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
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning
Ao Liu, Jing Chen, Ruiying Du, Cong Wu, Yebo Feng, Teng Li, Jianfeng Ma
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
Xiaowei Tang, Bin Long, Li Zhou
Artificial Intelligence of Things: A Survey
Shakhrul Iman Siam, Hyunho Ahn, Li Liu, Samiul Alam, Hui Shen, Zhichao Cao, Ness Shroff, Bhaskar Krishnamachari, Mani Srivastava, Mi Zhang
Integrating Large Language Models with Internet of Things Applications
Mingyu Zong, Arvin Hekmati, Michael Guastalla, Yiyi Li, Bhaskar Krishnamachari