Internet of Thing
The Internet of Things (IoT) involves connecting billions of devices to collect and analyze data, aiming to improve efficiency and decision-making across various sectors. Current research heavily focuses on enhancing IoT security through machine learning (ML) models like deep neural networks (CNNs, LSTMs, Transformers), federated learning, and the integration of large language models (LLMs) for improved anomaly detection and attack prediction. These advancements are crucial for addressing the growing concerns of data privacy, security vulnerabilities, and resource constraints within increasingly complex IoT networks, impacting fields from smart cities to healthcare.
Papers
SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach
Tianshi Wang, Jinyang Li, Ruijie Wang, Denizhan Kara, Shengzhong Liu, Davis Wertheimer, Antoni Viros-i-Martin, Raghu Ganti, Mudhakar Srivatsa, Tarek Abdelzaher
A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission
Wenjun Huang, Arghavan Rezvani, Hanning Chen, Yang Ni, Sanggeon Yun, Sungheon Jeong, Mohsen Imani
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
Mohammed El Hanjri, Hamza Reguieg, Adil Attiaoui, Amine Abouaomar, Abdellatif Kobbane, Mohamed El Kamili
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey
Jiayuan Chen, You Shi, Changyan Yi, Hongyang Du, Jiawen Kang, Dusit Niyato
IoT in the Era of Generative AI: Vision and Challenges
Xin Wang, Zhongwei Wan, Arvin Hekmati, Mingyu Zong, Samiul Alam, Mi Zhang, Bhaskar Krishnamachari
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
Ihsane Gryech, Chaimae Assad, Mounir Ghogho, Abdellatif Kobbane