications, making them valuable tools for staying connected to the online world while remaining engaged in real-world activities. Smartwatches have become powerful platforms for Human Activity Recognition (HAR) applications thanks to the increasing computational power and the presence of a wide array of sensors, such as accelerometers, gyroscopes, heart rate, and step counters. Efficient real-time data collection from internal sensors is a crucial requirement for HAR applications on wearable devices due to their constraints in battery size and duration. In this paper, we introduce the implementation of three energy-efficient user-level libraries developed for real-time data collection from inertial sensors using native Wear OS APIs and different techniques: Thread, Flow, and Channel. Experiments were conducted on a commercially available Oppo smartwatch comparing them in terms of code size, memory utilization, and energy consumption. The characterization results demonstrate empirically that the solution is lightweight and adaptable, making it suitable for development on wearable devices without significant impact on battery life and system performance.

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Section: Related Workmentioning

confidence: 99%