Self-renewal machine learning approach for fast wireless network optimization

OT Ajayi, X Cao, H Shan… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
The throughput maximization in multi-hop wireless networks is largely limited by interference
due to the reuse of the channel resources. Although machine learning (ML) can accelerate …

On the Efficiency and Robustness of Vibration-based Foundation Models for IoT Sensing: A Case Study

T Kimura, J Li, T Wang, D Kara, Y Chen, Y Hu… - arXiv preprint arXiv …, 2024 - arxiv.org
This paper demonstrates the potential of vibration-based Foundation Models (FMs), pre-
trained with unlabeled sensing data, to improve the robustness of run-time inference in (a …

VibroFM: Towards Micro Foundation Models for Robust Multimodal IoT Sensing

T Kimura, J Li, T Wang, Y Chen, R Wang… - 2024 IEEE 21st …, 2024 - ieeexplore.ieee.org
The paper argues for the feasibility and utility of micro foundation models (µFMs), a key
direction for future smart IoT/CPS systems that exploits advances in self-supervised …

Machine Learning Assisted Capacity Optimization for B5G/6G Integrated Access and Backhaul Networks

OT Ajayi, S Zhang, Y Cheng - IEEE INFOCOM 2023-IEEE …, 2023 - ieeexplore.ieee.org
The cross-layer design on the routing of traffic and scheduling of wireless backhaul links in
the beyond 5G (B5G)/6G integrated access and backhaul (IAB) networks has continued to …

[PDF][PDF] AutoWatch: Learning Driver Behavior with Graphs for Auto Theft Detection and Situational Awareness

P Agbaje, A Mookhoek, A Anjum, A Mitra, MD Pesé… - researchgate.net
Millions of lives are lost due to road accidents each year, emphasizing the importance of
improving driver safety measures. In addition, physical vehicle security is a persistent …