Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors

C Cecchinel, F Fouquet, S Mosser, P Collet - Future Generation Computer …, 2019 - Elsevier
Sensor networks empower Internet of Things (IoT) applications by connecting them to
physical world measurements. However, the necessary use of limited bandwidth networks …

Marble: Collaborative scheduling of batteryless sensors with meta reinforcement learning

F Fraternali, B Balaji, D Hong, Y Agarwal… - Proceedings of the 8th …, 2021 - dl.acm.org
Batteryless energy-harvesting sensing systems are attractive for low maintenance but face
challenges in real-world applications due to low quality of service from sporadic and …

SenDaL: An Effective and Efficient Calibration Framework of Low-cost Sensors for Daily Life

S Ahn, H Kim, E Lee, YD Seo - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
The collection of accurate and noise-free data is a crucial part of Internet of Things (IoT)-
controlled environments. However, the data collected from various sensors in daily life often …

Rest: Robust and efficient neural networks for sleep monitoring in the wild

R Duggal, S Freitas, C Xiao, DH Chau… - Proceedings of The Web …, 2020 - dl.acm.org
In recent years, significant attention has been devoted towards integrating deep learning
technologies in the healthcare domain. However, to safely and practically deploy deep …

Low-cost adaptive monitoring techniques for the internet of things

D Trihinas, G Pallis… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Internet-enabled physical devices with “smart” processing capabilities are becoming the
tools for understanding the complexity of the global inter-connected world we inhabit. The …

Extending the battery lifetime of wearable sensors with embedded machine learning

X Fafoutis, L Marchegiani, A Elsts… - 2018 IEEE 4th World …, 2018 - ieeexplore.ieee.org
Smart health home systems and assisted living architectures rely on severely energy-
constrained sensing devices, such as wearable sensors, for the generation of data and their …

Ember: energy management of batteryless event detection sensors with deep reinforcement learning

F Fraternali, B Balaji, D Sengupta, D Hong… - Proceedings of the 18th …, 2020 - dl.acm.org
Energy management can extend the lifetime of batteryless, energy-harvesting systems by
judiciously utilizing the energy available. Duty cycling of such systems is especially …

OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices

A Bukhari, S Hosseinimotlagh… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Recent advances in Internet-of-Things (IoT) technologies have sparked significant interest
towards developing learning-based sensing applications on embedded edge devices …

Dataflow Driven Partitioning of Machine Learning Applications for Optimal Energy Use in Batteryless Systems

A Gomez, A Tretter, PA Hager… - ACM Transactions on …, 2022 - dl.acm.org
Sensing systems powered by energy harvesting have traditionally been designed to tolerate
long periods without energy. As the Internet of Things (IoT) evolves toward a more transient …

Energy-Aware Adaptive Sampling for Self-Sustainability in Resource-Constrained IoT Devices

M Giordano, S Cortesi, PV Mekikis, M Crabolu… - Proceedings of the 11th …, 2023 - dl.acm.org
In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms
combined with energy harvesting solutions are crucial to obtain self-sustainability. This …