[PDF][PDF] Energy-efficient IoT by Continual Learning for Data Reduction in Wireless Sensor Networks
A Falzberger - 2023 - netidee.at
Abstract The Internet of Things (IoT) is becoming increasingly prevalent in our daily lives,
with sensor devices providing the ability to sense and act on the environment. However …
with sensor devices providing the ability to sense and act on the environment. However …
Leveraging live machine learning and deep sleep to support a self-adaptive efficient configuration of battery powered sensors
Sensor networks empower Internet of Things (IoT) applications by connecting them to
physical world measurements. However, the necessary use of limited bandwidth networks …
physical world measurements. However, the necessary use of limited bandwidth networks …
Quality-based and energy-efficient data communication for the internet of things networks
Y Fathy, P Barnaghi - IEEE Internet of Things Journal, 2019 - ieeexplore.ieee.org
Large volumes of real-world observation and measurement data are collected from sensory
devices in the Internet of Things (IoT) networks. IoT data is often generated in highly …
devices in the Internet of Things (IoT) networks. IoT data is often generated in highly …
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 …
towards developing learning-based sensing applications on embedded edge devices …
SenDaL: An Effective and Efficient Calibration Framework of Low-cost Sensors for Daily Life
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 …
controlled environments. However, the data collected from various sensors in daily life often …
Hierarchical and distributed machine learning inference beyond the edge
Networked applications with heterogeneous sensors are a growing source of data. Such
applications use machine learning (ML) to make real-time predictions. Currently, features …
applications use machine learning (ML) to make real-time predictions. Currently, features …
Maximizing training efficiency through intelligent data exposure
AC Depoian II, CP Bailey… - Big Data VI: Learning …, 2024 - spiedigitallibrary.org
Edge computing in remote sensing often necessitates on-device learning due to bandwidth
and latency constraints. However, limited memory and computational power on edge …
and latency constraints. However, limited memory and computational power on edge …
Replay-driven continual learning for the industrial internet of things
The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and
computing devices to monitor and control large and complex industrial processes. Machine …
computing devices to monitor and control large and complex industrial processes. Machine …
Active learning for IoT data prioritization in edge nodes over wireless networks
CK Tham, R Rajagopalan - … The 46th Annual Conference of the …, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) has emerged as a key networking infrastructure that connects a
large number of sensors, thereby allowing the collection and processing of large amounts of …
large number of sensors, thereby allowing the collection and processing of large amounts of …
REPTILE: a Tool for Replay-driven Continual Learning in IIoT
We present an automated Machine Learning (ML) tool designed as a continual learning
pipeline to adapt to evolving data streams in the Industrial Internet of Things (IIoT). This tool …
pipeline to adapt to evolving data streams in the Industrial Internet of Things (IIoT). This tool …