MPTCP-meLearning: A Multi-Expert Learning-Based MPTCP Extension to Enhance Multipathing Robustness against Network Attacks
With multiple network interfaces are being widely equipped in modern mobile devices, the
Multipath TCP (MPTCP) is increasingly becoming the preferred transport technique since it …
Multipath TCP (MPTCP) is increasingly becoming the preferred transport technique since it …
Tinyfedtl: Federated transfer learning on ubiquitous tiny iot devices
TinyML refers to the intersection of machine learning (ML), mathematical optimization, and
tiny IoT embedded systems. In the current era of ubiquitous connectivity and pervasive data …
tiny IoT embedded systems. In the current era of ubiquitous connectivity and pervasive data …
Edge artificial intelligence for industrial internet of things applications: an industrial edge intelligence solution
F Foukalas, A Tziouvaras - IEEE Industrial Electronics …, 2021 - ieeexplore.ieee.org
In this article, we study edge artificial intelligence (AI) for industrial Internet of Things (IIoT)
applications. We discuss edge AI technology, which is considered the combination of AI with …
applications. We discuss edge AI technology, which is considered the combination of AI with …
A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection
H Kim, S Park, H Hong, J Park, S Kim - Future Internet, 2024 - mdpi.com
As the size of the IoT solutions and services market proliferates, industrial fields utilizing IoT
devices are also diversifying. However, the proliferation of IoT devices, often intertwined with …
devices are also diversifying. However, the proliferation of IoT devices, often intertwined with …
Applications of distributed machine learning for the Internet-of-Things: A comprehensive survey
The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
Reducing web latency through dynamically setting TCP initial window with reinforcement learning
Latency, which directly affects the user experience and revenue of web services, is far from
ideal in reality, due to the well-known TCP flow startup problem. Specifically, since TCP …
ideal in reality, due to the well-known TCP flow startup problem. Specifically, since TCP …
Deep reinforcement learning for load-balancing aware network control in IoT edge systems
Load balancing is directly associated with the overall performance of a parallel and
distributed computing system. Although the relevant problems in communication and …
distributed computing system. Although the relevant problems in communication and …
Wanna make your TCP scheme great for cellular networks? Let machines do it for you!
Can we instead of designing yet another new TCP algorithm, design a TCP plug-in that can
enable machines to automatically boost the performance of the existing/future TCP designs …
enable machines to automatically boost the performance of the existing/future TCP designs …
A survey of recent advances in edge-computing-powered artificial intelligence of things
Z Chang, S Liu, X Xiong, Z Cai… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …
multitude of wired and wireless sensors generating a variety of heterogeneous data over …
FedDdrl: federated double deep reinforcement learning for heterogeneous IoT with adaptive early client termination and local epoch adjustment
Federated learning (FL) is a technique that allows multiple clients to collaboratively train a
global model without sharing their sensitive and bandwidth-hungry data. This paper …
global model without sharing their sensitive and bandwidth-hungry data. This paper …