Joint device selection and bandwidth allocation for cost-efficient federated learning in industrial internet of things
Along with the deployment of Industrial Internet of Things (IIoT), massive amounts of
industrial data have been generated at the network edge, driving the evolution of edge …
industrial data have been generated at the network edge, driving the evolution of edge …
Energy-efficient federated learning framework for digital twin-enabled industrial internet of things
The digital twin (DT) bridges the physical world with the digital world in real-time for the
Industrial Internet of Things (IIoT) and federated learning (FL) enables edge intelligence …
Industrial Internet of Things (IIoT) and federated learning (FL) enables edge intelligence …
Seamless transition from machine learning on the cloud to industrial edge devices with thinger. io
AL Bustamante, MA Patricio… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Due to Industry 4.0, machines can be connected to their manufacturing processes with the
ability to react faster and smarter to changing conditions in a factory. Previously, Internet of …
ability to react faster and smarter to changing conditions in a factory. Previously, Internet of …
Deep reinforcement learning for scheduling in an edge computing‐based industrial internet of things
J Wu, G Zhang, J Nie, Y Peng… - … and Mobile Computing, 2021 - Wiley Online Library
The demand for improving productivity in manufacturing systems makes the industrial
Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In …
Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In …
Learning-based and data-driven tcp design for memory-constrained iot
Advances in wireless technology have resulted in pervasive deployment of devices of a high
variability in form factors, memory and computational ability. The need for maintaining …
variability in form factors, memory and computational ability. The need for maintaining …
A novel three-layer IoT architecture for shared, private, scalable, and real-time machine learning from ubiquitous cyber-physical systems
Due to the recent movements in Industry 4.0 and Internet of Things (IoT), accessing or
generating data in the Smart Manufacturing (SM) domain has become more attainable; …
generating data in the Smart Manufacturing (SM) domain has become more attainable; …
Machine Learning Algorithms for 5G and Internet-of-Thing (IoT) Networks
With the advent of 5G wireless networking, the Internet of Things (IoT) will be a network of
sensors, actuators, and processing units for sensing, data sharing, and control. These are …
sensors, actuators, and processing units for sensing, data sharing, and control. These are …
[HTML][HTML] Ares: Adaptive resource-aware split learning for internet of things
Abstract Distributed training of Machine Learning models in edge Internet of Things (IoT)
environments is challenging because of three main points. First, resource-constrained …
environments is challenging because of three main points. First, resource-constrained …
SmartCC: A reinforcement learning approach for multipath TCP congestion control in heterogeneous networks
W Li, H Zhang, S Gao, C Xue… - IEEE Journal on …, 2019 - ieeexplore.ieee.org
The Multipath TCP (MPTCP) protocol has been standardized by the IETF as an extension of
conventional TCP, which enables multi-homed devices to establish multiple paths for …
conventional TCP, which enables multi-homed devices to establish multiple paths for …
Enabling cooperative relay selection by transfer learning for the industrial internet of things
Large manufacturing sites with movable obstacles and dynamic network topology call for
reliable and efficient strategies to transmit data through the industrial Internet of Things (IoT) …
reliable and efficient strategies to transmit data through the industrial Internet of Things (IoT) …